CN109313909A - Method, Apparatus, Apparatus and System for Evaluating Microphone Array Consistency - Google Patents

Abstract

The embodiment of the application provides a method, equipment, a device and a system for evaluating the consistency of a microphone array, which can evaluate the consistency among different microphones in the microphone array, so that the calibration of the microphone array is guided and the robustness of a multi-channel enhancement algorithm is evaluated according to a consistency evaluation result, and the user experience is improved. The method comprises the following steps: acquiring N audio signals respectively acquired by N microphones, wherein the N microphones form a microphone array, and N is more than or equal to 2; determining a phase spectrum difference value and/or a power spectrum difference value between each of the N microphones except a reference microphone and the reference microphone according to the N audio signals, wherein the reference microphone is any one of the N microphones; and performing consistency evaluation on the N microphones according to the phase spectrum difference value and/or the power spectrum difference value between each microphone except the reference microphone and the reference microphone.

Description

Method, Apparatus, Apparatus and System for Evaluating Microphone Array Consistency

technical field

The present application relates to the fields of voice communication and voice intelligent interaction, and more particularly, to methods, apparatuses, apparatuses and systems for evaluating the consistency of microphone arrays.

Background technique

In voice communication applications, voice enhancement technology can improve human hearing experience and improve the intelligibility of voice communication. In voice intelligent interactive applications, voice enhancement technology can improve the accuracy of voice recognition and improve user experience. Therefore, voice enhancement technology Both in traditional voice communication and voice interaction are crucial. Speech enhancement technology is divided into single-channel speech enhancement technology and multi-channel speech enhancement technology. Among them, single-channel speech enhancement technology can eliminate steady-state noise, but cannot eliminate non-stationary noise, and the improvement of signal ratio is at the expense of speech impairment, and signal-to-noise The more the ratio is improved, the greater the speech damage; the multi-channel speech enhancement technology uses the microphone array to collect multi-channel signals, and uses the phase information and coherent information between the multi-microphone signals to eliminate noise, which can eliminate non-steady-state noise, and has less effect on speech damage. Small.

In the multi-channel speech enhancement technology, the consistency between different microphones in the microphone array directly affects the performance of the algorithm. The existing scheme proposes an improved algorithm of the multi-channel enhancement technology to increase the robustness of the algorithm, and at the same time, the consistency between the microphones is improved. The performance requirement is reduced, however, low consistency between microphones still affects algorithm performance and thus user experience.

SUMMARY OF THE INVENTION

The present application provides a method, device, device and system for evaluating the consistency of a microphone array, which can evaluate the consistency between different microphones in the microphone array, so as to guide the calibration of the microphone array and evaluate the performance of the multi-channel enhancement algorithm according to the consistency evaluation result. Robustness, improve user experience.

In a first aspect, a method for evaluating the consistency of a microphone array is provided, including:

Obtain N audio signals collected by N microphones respectively, the N microphones form a microphone array, N≥2;

Determine, according to the N audio signals, a phase spectrum difference value and/or a power spectrum difference value between each of the N microphones except the reference microphone and the reference microphone, where the reference microphone is one of the N microphones any one of the microphones;

According to the phase spectrum difference and/or the power spectrum difference between each of the N microphones except the reference microphone and the reference microphone, the N microphones are evaluated for consistency.

It should be noted that the consistency evaluation of the N microphones can be used to guide the microphone distribution in the microphone array, or to guide the redesign of the microphone distribution in the microphone array, or to guide the redesign of the microphone array, or to evaluate the multi-channel Enhance the robustness of the algorithm.

For example, when the evaluation result shows that the consistency between microphone 1 and microphone 2 is poor, it may guide to adjust the distribution of microphone 1 or microphone 2 in the microphone array, or guide to redesign microphone 1 or microphone 2.

For another example, when the evaluation result shows that the consistency of microphone 1 with multiple microphones is poor, it can guide to adjust the distribution of microphone 1 in the microphone array, or guide to redesign microphone 1, or guide to redesign the microphone array.

In the embodiment of the present application, the phase spectrum difference and/or the power spectrum difference between each microphone and the reference microphone are determined according to the N audio signals collected by the N microphones, so that the consistency evaluation of the N microphones is performed, Eliminate the impact of consistency between microphones on multi-channel speech enhancement algorithms and improve user experience.

In some possible implementations, according to the phase spectrum difference between each of the N microphones except the reference microphone and the reference microphone, the N microphones are evaluated for consistency, including:

According to the phase spectrum difference value between each of the N microphones except the reference microphone and the reference microphone, the phase consistency between the corresponding microphone and the reference microphone is evaluated.

It should be noted that the smaller the phase spectrum difference between the two microphones, the better the phase consistency between the two microphones.

For example, the phase spectrum difference value between microphone 1 and the reference microphone is A, and the smaller A is, the better the phase consistency between microphone 1 and the reference microphone is.

Optionally, a threshold can be set. If the phase spectrum difference between the two microphones is less than this threshold, it means that the phase consistency between the two microphones meets the design requirements, and the consistency between the two microphones The effect on the multi-channel speech enhancement algorithm is negligible, or the consistency between the two microphones has no effect on the multi-channel speech enhancement algorithm.

It should be noted that the above threshold can be flexibly configured according to different multi-channel speech enhancement algorithms.

In some possible implementations, the method further includes:

Measure the distance difference between each of the N microphones except the reference microphone and the reference microphone to the sound source;

Calculate the fixed phase difference between each of the N microphones except the reference microphone and the reference microphone according to the measured distance difference;

According to the fixed phase difference between each of the N microphones except the reference microphone and the reference microphone, the corresponding phase spectrum difference values are respectively calibrated.

For example, the fixed phase difference between microphone 1 and the reference microphone is A, the phase spectrum difference between microphone 1 and the reference microphone is B, and after calibration, the phase spectrum difference between microphone 1 and the reference microphone is C, this , C=B-A.

In some possible implementations, according to the measured distance, calculating the fixed phase difference between each of the N microphones except the reference microphone and the reference microphone, respectively, includes:

According to the formula Calculate the fixed phase difference between each of the N microphones except the reference microphone and the reference microphone, respectively,

Among them, Y _i (ω) represents the spectrum of the ith microphone, Y ₁ (ω) represents the spectrum of the reference microphone, ω represents the frequency, d _i represents the distance difference between the ith microphone and the reference microphone to the sound source, and c represents the speed of sound , 2πωd _i /c represents the fixed phase difference between the ith microphone and the reference microphone.

In some possible implementations, according to the phase spectrum difference between each of the N microphones except the reference microphone and the reference microphone, the N microphones are evaluated for consistency, including:

According to the power spectrum difference between each of the N microphones except the reference microphone and the reference microphone, the amplitude consistency between the corresponding microphone and the reference microphone is evaluated.

It should be noted that the smaller the power spectrum difference between the two microphones, the better the amplitude consistency between the two microphones.

For example, the power spectrum difference between the microphone 1 and the reference microphone is A, and the smaller the A, the better the amplitude consistency between the microphone 1 and the reference microphone.

Optionally, a threshold can be set. If the power spectrum difference between the two microphones is less than this threshold, it means that the amplitude consistency between the two microphones meets the design requirements, and the consistency between the two microphones The effect on the multi-channel speech enhancement algorithm is negligible, or the consistency between the two microphones has no effect on the multi-channel speech enhancement algorithm.

It should be noted that the above threshold can be flexibly configured according to different multi-channel speech enhancement algorithms.

In some possible implementations, when the phase consistency evaluation is performed, the N audio signals are signals collected in an environment of playing frequency sweep signal data.

In some possible implementations, when the amplitude consistency evaluation is performed, the N audio signals are signals collected under the environment of playing Gaussian white noise data or frequency sweep signal data.

In some possible implementations, the frequency sweep signal is any one of a linear frequency sweep signal, a logarithmic frequency sweep signal, a linear step frequency sweep signal, and a logarithmic step frequency sweep signal.

In some possible implementations, the phase spectrum difference value and/or the power spectrum difference value between each of the N microphones except the reference microphone and the reference microphone is determined according to the N audio signals ,include:

Framing each of the N audio signals to obtain K signal frames of equal length, K≥2;

Perform windowing processing on each of the K signal frames to obtain K windowed signal frames;

Perform Fast Fourier Transform (FFT) transformation on each of the K windowed signal frames to obtain K target signal frames;

According to the K target signal frames corresponding to each audio signal, a phase spectrum difference value and/or a power spectrum difference value between each of the N microphones except the reference microphone and the reference microphone is determined.

Optionally, K represents the total number of frames of signals collected by each microphone.

It should be noted that the windowing process is used to eliminate the truncation effect caused by frame division. Optionally, each of the K signal frames may be processed by adding a Hamming window.

In some possible implementations, any two adjacent signal frames in the K signal frames overlap by R%, and R>0. For example, the R is 25 or 50.

Optionally, the signal amplitude remains unchanged after overlapping windowing.

It should be understood that each frame signal after the overlap has the components of the previous frame to prevent discontinuity between the two frames.

In some possible implementations, the ith audio signal is divided into frames to obtain K signal frames of equal length, which are written in the following vector form:

x _i (t)=[x _i,1 (t), _xi,2 (t),…, _xi,K (t)] ^T

Among them, x _i (t) represents the ith audio signal, K represents the total number of frames of signals collected by each microphone, and [ ] ^T represents the transpose of the vector or matrix.

In some possible implementations, the phase spectrum between each of the N microphones except the reference microphone and the reference microphone is determined according to the K target signal frames corresponding to each audio signal difference, including:

According to the formula determining the phase spectrum difference between each of the N microphones except the reference microphone and the reference microphone,

Among them, imag() means taking the imaginary part, ln() means taking the natural logarithm, represents the phase spectrum difference between the ith microphone and the reference microphone, represents the jth target signal frame of the reference microphone, represents the jth target signal frame of the ith microphone, Indicates the dominant frequency.

In some possible implementations, the power spectrum between each of the N microphones except the reference microphone and the reference microphone is determined according to the K target signal frames corresponding to each audio signal difference, including:

Determine the power spectrum of each audio signal according to the K target signal frames corresponding to each audio signal;

According to the power spectrum of each audio signal, a power spectrum difference value between each of the N microphones except the reference microphone and the reference microphone is determined.

In some possible implementations, determining the power spectrum of each audio signal according to the K target signal frames corresponding to each audio signal includes:

According to the formula Calculate the power spectrum of each audio signal,

Among them, P _i (ω) represents the power spectrum of the ith audio signal, Y _i,j (ω) represents the jth target signal frame in the ith audio signal, and K represents the total frame of the signal received by each microphone number, ω represents the frequency.

In some possible implementations, the determining, according to the power spectrum of each audio signal, a power spectrum difference value between each of the N microphones except the reference microphone and the reference microphone includes:

Calculate the power spectrum difference between each of the N microphones except the reference microphone and the reference microphone according to the formula PD _i (ω)=P ₁ (ω)-P _i (ω),

Among them, PD _i (ω) represents the power spectrum difference between the ith microphone and the reference microphone, P ₁ (ω) represents the power spectrum of the reference microphone, and P _i (ω) represents the power spectrum of the ith microphone.

In some possible implementations, the acquiring N audio signals respectively collected by the N microphones includes:

Determine the sampling frequency F _s and the number of FFT points N _fft of the N microphones when collecting the audio signals, use the speaker to play the Gaussian white noise data or the frequency sweep signal data, and the N microphones collect the N audio signals, where if the The data played by the speaker is the frequency sweep signal data, and the frequency sweep signal data is composed of M+1 segments of signals with equal lengths and unequal frequencies.

It should be noted that the number of FFT points N _fft is an even number, generally 32, 64, 128, .

In some possible implementations, according to the formula calculate the frequency of each segment of the M+1 segment signal, and

According to the formula S _i (t)=sin(2πf _i t), each segment of the M+1 segment signal is calculated,

Among them, f _i represents the frequency of the i-th segment signal, F _s represents the sampling frequency, N _fft represents the number of FFT points, S _i (t) represents the i-th segment signal, and the length of S ₁ (t) is an integer multiple of the period T, T=1/f ₁ .

In some possible implementations, the frequency sweep signal data played by the speaker can be written in the following vector form:

S(t)=[S ₀ (t),S ₁ (t),…,S _M (t)] ^T

Among them, S(t) represents the frequency sweep signal data played by the speaker, S _i (t) represents the i-th signal, [ ] ^T represents the transpose of a vector or matrix.

In some possible implementations, the N microphones collect N audio signals respectively, wherein the audio signal collected by the ith microphone is represented as _xi (t), and _xi (t) can be written in the following vector form:

x _i (t)=[x _i,1 (t), _xi,2 (t),…, _xi,K (t)] ^T

Among them, x _i (t) represents the audio signal collected by the ith microphone, K represents the total number of frames of the signal collected by each microphone, [ ] ^T represents the transpose of the vector or matrix.

In some possible implementations, the acquiring N audio signals respectively collected by the N microphones includes:

The N microphones are placed in a test room, a speaker is configured in the test room, and the N microphones are located directly in front of the speaker;

The speaker is controlled to play Gaussian white noise data or frequency sweep signal data, and the N microphones are controlled to collect the N audio signals respectively.

In some possible implementations, the test room has an anechoic room environment, the speaker is an artificial mouth dedicated to audio testing, and the artificial mouth is calibrated with a standard microphone before use.

In some possible implementations, before controlling the speaker to play the Gaussian white noise data or the frequency sweep signal data, the method further includes:

In a quiet environment, obtain the first audio data X ₁ (n) collected by the N microphones within the first duration T ₁ ;

Under the environment of playing Gaussian white noise data or frequency sweep signal data, obtain the second audio data X ₂ (n) collected by the N microphones in the second time period T2;

According to the formula Calculate the signal-to-noise ratio SNR and ensure that the SNR is greater than the first threshold.

In a second aspect, a device for evaluating the consistency of a microphone array is provided, including:

an acquisition unit, configured to acquire N audio signals collected by N microphones respectively, the N microphones form a microphone array, and N≥2;

A processing unit, configured to determine, according to the N audio signals, a phase spectrum difference value and/or a power spectrum difference value between each of the N microphones except the reference microphone and the reference microphone, where The reference microphone is any one of the N microphones;

The processing unit is further configured to, according to the phase spectrum difference value and/or the power spectrum difference value between each of the N microphones except the reference microphone and the reference microphone, determine the N value for the N microphones. microphones for conformance assessment.

In some possible implementations, the processing unit is specifically used for:

According to the phase spectrum difference value between each of the N microphones except the reference microphone and the reference microphone, the phase consistency between the corresponding microphone and the reference microphone is evaluated.

In some possible implementations, the processing unit is further configured to:

respectively measuring the distance difference between each of the N microphones except the reference microphone and the reference microphone to the sound source;

Calculate the fixed phase difference between each of the N microphones except the reference microphone and the reference microphone according to the measured distance difference;

According to the fixed phase difference between each of the N microphones except the reference microphone and the reference microphone, the corresponding phase spectrum difference values are respectively calibrated.

In some possible implementations, the processing unit is specifically used for:

According to the formula Calculate the fixed phase difference between each of the N microphones except the reference microphone and the reference microphone, respectively,

Among them, Y _i (ω) represents the spectrum of the ith microphone, Y ₁ (ω) represents the spectrum of the reference microphone, ω represents the frequency, d _i represents the distance difference between the ith microphone and the reference microphone to the sound source, and c represents the speed of sound , 2πωd _i /c represents the fixed phase difference between the ith microphone and the reference microphone.

In some possible implementations, the processing unit is specifically used for:

According to the power spectrum difference between each of the N microphones except the reference microphone and the reference microphone, the amplitude consistency between the corresponding microphone and the reference microphone is evaluated.

In some possible implementations, the N audio signals are signals collected in an environment of playing frequency sweep signal data.

In some possible implementations, the N audio signals are signals collected in an environment of playing Gaussian white noise data or frequency sweep signal data.

In some possible implementation manners, the frequency sweep signal is any one of a linear frequency sweep signal, a logarithmic frequency sweep signal, a linear step frequency sweep signal, and a logarithmic step frequency sweep signal.

In some possible implementations, the processing unit is specifically used for:

Framing each audio signal in the N audio signals to obtain K signal frames of equal length, K≥2;

Windowing is performed on each of the K signal frames to obtain K windowed signal frames;

Perform FFT transformation on each of the K windowed signal frames to obtain K target signal frames;

Determine, according to the K target signal frames corresponding to each audio signal, a phase spectrum difference value and/or a phase spectrum difference between each of the N microphones except the reference microphone and the reference microphone Power spectrum difference.

In some possible implementations, any two adjacent signal frames in the K signal frames overlap by R%, and R>0.

In some possible implementations, the R is 25 or 50.

In some possible implementations, the ith audio signal is divided into frames to obtain K signal frames of equal length, which are written in the following vector form:

x _i (t)=[x _i,1 (t), _xi,2 (t),…, _xi,K (t)] ^T

Among them, x _i (t) represents the ith audio signal, K represents the total number of frames of signals collected by each microphone, and [ ] ^T represents the transpose of the vector or matrix.

In some possible implementations, the processing unit is specifically used for:

According to the formula determining a phase spectrum difference value between each of the N microphones except the reference microphone and the reference microphone,

Among them, imag() means taking the imaginary part, ln() means taking the natural logarithm, represents the phase spectrum difference between the ith microphone and the reference microphone, represents the jth target signal frame of the reference microphone, represents the jth target signal frame of the ith microphone, Indicates the dominant frequency.

In some possible implementations, the processing unit is specifically used for:

Determine the power spectrum of each audio signal according to the K target signal frames corresponding to each audio signal;

According to the power spectrum of each audio signal, a power spectrum difference value between each of the N microphones except the reference microphone and the reference microphone is determined.

In some possible implementations, the processing unit is specifically used for:

According to the formula Calculate the power spectrum of each audio signal,

Among them, P _i (ω) represents the power spectrum of the ith audio signal, Y _i,j (ω) represents the jth target signal frame in the ith audio signal, and K represents the total frame of the signal collected by each microphone number, ω represents the frequency.

In some possible implementations, the processing unit is specifically used for:

Calculate the power spectrum difference between each of the N microphones except the reference microphone and the reference microphone according to the formula PD _i (ω)=P ₁ (ω)-P _i (ω),

Among them, PD _i (ω) represents the power spectrum difference between the ith microphone and the reference microphone, P ₁ (ω) represents the power spectrum of the reference microphone, and P _i (ω) represents the power spectrum of the ith microphone.

In some possible implementations, the processing unit is specifically used for:

determining the sampling frequency F _s and the number of FFT points N _fft of the N microphones when collecting audio signals, using a speaker to play Gaussian white noise data or frequency sweep signal data, and controlling the N microphones to collect the N audio signals, Wherein, if the data played by the speaker is frequency sweep signal data, the frequency sweep signal data is composed of M+1 segments of signals with equal lengths and unequal frequencies,

In some possible implementations, the processing unit is further configured to:

According to the formula calculating the frequency of each segment of the M+1 segment signal, and

Calculate each segment of the M+1 segment signals according to the formula S _i (t)=sin( _2πfi t),

Among them, f _i represents the frequency of the i-th segment signal, F _s represents the sampling frequency, N _fft represents the number of FFT points, S _i (t) represents the i-th segment signal, and the length of S ₁ (t) is an integer multiple of the period T, T=1/f ₁ .

In some possible implementations, the frequency sweep signal data played by the speaker is written in the following vector form:

S(t)=[S ₀ (t),S ₁ (t),…,S _M (t)] ^T

Among them, S(t) represents the frequency sweep signal data played by the speaker, S _i (t) represents the i-th signal, [ ] ^T represents the transpose of a vector or matrix.

In some possible implementations, the N microphones collect N audio signals respectively, wherein the audio signal collected by the ith microphone is represented as _xi (t), and _xi (t) can be written in the following vector form :

x _i (t)=[x _i,1 (t), _xi,2 (t),…, _xi,K (t)] ^T

Among them, x _i (t) represents the audio signal collected by the ith microphone, K represents the total number of frames of the signal collected by each microphone, [ ] ^T represents the transpose of the vector or matrix.

In some possible implementations, the obtaining unit is specifically used for:

The N microphones are placed in a test room, where a speaker is configured in the test room, and the N microphones are located directly in front of the speaker;

The speaker is controlled to play Gaussian white noise data or frequency sweep signal data, and the N microphones are controlled to collect the N audio signals respectively.

In some possible implementations, the test room has an anechoic chamber environment, the speaker is an artificial mouth dedicated to audio testing, and the artificial mouth is calibrated with a standard microphone before use.

In some possible implementations, before the processing unit controls the speaker to play Gaussian white noise data or frequency sweep signal data, the acquiring unit is further configured to:

In a quiet environment, obtain the first audio data X ₁ (n) collected by the N microphones within the first duration T ₁ ;

Under the environment of playing Gaussian white noise data or frequency sweep signal data, obtain the second audio data X ₂ (n) collected by the N microphones within the second time period T ₂ ;

trigger the processing unit according to the formula Calculate the signal-to-noise ratio SNR and ensure that the SNR is greater than a first threshold.

In a third aspect, an apparatus for evaluating the consistency of a microphone array is provided, including:

memory, for storing programs and data; and

a processor for calling and running the programs and data stored in the memory;

The apparatus is configured to perform the method of the first aspect above or any possible implementation thereof.

A fourth aspect provides a system for evaluating the consistency of microphone arrays, including:

N microphones forming a microphone array, N≥2;

at least one audio source;

An apparatus comprising a memory for storing programs and data and a processor for invoking and executing the programs and data stored in the memory, the apparatus being configured as a method in the above first aspect or any possible implementations thereof.

In a fifth aspect, a computer storage medium is provided, and program codes are stored in the computer storage medium, and the program codes can be used to instruct to execute the method in the first aspect or any possible implementation manners thereof.

In a sixth aspect, there is provided a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method of the first aspect or any possible implementations thereof.

Description of drawings

FIG. 1 is a schematic flowchart of a method for evaluating the consistency of a microphone array according to an embodiment of the present application.

FIG. 2 is a schematic diagram of a test environment according to an embodiment of the present application.

FIG. 3 is a schematic diagram of calculating a phase spectrum difference value according to an embodiment of the present application.

FIG. 4 is a schematic diagram of calculating a power spectrum difference according to an embodiment of the present application.

FIG. 5 is a schematic diagram of a phase spectrum difference between two microphones according to an embodiment of the present application.

FIG. 6 is a schematic diagram of a phase spectrum difference value after calibration between two microphones according to an embodiment of the present application.

FIG. 7a is a schematic diagram of power spectra of two microphones according to an embodiment of the present application.

FIG. 7b is a schematic diagram of a power spectrum difference between two microphones according to an embodiment of the present application.

FIG. 8 is a schematic structural diagram of a device for evaluating the consistency of a microphone array according to an embodiment of the present application.

FIG. 9 is a schematic structural diagram of an apparatus for evaluating the consistency of a microphone array according to an embodiment of the present application.

FIG. 10 is a schematic structural diagram of a system for evaluating the consistency of a microphone array according to an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be clearly described below with reference to the accompanying drawings in the embodiments of the present application.

Microphone Array refers to a system composed of a certain number of microphones (acoustic sensors) used to sample and process the spatial characteristics of the sound field. Using the difference between the phases of the sound waves received by the two microphones to filter the sound waves, the ambient background sound can be removed to the maximum extent, and only the required sound waves are left.

The assumption of the multi-channel speech enhancement technology algorithm is that the target speech components of multiple microphones in the microphone array are highly correlated, and the target speech is not correlated with non-target interference, so the consistency between different microphones in the microphone array directly affects the performance of the algorithm.

Quantitative evaluation of microphone consistency can be used to guide the design of microphones and microphone arrays. The circuits, electronic components, and acoustic structures of microphone arrays will affect the consistency of microphones. When designing microphone arrays, various factors can be tested item by item. The effect of consistency, so that the design of microphone consistency can meet the system requirements.

Quantitative evaluation of microphone consistency can be used to compare the robustness of different algorithms. On the premise of achieving the same speech enhancement performance, the lower the requirements for consistency indicators, the better the algorithm robustness.

In the embodiment of the present application, the consistency is measured from the two aspects of the amplitude spectrum difference value and the phase spectrum difference value, which is objective and accurate, and the quantitative consistency evaluation method can objectively guide the design of the microphone array, and can also objectively guide the design of the microphone array. A comparison of the robustness of multi-channel speech enhancement algorithms.

Hereinafter, with reference to FIGS. 1 to 7 , the method for evaluating the consistency of the microphone array according to the embodiment of the present application will be described in detail.

FIG. 1 is a schematic flowchart of a method for evaluating the consistency of a microphone array according to an embodiment of the present application. It should be understood that FIG. 1 shows steps or operations of the method, but these steps or operations are only examples, and the embodiments of the present application may also perform other operations or variations of the respective operations in FIG. 1 . The method may be performed by a device for evaluating the consistency of a microphone array, where the device for evaluating the consistency of the microphone array may be a mobile phone, a tablet computer, a portable computer, a Personal Digital Assistant (PDA), and the like.

S110: Acquire N audio signals respectively collected by N microphones, where the N microphones form a microphone array, and N≥2.

When evaluating the consistency of N microphones, it is necessary to limit the environment where the N microphones are located, that is, the N audio signals are collected in a special test environment.

Specifically, as shown in FIG. 2 , a microphone array 201 composed of the N microphones is placed in a test room 202 , and a speaker 203 is configured in the test room 202 , and the microphone array 201 is located directly in front of the speaker 203 , the microphone array 201 and the speaker 203 are connected to a control device 204 such as a computer. The control device 204 can control the speaker 203 to play specific audio data, for example, to play Gaussian white noise data or frequency sweep signal data, and at the same time, the control device 204 can obtain from the microphone array 201 the N data collected by the N microphone distributions audio signal.

It should be noted that the microphone conformance assessment requires that the signal-to-noise ratio of the collected audio signal is high enough and the background noise is weak enough, so the test environment requires a quiet environment. In particular, an anechoic room environment is required within the test room 202 . The speaker 203 requires a high signal-to-noise ratio and a flat frequency response curve. In particular, the speaker uses an artificial mouth dedicated to audio testing and is calibrated with a standard microphone before use. The microphone array 201 is placed directly in front of the loudspeaker 203, in particular, it is required to be placed in a position for standard microphone calibration.

Optionally, before the formal audio signal collection is performed, a signal-to-noise ratio (signal-to-noise ratio, SNR) detection needs to be performed on the above-mentioned test environment.

Specifically, in the test environment shown in FIG. 2 , first, in a quiet environment (that is, the speaker 203 is in a closed state), first audio data X ₁ collected by the N microphones within the first time period T ₁ is acquired (n); Then, under the environment of playing Gaussian white noise data or frequency sweep signal data (that is, the control device 204 controls the speaker 203 to play Gaussian white noise data or frequency sweep signal data), obtain the N microphones in the second The second audio data X ₂ (n) collected within the duration T ₂ ; then, the SNR is calculated according to the following formula 1; finally, when the SNR is greater than the set threshold, the detection passes, otherwise the detection fails.

Wherein, T ₁ represents the first duration, T ₂ represents the second duration, X ₁ (n) represents the first audio data, and X ₂ (n) represents the second audio data.

It should be noted that if the test fails, the above test environment needs to be adjusted or calibrated to eliminate some factors that may affect the SNR, until the SNR calculated according to the above formula 1 is greater than the set threshold.

Optionally, in this embodiment of the present application, using the test environment shown in FIG. 2 to collect audio signals may specifically include:

Determine the sampling frequency F _s and the number of FFT points N _fft of the N microphones when collecting audio signals, use the speaker to play Gaussian white noise data or frequency sweep signal data, and collect the N audio signals by the N microphones.

Optionally, the number of FFT points N _fft is an even number, generally 32, 64, 128, .

It should be noted that, if the data played by the speaker is frequency sweep signal data, the frequency sweep signal data is composed of M+1 segments of signals with equal lengths and unequal frequencies.

Optionally, the frequency of each segment of the M+1 segment signal may be calculated according to the following formula 2, and the frequency of each segment of the M+1 segment signal may be calculated according to the following formula 3.

Among them, f _i is the frequency of the i-th signal, F _s is the sampling frequency, and N _fft is the number of FFT points.

S _i (t)=sin(2πf _i t) Equation 3

Among them, S _i (t) represents the i-th segment signal, and f _i is the frequency of the i-th segment signal.

It should be noted that the length of the first segment signal S ₁ (t) is an integer multiple of the period T, T=1/f ₁ .

Optionally, the frequency sweep signal data played by the speaker can be written in the following vector form:

S(t)=[S ₀ (t),S ₁ (t),…,S _M (t)] ^T

Among them, S(t) represents the frequency sweep signal data played by the speaker, S _i (t) represents the i-th signal, [ ] ^T represents the transpose of a vector or matrix.

Optionally, N audio signals are collected by the N microphones, wherein the audio signal collected by the ith microphone is represented as _xi (t), and _xi (t) can be written in the following vector form:

x _i (t)=[x _i,1 (t), _xi,2 (t),…, _xi,K (t)] ^T

Among them, x _i (t) represents the audio signal collected by the ith microphone, K represents the total number of frames of the signal collected by each microphone, [ ] ^T represents the transpose of the vector or matrix.

S120: Determine, according to the N audio signals, a phase spectrum difference value and/or a power spectrum difference value between each of the N microphones except the reference microphone and the reference microphone, where the reference microphones are the N Any of the microphones.

Optionally, in this embodiment of the present application, after the N audio signals are collected, the audio signals can be divided into frames, each frame of audio signals can be windowed, and each frame of windowed signals can be subjected to FFT transformation to obtain the difference between different microphones. phase spectrum difference.

Specifically, as shown in FIG. 3 , assuming that the N audio signals are x ₁ (t), x ₂ (t), . . . , x _N (t), each of the N audio signals is divided into frame, to obtain K signal frames of equal length, K≥2, for example, divide the ith audio signal into frames to obtain K signal frames of equal length, and write the following vector form:

x _i (t)=[x _i,1 (t), _xi,2 (t),…, _xi,K (t)] ^T

Among them, x _i (t) represents the ith audio signal, K represents the total number of frames of the signal collected by each microphone, [ ] ^T represents the transpose of the vector or matrix;

Perform windowing processing on each of the K signal frames to obtain K windowed signal frames, for example, add a window to the jth frame x _i,j of the ith audio signal to obtain the ith audio The jth windowed signal frame of the signal _yi,j = _xi,j ×Win;

Perform FFT transformation on each of the K windowed signal frames to obtain K target signal frames, for example, for the jth windowed signal frame y _i,j (t) of the ith audio signal Do FFT transformation to obtain the j-th target signal frame Yi _,j (ω) of the i-th audio signal;

According to the K target signal frames corresponding to each audio signal, determine the phase spectrum difference between each of the N microphones except the reference microphone and the reference microphone, for example, assuming the jth target The dominant frequency of the signal frame is Then the main frequency of the i-th microphone and the reference microphone can be calculated according to the following formula 4: The phase spectrum difference at .

Among them, imag() means taking the imaginary part, ln() means taking the natural logarithm, represents the phase spectrum difference between the ith microphone and the reference microphone, represents the jth target signal frame of the reference microphone, represents the jth target signal frame of the ith microphone, Indicates the dominant frequency.

It should be noted that in FIG. 3 above, the first microphone is used as the reference microphone, that is, the phase spectrum difference values between each microphone except the first microphone and the first microphone are calculated separately, and The first microphone corresponds to the audio signal x ₁ (t), the second microphone corresponds to the audio signal x ₂ (t), ..., and the Nth microphone corresponds to the audio signal x _N (t).

Optionally, K represents the total number of frames of signals received by each microphone.

It should be noted that the windowing process is used to eliminate the truncation effect caused by frame division. Optionally, each of the K signal frames may be processed by adding a Hamming window.

In some possible implementations, any two adjacent signal frames in the K signal frames overlap by R%, and R>0. For example, the R is 25 or 50. In other words, any two adjacent signal frames among the K signal frames overlap by 25% or 50%.

Optionally, the signal amplitude remains unchanged after overlapping windowing.

It should be understood that each frame signal after the overlap has the components of the previous frame to prevent discontinuity between the two frames.

Optionally, in this embodiment of the present application, when the phase consistency evaluation is performed, the N audio signals are signals collected in an environment where frequency sweep signal data is played. In other words, when calculating the above-mentioned phase spectrum difference, the N audio signals are signals collected in an environment of playing frequency sweep signal data.

Therefore, the phase difference of any frequency ω can be calculated, that is, the phase spectrum difference PDiff _i (ω) between the ith microphone and the reference microphone can be obtained, that is, the above

Optionally, in this embodiment of the present application, after the N audio signals are collected, the audio signals can be divided into frames, each frame of audio signals can be windowed, and FFT transformation can be performed on each frame of the windowed signals, and after the FFT transformation is obtained. The power spectrum of each frame of the signal, and find the power spectrum difference between different microphones.

Specifically, as shown in FIG. 4 , assuming that the N audio signals are x ₁ (t), x ₂ (t), . . . , x _N (t), each of the N audio signals is divided into frame, to obtain K signal frames of equal length, K≥2, for example, divide the ith audio signal into frames to obtain K signal frames of equal length, and write the following vector form:

x _i (t)=[x _i,1 (t), _xi,2 (t),…, _xi,K (t)] ^T

Among them, x _i (t) represents the ith audio signal, K represents the total number of frames of the signal received by each microphone, [ ] ^T represents the transpose of the vector or matrix;

Perform windowing processing on each of the K signal frames to obtain K windowed signal frames, for example, add a window to the jth frame x _i,j of the ith audio signal to obtain the ith audio The jth windowed signal frame of the signal _yi,j = _xi,j ×Win;

Perform FFT transformation on each of the K windowed signal frames to obtain K target signal frames, for example, for the jth windowed signal frame y _i,j (t) of the ith audio signal Do FFT transformation to obtain the j-th target signal frame Yi _,j (ω) of the i-th audio signal;

Determine the power spectrum of each audio signal according to the K target signal frames corresponding to each audio signal, for example, calculate the power spectrum of the ith audio signal according to the following formula 5;

According to the power spectrum of each audio signal, determine the power spectrum difference between each of the N microphones except the reference microphone and the reference microphone, for example, calculate the ith microphone and the reference microphone according to the following formula 6 The power spectrum difference between the reference microphones.

Among them, P _i (ω) represents the power spectrum of the ith audio signal, Y _i,j (ω) represents the jth target signal frame in the ith audio signal, ω represents the frequency, and K represents the frequency collected by each microphone. The total number of frames of the signal.

PD _i (ω)=P ₁ (ω)-P _i (ω) Equation 6

Among them, PD _i (ω) represents the power spectrum difference between the ith microphone and the reference microphone, P ₁ (ω) represents the power spectrum of the reference microphone, and P _i (ω) represents the power spectrum of the ith microphone.

It should be noted that in the above-mentioned FIG. 4, the first microphone is used as the reference microphone, that is, the power spectrum difference between each microphone except the first microphone and the first microphone is calculated separately, and The first microphone corresponds to the audio signal x ₁ (t), the second microphone corresponds to the audio signal x ₂ (t), ..., and the Nth microphone corresponds to the audio signal x _N (t).

It should be noted that the windowing process is used to eliminate the truncation effect caused by frame division. Optionally, each of the K signal frames may be processed by adding a Hamming window.

In some possible implementations, any two adjacent signal frames in the K signal frames overlap by R%, and R>0. For example, the R is 25 or 50. In other words, any two adjacent signal frames among the K signal frames overlap by 25% or 50%.

Optionally, the signal amplitude remains unchanged after overlapping windowing.

It should be understood that each frame signal after the overlap has the components of the previous frame to prevent discontinuity between the two frames.

Optionally, in this embodiment of the present application, when the amplitude consistency evaluation is performed, the N audio signals are signals collected in an environment where Gaussian white noise data or frequency sweep signal data is played. In other words, when calculating the above power spectrum difference, the N audio signals are signals collected in an environment where Gaussian white noise data or frequency sweep signal data is played.

S130, according to the phase spectrum difference and/or the power spectrum difference between each of the N microphones except the reference microphone and the reference microphone, perform consistency evaluation on the N microphones.

Specifically, the phase spectrum difference value is used for phase consistency evaluation, and the power spectrum difference value is used for amplitude consistency evaluation.

Optionally, in this embodiment of the present application, according to the phase spectrum difference value between each of the N microphones except the reference microphone and the reference microphone, the corresponding microphone and the reference microphone are evaluated. phase coherence between.

It should be noted that the smaller the phase spectrum difference between the two microphones, the better the phase consistency between the two microphones.

For example, the phase spectrum difference value between microphone 1 and the reference microphone is A, and the smaller A is, the better the phase consistency between microphone 1 and the reference microphone is.

Optionally, a threshold can be set. If the phase spectrum difference between the two microphones is less than this threshold, it means that the phase consistency between the two microphones meets the design requirements, and the consistency between the two microphones The effect on the multi-channel speech enhancement algorithm is negligible, or the consistency between the two microphones has no effect on the multi-channel speech enhancement algorithm.

It should be noted that the above threshold can be flexibly configured according to different multi-channel speech enhancement algorithms.

It should be noted that since the distances from different microphones to the sound source are difficult to be completely consistent when collecting data, there is a fixed phase difference between different microphones.

Optionally, in this embodiment of the present application, the above-mentioned phase spectrum difference value may be calibrated by using a fixed phase difference.

Specifically, measure the distance difference between each of the N microphones except the reference microphone and the reference microphone to the sound source, for example, d _i represents the distance difference between the ith microphone and the reference microphone to the sound source;

According to the measured distance difference, calculate the fixed phase difference between each of the N microphones except the reference microphone and the reference microphone, for example, the ith microphone and the reference microphone can be calculated according to the following formula 7 A fixed phase difference between;

According to the fixed phase difference between each of the N microphones except the reference microphone and the reference microphone, the corresponding phase spectrum difference values are respectively calibrated.

Among them, Y _i (ω) represents the spectrum of the ith microphone, Y ₁ (ω) represents the spectrum of the reference microphone, ω represents the frequency, d _i represents the distance difference between the ith microphone and the reference microphone to the sound source, and c represents the speed of sound , 2πωd _i /c represents the fixed phase difference between the ith microphone and the reference microphone.

It should be noted that the fixed phase difference and the signal frequency satisfy a linear relationship, therefore, the fixed phase difference can be determined by means of linear fitting.

For example, the fixed phase difference between microphone 1 and the reference microphone is A, and the phase spectrum difference between microphone 1 and the reference microphone is B. As shown in Figure 5, the straight line represents the fitted difference between microphone 1 and the reference microphone. The fixed phase difference between the two, the curve part represents the phase spectrum difference between the microphone 1 and the reference microphone, and the whole shows that as the frequency increases from 0Hz to 8000Hz, the phase spectrum between the microphone 1 and the reference microphone The difference between the microphone 1 and the reference microphone changes from 0 The radians are reduced to -2 radians. After calibration, the phase spectrum difference between microphone 1 and the reference microphone is C, as shown in the curve in Figure 6, at this time, C=B-A, which shows that as the frequency increases from 0Hz to 8000Hz, the difference between microphone 1 and The phase spectrum difference between the reference microphones fluctuates between 0 radians and ±0.5 radians.

It can be seen from the comparison between Figure 5 and Figure 6 that the fixed phase difference will have a greater impact on the phase spectrum difference between the two microphones. Therefore, when evaluating the amplitude consistency of the two microphones, it is necessary to eliminate the difference between the two microphones. The effect of a fixed phase difference.

Optionally, in this embodiment of the present application, according to the power spectrum difference between each of the N microphones except the reference microphone and the reference microphone, the amplitude between the corresponding microphone and the reference microphone is evaluated. consistency.

It should be noted that the smaller the power spectrum difference between the two microphones, the better the amplitude consistency between the two microphones.

For example, as shown in Figure 7, specifically, Figure 7a shows the power spectrum of the microphone 1 and the power spectrum of the reference microphone, Figure 7b shows the power spectrum difference between the microphone 1 and the reference microphone, the microphone 1 and the reference microphone The power spectrums between the microphones are not very different, and the maximum value of their power spectrum differences is <±1 decibel (dB).

Optionally, a threshold can be set. If the power spectrum difference between the two microphones is less than this threshold, it means that the amplitude consistency between the two microphones meets the design requirements, and the consistency between the two microphones The effect on the multi-channel speech enhancement algorithm is negligible, or the consistency between the two microphones has no effect on the multi-channel speech enhancement algorithm.

It should be noted that the above threshold can be flexibly configured according to different multi-channel speech enhancement algorithms.

Optionally, in this embodiment of the present application, the influence of factors such as circuits, electronic components, and acoustic structures of the microphone array on the consistency of the microphone may be tested item by item, so as to guide the calibration of the microphone array. Specifically, it may be used to guide the microphone array. Design and design of microphone arrays to evaluate the robustness of multi-channel enhancement algorithms.

Therefore, in this embodiment of the present application, the phase spectrum difference value and/or the power spectrum difference value between each microphone and the reference microphone may be determined according to the N audio signals collected by the N microphones, so that the N microphones can be consistent with each other. performance evaluation, eliminate the influence of the consistency between microphones on the multi-channel speech enhancement algorithm, and improve the user experience.

Optionally, as shown in FIG. 8 , an embodiment of the present application provides a device 800 for evaluating the consistency of a microphone array, including:

an acquisition unit 810, configured to acquire N audio signals collected by N microphones respectively, where the N microphones constitute a microphone array, and N≥2;

a processing unit 820, configured to determine, according to the N audio signals, a phase spectrum difference value and/or a power spectrum difference value between each of the N microphones except the reference microphone and the reference microphone, The reference microphone is any one of the N microphones;

The processing unit 820 is further configured to, according to the phase spectrum difference value and/or the power spectrum difference value between each of the N microphones except the reference microphone and the reference microphone, perform a N microphones for consistency evaluation.

Optionally, the processing unit 820 is specifically configured to:

According to the phase spectrum difference value between each of the N microphones except the reference microphone and the reference microphone, the phase consistency between the corresponding microphone and the reference microphone is evaluated.

Optionally, the processing unit 820 is further configured to:

respectively measuring the distance difference between each of the N microphones except the reference microphone and the reference microphone to the sound source;

Calculate the fixed phase difference between each of the N microphones except the reference microphone and the reference microphone according to the measured distance difference;

According to the fixed phase difference between each of the N microphones except the reference microphone and the reference microphone, the corresponding phase spectrum difference values are respectively calibrated.

Optionally, the processing unit 820 is specifically configured to:

According to the formula Calculate the fixed phase difference between each of the N microphones except the reference microphone and the reference microphone, respectively,

Among them, Y _i (ω) represents the spectrum of the ith microphone, Y ₁ (ω) represents the spectrum of the reference microphone, ω represents the frequency, d _i represents the distance difference between the ith microphone and the reference microphone to the sound source, and c represents the speed of sound , 2πωd _i /c represents the fixed phase difference between the ith microphone and the reference microphone.

Optionally, the processing unit 820 is specifically configured to:

According to the power spectrum difference between each of the N microphones except the reference microphone and the reference microphone, the amplitude consistency between the corresponding microphone and the reference microphone is evaluated.

Optionally, the N audio signals are signals collected in an environment of playing frequency sweep signal data.

Optionally, the N audio signals are signals collected in an environment of playing Gaussian white noise data or frequency sweep signal data.

Optionally, the frequency sweep signal is any one of a linear frequency sweep signal, a logarithmic frequency sweep signal, a linear step frequency sweep signal, and a logarithmic step frequency sweep signal.

Optionally, the processing unit 820 is specifically configured to:

Framing each audio signal in the N audio signals to obtain K signal frames of equal length, K≥2;

Windowing is performed on each of the K signal frames to obtain K windowed signal frames;

Perform FFT transformation on each of the K windowed signal frames to obtain K target signal frames;

Determine, according to the K target signal frames corresponding to each audio signal, a phase spectrum difference value and/or a phase spectrum difference between each of the N microphones except the reference microphone and the reference microphone Power spectrum difference.

Optionally, any two adjacent signal frames in the K signal frames overlap by R%, and R>0.

Optionally, the R is 25 or 50.

Optionally, the ith audio signal is divided into frames to obtain K signal frames of equal length and written in the following vector form:

x _i (t)=[x _i,1 (t), _xi,2 (t),…, _xi,K (t)] ^T

Among them, x _i (t) represents the ith audio signal, K represents the total number of frames of signals collected by each microphone, and [ ] ^T represents the transpose of the vector or matrix.

Optionally, the processing unit 820 is specifically configured to:

According to the formula determining a phase spectrum difference value between each of the N microphones except the reference microphone and the reference microphone,

Among them, imag() means taking the imaginary part, ln() means taking the natural logarithm, represents the phase spectrum difference between the ith microphone and the reference microphone, represents the jth target signal frame of the reference microphone, represents the jth target signal frame of the ith microphone, Indicates the dominant frequency.

Optionally, the processing unit 820 is specifically configured to:

Determine the power spectrum of each audio signal according to the K target signal frames corresponding to each audio signal;

According to the power spectrum of each audio signal, a power spectrum difference value between each of the N microphones except the reference microphone and the reference microphone is determined.

Optionally, the processing unit 820 is specifically configured to:

According to the formula Calculate the power spectrum of each audio signal,

Among them, P _i (ω) represents the power spectrum of the ith audio signal, Y _i,j (ω) represents the jth target signal frame in the ith audio signal, and K represents the total frame of the signal collected by each microphone number, ω represents the frequency.

Optionally, the processing unit 820 is specifically configured to:

Calculate the power spectrum difference between each of the N microphones except the reference microphone and the reference microphone according to the formula PD _i (ω)=P ₁ (ω)-P _i (ω),

Among them, PD _i (ω) represents the power spectrum difference between the ith microphone and the reference microphone, P ₁ (ω) represents the power spectrum of the reference microphone, and P _i (ω) represents the power spectrum of the ith microphone.

Optionally, the processing unit 820 is specifically configured to:

determining the sampling frequency F _s and the number of FFT points N _fft of the N microphones when collecting audio signals, using a speaker to play Gaussian white noise data or frequency sweep signal data, and controlling the N microphones to collect the N audio signals, Wherein, if the data played by the speaker is frequency sweep signal data, the frequency sweep signal data is composed of M+1 segments of signals with equal lengths and unequal frequencies,

Optionally, the processing unit 820 is further configured to:

According to the formula calculating the frequency of each segment of the M+1 segment signal, and

Calculate each segment of the M+1 segment signals according to the formula S _i (t)=sin( _2πfi t),

Among them, f _i represents the frequency of the i-th segment signal, F _s represents the sampling frequency, N _fft represents the number of FFT points, S _i (t) represents the i-th segment signal, and the length of S ₁ (t) is an integer multiple of the period T, T=1/f ₁ .

Optionally, the frequency sweep signal data played by the speaker is written in the following vector form:

S(t)=[S ₀ (t),S ₁ (t),…,S _M (t)] ^T

Among them, S(t) represents the frequency sweep signal data played by the speaker, S _i (t) represents the i-th signal, [ ] ^T represents the transpose of a vector or matrix.

Optionally, the N microphones collect N audio signals respectively, wherein the audio signal collected by the i-th microphone is represented as x _i (t), and x _i (t) can be written in the following vector form:

x _i (t)=[x _i,1 (t), _xi,2 (t),…, _xi,K (t)] ^T

Among them, x _i (t) represents the audio signal collected by the ith microphone, K represents the total number of frames of the signal collected by each microphone, [ ] ^T represents the transpose of the vector or matrix.

Optionally, the obtaining unit 810 is specifically configured to:

The N microphones are placed in a test room, where a speaker is configured in the test room, and the N microphones are located directly in front of the speaker;

The speaker is controlled to play Gaussian white noise data or frequency sweep signal data, and the N microphones are controlled to collect the N audio signals respectively.

Optionally, the test room has an anechoic room environment, the speaker is an artificial mouth dedicated to audio testing, and the artificial mouth is calibrated with a standard microphone before use.

Optionally, before the processing unit 820 controls the speaker to play Gaussian white noise data or frequency sweep signal data, the acquiring unit 810 is further configured to:

In a quiet environment, obtain the first audio data X ₁ (n) collected by the N microphones within the first duration T ₁ ;

Under the environment of playing Gaussian white noise data or frequency sweep signal data, obtain the second audio data X ₂ (n) collected by the N microphones within the second time period T ₂ ;

trigger the processing unit 820 according to the formula Calculate the signal-to-noise ratio SNR and ensure that the SNR is greater than a first threshold.

Optionally, as shown in FIG. 9 , an embodiment of the present application provides an apparatus 900 for evaluating the consistency of a microphone array, including:

memory 910 for storing programs and data; and

a processor 920, for calling and running the programs and data stored in the memory;

The apparatus 900 is configured to perform the methods shown in FIGS. 1 to 7 above.

Optionally, as shown in FIG. 10 , an embodiment of the present application provides a system 1000 for evaluating the consistency of a microphone array, including:

N microphones constituting the microphone array 1010, N≥2;

at least one audio source 1020;

An apparatus 1030, comprising a memory 1031 for storing programs and data and a processor 1032 for calling and executing the programs and data stored in the memory, is configured as the method shown in FIGS. 1 to 7 above.

It should be understood that, in various embodiments of the present application, the size of the sequence numbers of the above-mentioned processes does not mean the sequence of execution, and the execution sequence of each process should be determined by its functions and internal logic, and should not be dealt with in the embodiments of the present application. implementation constitutes any limitation.

Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of this application.

Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described systems, devices and units may refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

In the several embodiments provided in this application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are only illustrative. For example, the division of the unit is only a logical function division. In actual implementation, there may be other division methods, for example, multiple units or components may be combined or Integration into another system, or some features can be ignored, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.

The integrated unit, if implemented in the form of a software functional unit and sold or used as an independent product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be embodied in the form of a software product in essence, or the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: U disk, removable hard disk, Read-Only Memory (ROM, Read-Only Memory), Random Access Memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program codes .

The above are only specific embodiments of the present application, but the protection scope of the present application is not limited to this. should be covered within the scope of protection of this application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (48)

1. a kind of method for assessing microphone array consistency characterized by comprising

N number of audio signal that N number of microphone acquires respectively is obtained, N number of microphone constitutes microphone array, N >=2；

According to N number of audio signal, determine each microphone in N number of microphone in addition to reference microphone with it is described Phase spectrum difference and/or power spectrum difference between reference microphone, the reference microphone are appointing in N number of microphone It anticipates a microphone；

According to each microphone in N number of microphone in addition to the reference microphone and between the reference microphone Phase spectrum difference and/or power spectrum difference carry out compliance evaluation to N number of microphone.

2. the method according to claim 1, wherein it is described according in N number of microphone remove reference microphone Except each microphone and the reference microphone between phase spectrum difference, consistency is carried out to the N number of microphone and is commented Estimate, comprising:

According to each microphone in N number of microphone in addition to the reference microphone and between the reference microphone Phase spectrum difference assesses the phase equalization between corresponding microphone and the reference microphone.

3. according to the method described in claim 2, it is characterized in that, the method also includes:

Each microphone in N number of microphone in addition to the reference microphone is measured respectively to arrive with the reference microphone The range difference of sound source；

According to measured range difference, each Mike in N number of microphone in addition to the reference microphone is calculated separately Fixed skew between wind and the reference microphone；

According to each microphone in N number of microphone in addition to the reference microphone and between the reference microphone Fixed skew calibrates its corresponding phase spectrum difference respectively.

4. according to the method described in claim 3, it is characterized in that, the distance according to measured by, calculates separately described N number of The fixed skew between each microphone and the reference microphone in microphone in addition to the reference microphone, packet It includes:

According to formulaIt calculates separately in N number of microphone in addition to the reference microphone Fixed skew between each microphone and the reference microphone,

Wherein, Y_i(ω) indicates the frequency spectrum of i-th of microphone, Y₁(ω) indicates that the frequency spectrum of reference microphone, ω indicate frequency, d_i Indicate the range difference of i-th of microphone and reference microphone to sound source, the c expression velocity of sound, 2 π ω d_i/ c indicate i-th microphone with Fixed skew between reference microphone.

5. method according to claim 1 to 4, which is characterized in that described to be removed according in N number of microphone The phase spectrum difference between each microphone and the reference microphone except reference microphone, to N number of microphone into Row compliance evaluation, comprising:

According to each microphone in N number of microphone in addition to the reference microphone and between the reference microphone Power spectrum difference assesses the amplitude coincidence between corresponding microphone and the reference microphone.

6. method according to any one of claim 2 to 4, which is characterized in that N number of audio signal is swept in broadcasting The signal acquired in the environment of frequency signal data.

7. according to the method described in claim 5, it is characterized in that, N number of audio signal is to play white Gaussian noise number According to or swept-frequency signal data in the environment of the signal that acquires.

8. method according to claim 6 or 7, which is characterized in that the swept-frequency signal is Linear chirp, logarithm is swept Frequency signal, linear stepping swept-frequency signal, any one in logarithm stepping swept-frequency signal.

9. method according to any one of claim 1 to 8, which is characterized in that it is described according to N number of audio signal, Determine each microphone in N number of microphone in addition to reference microphone and the Phase spectrum difference between the reference microphone Value and/or power spectrum difference, comprising:

Each audio signal in N number of audio signal is subjected to framing, obtains K signal frame of equal length, K >=2；

Windowing process is done to each signal frame in the K signal frame, obtains K windowing signal frame；

FFT transform is done to each windowing signal frame in the K windowing signal frame, obtains K echo signal frame；

According to the corresponding K echo signal frame of each audio signal, determine in N number of microphone except the reference The phase spectrum difference and/or power spectrum difference between each microphone and the reference microphone except microphone.

10. according to the method described in claim 9, it is characterized in that, any two adjacent signals frame weight in the K signal frame Folded R%, R > 0.

11. according to the method described in claim 10, it is characterized in that, the R is 25 or 50.

12. the method according to any one of claim 9 to 11, which is characterized in that divided i-th of audio signal Frame, K signal frame for obtaining equal length are write as following vector form:

x_i(t)=[x_i,1(t),x_i,2(t),…,x_i,K(t)]^T

Wherein, x_i(t) i-th of audio signal is indicated, K indicates that each microphone collects the totalframes of signal, []^TIndicate vector Or the transposition of matrix.

13. the method according to any one of claim 9 to 12, which is characterized in that described to be believed according to each audio Number corresponding K echo signal frame, determines each microphone in N number of microphone in addition to the reference microphone Phase spectrum difference between the reference microphone, comprising:

According to formulaIt determines in N number of microphone in addition to the reference microphone Each microphone and the reference microphone between phase spectrum difference,

Wherein, imag () expression takes imaginary part, and ln () expression takes natural logrithm,It indicates i-th of microphone and refers to wheat Phase spectrum difference between gram wind,Indicate j-th of echo signal frame of reference microphone,Indicate i-th of wheat J-th of echo signal frame of gram wind,Indicate basic frequency.

14. the method according to any one of claim 9 to 13, which is characterized in that described to be believed according to each audio Number corresponding K echo signal frame, determines each microphone in N number of microphone in addition to the reference microphone Power spectrum difference between the reference microphone, comprising:

According to the corresponding K echo signal frame of each audio signal, the power spectrum of each audio signal is determined；

According to the power spectrum of each audio signal, determine every in addition to the reference microphone in N number of microphone Power spectrum difference between a microphone and the reference microphone.

15. according to the method for claim 14, which is characterized in that described corresponding described according to each audio signal K echo signal frame determines the power spectrum of each audio signal, comprising:

According to formulaThe power spectrum of each audio signal is calculated,

Wherein, P_i(ω) indicates the power spectrum of i-th of audio signal, Y_i,j(ω) indicates j-th of target in i-th of audio signal Signal frame, K indicate that each microphone collects the totalframes of signal, and ω indicates frequency.

16. method according to claim 14 or 15, which is characterized in that the power according to each audio signal Spectrum, determines each microphone in N number of microphone in addition to the reference microphone and between the reference microphone Power spectrum difference, comprising:

According to formula PD_i(ω)=P₁(ω)-P_i(ω) calculates each wheat in N number of microphone in addition to reference microphone Power spectrum difference gram between wind and the reference microphone,

Wherein, PD_i(ω) indicates the power spectrum difference between i-th of microphone and reference microphone, P₁(ω) indicates to refer to Mike The power spectrum of wind, P_i(ω) indicates the power spectrum of i-th of microphone.

17. according to claim 1 to method described in any one of 16, which is characterized in that the N number of microphone of acquisition is adopted respectively N number of audio signal of collection, comprising:

Determine sample frequency F of the N number of microphone when carrying out audio signal sample_sWith FFT points N_fft, broadcast using loudspeaker White Gaussian noise data or swept-frequency signal data are put, N number of microphone acquires N number of audio signal, wherein if described The data that loudspeaker is played are swept-frequency signal data, the swept-frequency signal data are equal by M+1 segment length and frequency not etc. Signal is constituted,

18. according to the method for claim 17, which is characterized in that

According to formulaThe frequency of every segment signal in the M+1 segment signal is calculated, and

According to formula S_i(t)=sin (2 π f_iT) every segment signal in the M+1 segment signal is calculated,

Wherein, f_iIndicate the frequency of the i-th segment signal, F_sIndicate sample frequency, N_fftIndicate FFT points, S_i(t) i-th section of letter is indicated Number, and S₁(t) length is the integral multiple of cycle T, T=1/f₁。

19. according to the method for claim 18, which is characterized in that the swept-frequency signal data that the loudspeaker is played are write as Following vector form:

S (t)=[S₀(t),S₁(t),…,S_M(t)]^T

Wherein, S (t) indicates the swept-frequency signal data that loudspeaker is played, S_i(t) the i-th segment signal is indicated,[]^TTable Show the transposition of vector or matrix.

20. according to claim 1 to method described in any one of 19, which is characterized in that N number of microphone collects respectively N number of audio signal, wherein the collected audio signal of i-th of microphone is expressed as x_iAnd x (t),_i(t) it can be write as following vector Form:

x_i(t)=[x_i,1(t),x_i,2(t),…,x_i,K(t)]^T

Wherein, x_i(t) the collected audio signal of i-th of microphone is indicated, K indicates that each microphone collects total frame of signal Number, []^TIndicate the transposition of vector or matrix.

21. according to claim 1 to method described in any one of 20, which is characterized in that the N number of microphone of acquisition is adopted respectively N number of audio signal of collection, comprising:

N number of microphone is placed in test room, loudspeaker, N number of microphone are configured in the test room Positioned at the front of the loudspeaker；

It controls the loudspeaker and plays white Gaussian noise data or swept-frequency signal data, and control N number of microphone point N number of audio signal is not acquired.

22. according to the method for claim 21, which is characterized in that there is noise reduction room environmental in the test room, it is described Loudspeaker is audio-frequency test Special artificial mouth, and the artificial mouth is calibrated with standard microphone before the use.

23. the method according to claim 21 or 22, which is characterized in that play white Gaussian noise controlling the loudspeaker Before data or swept-frequency signal data, the method also includes:

Under quiet environment, N number of microphone is obtained in the first duration T₁First audio data X of interior acquisition₁(n)；

In the environment of playing white Gaussian noise data or swept-frequency signal data, N number of microphone is obtained in the second duration T₂ The second audio data X of interior acquisition₂(n)；

According to formulaSignal to Noise Ratio (SNR) is calculated, and ensures that the SNR is greater than first threshold.

24. a kind of equipment for assessing microphone array consistency characterized by comprising

Acquiring unit, the N number of audio signal acquired respectively for obtaining N number of microphone, N number of microphone constitute microphone array Column, N >=2；

Processing unit, for determining every in addition to reference microphone in N number of microphone according to N number of audio signal Phase spectrum difference and/or power spectrum difference between a microphone and the reference microphone, the reference microphone are the N Any one microphone in a microphone；

The processing unit, be also used to according in N number of microphone in addition to the reference microphone each microphone with Phase spectrum difference and/or power spectrum difference between the reference microphone carry out compliance evaluation to N number of microphone.

25. equipment according to claim 24, which is characterized in that the processing unit is specifically used for:

According to each microphone in N number of microphone in addition to the reference microphone and between the reference microphone Phase spectrum difference assesses the phase equalization between corresponding microphone and the reference microphone.

26. equipment according to claim 25, which is characterized in that the processing unit is also used to:

Each microphone in N number of microphone in addition to the reference microphone is measured respectively to arrive with the reference microphone The range difference of sound source；

According to measured range difference, each Mike in N number of microphone in addition to the reference microphone is calculated separately Fixed skew between wind and the reference microphone；

According to each microphone in N number of microphone in addition to the reference microphone and between the reference microphone Fixed skew calibrates its corresponding phase spectrum difference respectively.

27. equipment according to claim 26, which is characterized in that the processing unit is specifically used for:

According to formulaIt calculates separately in N number of microphone in addition to the reference microphone Fixed skew between each microphone and the reference microphone,

Wherein, Y_i(ω) indicates the frequency spectrum of i-th of microphone, Y₁(ω) indicates that the frequency spectrum of reference microphone, ω indicate frequency, d_i Indicate the range difference of i-th of microphone and reference microphone to sound source, the c expression velocity of sound, 2 π ω d_i/ c indicate i-th microphone with Fixed skew between reference microphone.

28. the equipment according to any one of claim 24 to 27, which is characterized in that the processing unit is specifically used for:

According to each microphone in N number of microphone in addition to the reference microphone and between the reference microphone Power spectrum difference assesses the amplitude coincidence between corresponding microphone and the reference microphone.

29. the equipment according to any one of claim 25 to 27, which is characterized in that N number of audio signal is to broadcast Put the signal acquired in the environment of swept-frequency signal data.

30. equipment according to claim 28, which is characterized in that N number of audio signal is to play white Gaussian noise The signal acquired in the environment of data or swept-frequency signal data.

31. the equipment according to claim 29 or 30, which is characterized in that the swept-frequency signal is Linear chirp, right Number swept-frequency signals, linear stepping swept-frequency signal, any one in logarithm stepping swept-frequency signal.

32. the equipment according to any one of claim 24 to 31, which is characterized in that the processing unit is specifically used for:

Each audio signal in N number of audio signal is subjected to framing, obtains K signal frame of equal length, K >=2；

Windowing process is done to each signal frame in the K signal frame, obtains K windowing signal frame；

FFT transform is done to each windowing signal frame in the K windowing signal frame, obtains K echo signal frame；

According to the corresponding K echo signal frame of each audio signal, determine in N number of microphone except the reference The phase spectrum difference and/or power spectrum difference between each microphone and the reference microphone except microphone.

33. equipment according to claim 32, which is characterized in that any two adjacent signals frame in the K signal frame It is overlapped R%, R > 0.

34. equipment according to claim 33, which is characterized in that the R is 25 or 50.

35. the equipment according to any one of claim 32 to 34, which is characterized in that divided i-th of audio signal Frame, K signal frame for obtaining equal length are write as following vector form:

x_i(t)=[x_i,1(t),x_i,2(t),…,x_i,K(t)]^T

Wherein, x_i(t) i-th of audio signal is indicated, K indicates that each microphone collects the totalframes of signal, []^TIndicate vector Or the transposition of matrix.

36. the equipment according to any one of claim 32 to 35, which is characterized in that the processing unit is specifically used for:

According to formulaIt determines in N number of microphone in addition to the reference microphone Each microphone and the reference microphone between phase spectrum difference,

Wherein, imag () expression takes imaginary part, and ln () expression takes natural logrithm,It indicates i-th of microphone and refers to wheat Phase spectrum difference between gram wind,Indicate j-th of echo signal frame of reference microphone,Indicate i-th of wheat J-th of echo signal frame of gram wind,Indicate basic frequency.

37. the equipment according to any one of claim 32 to 36, which is characterized in that the processing unit is specifically used for:

According to the corresponding K echo signal frame of each audio signal, the power spectrum of each audio signal is determined；

According to the power spectrum of each audio signal, determine every in addition to the reference microphone in N number of microphone Power spectrum difference between a microphone and the reference microphone.

38. the equipment according to claim 37, which is characterized in that the processing unit is specifically used for:

According to formulaThe power spectrum of each audio signal is calculated,

Wherein, P_i(ω) indicates the power spectrum of i-th of audio signal, Y_i,j(ω) indicates j-th of target in i-th of audio signal Signal frame, K indicate that each microphone collects the totalframes of signal, and ω indicates frequency.

39. the equipment according to claim 37 or 38, which is characterized in that the processing unit is specifically used for:

According to formula PD_i(ω)=P₁(ω)-P_i(ω) calculates each wheat in N number of microphone in addition to reference microphone Power spectrum difference gram between wind and the reference microphone,

Wherein, PD_i(ω) indicates the power spectrum difference between i-th of microphone and reference microphone, P₁(ω) indicates to refer to Mike The power spectrum of wind, P_i(ω) indicates the power spectrum of i-th of microphone.

40. the equipment according to any one of claim 24 to 39, which is characterized in that the processing unit is specifically used for:

Determine sample frequency F of the N number of microphone when carrying out audio signal sample_sWith FFT points N_fft, broadcast using loudspeaker White Gaussian noise data or swept-frequency signal data are put, N number of microphone is controlled and acquires N number of audio signal, wherein if The data that the loudspeaker is played are swept-frequency signal data, the swept-frequency signal data are equal by M+1 segment length and frequency not Deng signal constitute,

41. equipment according to claim 40, which is characterized in that the processing unit is also used to:

According to formulaThe frequency of every segment signal in the M+1 segment signal is calculated, and

According to formula S_i(t)=sin (2 π f_iT) every segment signal in the M+1 segment signal is calculated,

Wherein, f_iIndicate the frequency of the i-th segment signal, F_sIndicate sample frequency, N_fftIndicate FFT points, S_i(t) i-th section of letter is indicated Number, and S₁(t) length is the integral multiple of cycle T, T=1/f₁。

42. equipment according to claim 41, which is characterized in that the swept-frequency signal data that the loudspeaker is played are write as Following vector form:

S (t)=[S₀(t),S₁(t),…,S_M(t)]^T

Wherein, S (t) indicates the swept-frequency signal data that loudspeaker is played, S_i(t) the i-th segment signal is indicated,[]^TTable Show the transposition of vector or matrix.

43. the equipment according to any one of claim 24 to 42, which is characterized in that N number of microphone acquires respectively To N number of audio signal, wherein the collected audio signal of i-th of microphone is expressed as x_iAnd x (t),_i(t) can be write as it is following to Amount form:

x_i(t)=[x_i,1(t),x_i,2(t),…,x_i,K(t)]^T

Wherein, x_i(t) the collected audio signal of i-th of microphone is indicated, K indicates that each microphone collects total frame of signal Number, []^TIndicate the transposition of vector or matrix.

44. the equipment according to any one of claim 24 to 43, which is characterized in that the acquiring unit is specifically used for:

N number of microphone is placed in test room, loudspeaker, N number of microphone are configured in the test room Positioned at the front of the loudspeaker；

It controls the loudspeaker and plays white Gaussian noise data or swept-frequency signal data, and control N number of microphone point N number of audio signal is not acquired.

45. equipment according to claim 44, which is characterized in that there is noise reduction room environmental in the test room, it is described Loudspeaker is audio-frequency test Special artificial mouth, and the artificial mouth is calibrated with standard microphone before the use.

46. the equipment according to claim 44 or 45, which is characterized in that control the loudspeaker in the processing unit and broadcast Before putting white Gaussian noise data or swept-frequency signal data, the acquiring unit is also used to:

Under quiet environment, N number of microphone is obtained in the first duration T₁First audio data X of interior acquisition₁(n)；

In the environment of playing white Gaussian noise data or swept-frequency signal data, N number of microphone is obtained in the second duration T₂ The second audio data X of interior acquisition₂(n)；

The processing unit is triggered according to formulaSignal to Noise Ratio (SNR) is calculated, and ensures that the SNR is big In first threshold.

47. a kind of device for assessing microphone array consistency characterized by comprising

Memory, for storing program and data；And

Processor, for calling and running the program and data that store in the memory；

Described device is configured as: executing the method as described in any one of claim 1 to 23.

48. a kind of system for assessing microphone array consistency characterized by comprising

Constitute N number of microphone of microphone array, N >=2；

At least one audio-source；

Device, including the memory for storing program and data and for calling and running the program stored in the memory With the processor of data, described device is configured as:

Execute the method as described in any one of claim 1 to 23.