CN118509772A - Chirp signal equalization optimization method for progressive filter parameter adjustment - Google Patents

Abstract

The invention relates to the technical field of audio signal processing, in particular to a Chirp signal equalization optimization method for adjusting parameters of a progressive filter, which comprises the following steps: playing the Chirp signal by using a loudspeaker, and using a microphone to collect the response of the Chirp signal emitted by the loudspeaker to obtain a response signal; performing fast Fourier transform on the response signals to determine a frequency segment to be equalized; processing the frequency band to be equalized by adopting a filter according to the filter parameters, and acquiring and analyzing a loudspeaker response signal in real time by adopting a microphone to correct the filter parameters; and verifying the effect of the adjusted filter parameters by using a simulation tool. The method for combining the gradual filter parameter adjustment with the Chirp signal and the simulation verification adopts the gradual filter parameter application and dynamic gain adjustment technology, avoids the abrupt change of sound pressure, realizes smooth transition, and ensures stable sound quality and high fidelity of the signal.

Description

Chirp signal equalization optimization method based on progressive filter parameter adjustment

Technical Field

The present invention relates to the technical field of audio signal processing, and in particular to a Chirp signal equalization optimization method for progressive filter parameter adjustment.

Background Art

In the design and implementation of modern audio test systems, frequency response equalization optimization plays a vital role, which is directly related to the purity of the final output sound quality and the performance of the overall system. The core goal of this technical link is to eliminate or reduce the frequency response nonlinearity caused by hardware limitations, environmental factors, etc., so as to ensure that the audio signal can maintain consistent and ideal playback effects in the full frequency range.

Although the traditional equalization optimization strategy has certain practicality and convenience, its basic principle is to analyze the actual frequency response curve of the measured audio equipment (such as speakers) and compare it with the expected ideal sound pressure response curve. Once the deviation is found, the voltage value that needs to be compensated is directly calculated based on these differences, and then these compensation values are added to the signal of the corresponding frequency band in the signal processing stage to achieve the correction of the frequency response. The advantage of this method is that it is intuitive to operate and simple to implement, and it can make preliminary corrections to the uneven frequency response relatively quickly.

However, this direct and mechanical compensation method also exposes some significant problems. First, because the compensation process relies too much on the direct conversion of the original difference, it often ignores the continuity and dynamic characteristics of the sound signal at the physical level, resulting in over-compensation or under-compensation at certain frequency points, causing a sudden change in the sound pressure level. This sudden change will not only cause audible noise or distortion when the speaker is playing, affecting the naturalness and clarity of the sound quality, but may also cause the speaker to work in an unstable state, increase power consumption, and shorten its service life.

Summary of the invention

The purpose of the present invention is to provide a Chirp signal equalization optimization method for progressive filter parameter adjustment, aiming to adopt progressive filter parameter application and dynamic gain adjustment technology to avoid sudden changes in sound pressure, achieve smooth transition, and ensure stable sound quality and high fidelity of signals.

To achieve the above object, the present invention provides a Chirp signal equalization optimization method with progressive filter parameter adjustment, comprising using a loudspeaker to play a Chirp signal, and using a microphone to collect a Chirp signal response emitted by the loudspeaker to obtain a response signal;

Performing fast Fourier transform on the response signal to determine the frequency band that needs to be equalized;

A filter is used to process the frequency band that needs to be equalized according to the filter parameters, and a microphone is used to collect and analyze the speaker response signal in real time to modify the filter parameters;

Use simulation tools to verify the effect of filter parameter adjustment.

The specific steps of using a loudspeaker to play a Chirp signal and using a microphone to collect a Chirp signal response emitted by the loudspeaker to obtain a response signal include:

Determine the working parameters of the Chirp signal;

Based on the working parameters, an audio output device is used to generate a chirp signal to cover the working frequency band of the speaker;

Connect the speaker to the audio output device and play the Chirp signal through the speaker;

Place a microphone in front of the speaker to record the chirp signal response emitted by the speaker and obtain a response signal.

The working parameters include starting frequency, ending frequency, duration and signal amplitude.

The specific step of performing fast Fourier transform on the response signal to determine the frequency band that needs to be equalized includes:

Preprocessing the collected response signal;

Performing fast Fourier transform on the preprocessed response signal to obtain the frequency response curve of the speaker;

Set the frequency response threshold to identify the frequency points that exceed the threshold, mark the key points in the frequency response curve, and determine the frequency band that needs equalization.

The specific steps of preprocessing the collected response signal include:

Use wavelet transform to denoise the signal;

Perform mean normalization on the signal;

Apply the Hanning window function to process the signal.

The specific steps of using a filter to process the frequency band that needs to be equalized according to the filter parameters, and using a microphone to collect and analyze the speaker response signal in real time to correct the filter parameters include:

S601 applies the initial adjustment parameters of the filter to the frequency band that needs to be equalized;

S602 transmits a Chirp signal after applying the initial adjustment parameters, and uses a microphone to collect and analyze the speaker response in real time;

S603 uses a monitoring system to collect speaker responses in real time and dynamically adjusts filter parameters based on real-time data feedback;

S604 dynamically adjusts the input signal gain and uses a transition function to smooth the parameter adjustment;

After the adjustment parameters are modified in S605, steps S601 to S604 are repeated, and the frequency response curve is recorded after each iteration, and the adjustment effect is evaluated.

The Chirp signal equalization optimization method for progressive filter parameter adjustment of the present invention generates a Chirp signal through professional audio test software, which is a signal with linear frequency changes and can cover the entire working frequency band of the speaker in a short time. This signal is particularly effective in revealing the nonlinearity and frequency characteristics in the system response. Subsequently, the Chirp signal is played, and the wide spectrum characteristics contained in it can fully excite the characteristics of the speaker and the surrounding acoustic environment. A high-precision microphone is used to collect the response of the Chirp signal emitted by the speaker at a close distance. This response includes the speaker's own characteristics, room acoustic effects and any non-ideal factors that may exist. The collected signal is digitized and stored for in-depth analysis. The collected response signal is imported into the signal processing software and converted from the time domain to the frequency domain using the fast Fourier transform. Through FFT analysis, the response amplitude and phase characteristics of the speaker at each frequency point can be intuitively observed, so as to accurately identify the frequency bands that need to be equalized, which are usually the peaks and valleys of the response curve or places that deviate from the target response. Based on the FFT analysis results, a suitable filter type (such as low pass, high pass, band pass or -notch, etc.) is selected and the filter parameters are preliminarily set. Then, the previously identified frequency bands are processed according to the preset parameters. This process is not achieved overnight, but through repeated iterations, the filter's cutoff frequency, slope, gain and other parameters are gradually adjusted. After each adjustment, the microphone is used again to collect the speaker's response signal in real time and analyze it immediately to provide real-time feedback on the adjustment effect. Using high-speed data processing technology, the changes in the speaker response after filtering are monitored in real time. By comparing the frequency response curves before and after processing, the effect of the filter parameter adjustment can be quickly determined, and subtle corrections can be made accordingly. After multiple rounds of iterative adjustments, when the filter parameters reach a relatively satisfactory state, a high-precision simulation tool is used to verify the speaker performance under the current parameter settings. In summary, the progressive filter parameter adjustment combines the Chirp signal with the simulation verification method, and uses the progressive filter parameter application and dynamic gain adjustment technology to avoid sudden changes in sound pressure, achieve smooth transition, and ensure stable sound quality and high fidelity of the signal.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

FIG. 1 is a flow chart of a Chirp signal equalization optimization method with progressive filter parameter adjustment according to a first embodiment of the present invention.

FIG. 2 is a flow chart of the first embodiment of the present invention, which uses a Chirp signal to be played and a microphone to collect a Chirp signal response emitted by a speaker to obtain a response signal.

FIG. 3 is a flow chart of performing fast Fourier transform on a response signal to determine a frequency band that needs equalization according to the first embodiment of the present invention.

FIG. 4 is a flow chart of preprocessing the collected response signal according to the first embodiment of the present invention.

5 is a flow chart of the first embodiment of the present invention, which uses a filter to process a frequency band that needs equalization according to filter parameters, and uses a microphone to collect and analyze a speaker response signal in real time to modify the filter parameters.

FIG. 6 is a diagram showing the connection of devices for acquiring a played Chirp signal according to the first embodiment of the present invention.

DETAILED DESCRIPTION

Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

First embodiment:

Referring to FIG. 1 to FIG. 6 , the present invention provides a Chirp signal equalization optimization method for progressive filter parameter adjustment, comprising:

S101 uses a speaker to play a Chirp signal, and uses a microphone to collect the Chirp signal response emitted by the speaker to obtain a response signal;

The specific steps include:

S201 determines the working parameters of the Chirp signal;

Determine the starting frequency of the Chirp signal and stop frequency . Set the duration of the Chirp signal Sum signal amplitude .

In this step, the first task is to carefully set the parameters of the Chirp signal, which are directly related to the effectiveness and pertinence of the test. The core parameters include but are not limited to the starting frequency (marking the lowest frequency of the Chirp signal), the ending frequency (representing the highest frequency that the signal can reach), the duration (determines the fineness of the signal coverage of the spectrum) and the signal amplitude (affects the strength of the signal and the sensitivity of the test). The setting of these parameters needs to be based on the technical specifications of the speaker and the expected test objectives to ensure that the Chirp signal can fully and accurately stimulate and examine the full-band performance of the speaker.

S202 generates a Chirp signal using an audio output device based on the working parameters to cover the working frequency band of the speaker;

According to the previously defined parameters, use professional audio generation software or hardware equipment to create a Chirp signal. The signal is carefully designed to cover the working frequency band declared by the speaker to ensure the comprehensiveness of the test. This step requires a high degree of precision to generate a distortion-free, compliant Chirp signal. Its waveform is as follows:

.

S203 connects the speaker to the audio output device and plays the Chirp signal through the speaker;

Connect the audio output device to the speaker through physical connection or wireless connection. Once the connection is successful, the chirp signal is transmitted to the speaker and played out, starting the actual test of the speaker's performance.

S204 places a microphone in front of the speaker to record the chirp signal response emitted by the speaker to obtain a response signal.

Under the premise of ensuring that the microphone has high enough sensitivity and accuracy, it is placed in the ideal position in front of the speaker. The purpose of this is to most effectively capture the response of the chirp signal radiated from the speaker after it propagates through the air. This response contains information such as the speaker's frequency response, distortion, and directivity. The recorded response signal will become the basis for subsequent data analysis to evaluate whether the actual performance of the speaker meets the design standards or meets specific application requirements.

S102 performs fast Fourier transform on the response signal to determine the frequency band that needs to be equalized;

The specific steps include:

S301 pre-processes the collected response signal;

The specific steps include:

S401 uses wavelet transform to denoise the signal;

Wavelet transform is a time-frequency localized analysis method that can analyze signal characteristics at different scales. It is particularly suitable for processing signals containing sudden or non-stationary noise. By selecting the appropriate wavelet basis and decomposition layer number, the noise components in the signal can be effectively separated and removed, retaining the true characteristics of the signal.

S402 performs mean normalization processing on the signal;

This step aims to remove the DC offset of the signal and adjust the signal to a common reference level. By calculating the average value of the signal and subtracting this average value from each sample point, the numerical center of the signal can be located at zero, which is convenient for subsequent processing and comparison. Mean normalization helps improve the algorithm's adaptability to the dynamic range of the signal, especially in scenarios where the signal strength varies greatly.

S403 applies a Hanning window function to process the signal.

Window functions are used in signal processing to reduce spectral leakage, especially when the signal is truncated before FFT. The Hanning window is a commonly used window function. Because of its relatively good sidelobe attenuation and moderate mainlobe width, it can effectively suppress the sidelobe leakage effect while maintaining spectral resolution. By applying a Hanning window to both ends of the signal, the signal can be smoothly transitioned to zero, reducing the spectral distortion caused by directly truncating the signal, thereby obtaining a more accurate spectrum estimate.

S302 performs fast Fourier transform on the preprocessed response signal to obtain a frequency response curve of the speaker;

The time domain signal is converted to the frequency domain through mathematical transformation, which intuitively shows the response strength of the speaker at different frequencies. The frequency response curve generated in this step is an important indicator for evaluating the performance of the speaker. It can show the gain or attenuation characteristics of the speaker in the entire audible frequency range (usually 20Hz to 20kHz). Using FFT, we can clearly observe the fluctuations of the speaker output at various frequency points, which is extremely important for subsequent equalization processing.

The FFT is calculated as Record the frequency response curve and identify the main frequency components.

S303 sets a threshold of the frequency response to identify frequency points exceeding the threshold, marks key points in the frequency response curve, and determines a frequency band that needs to be equalized.

Set frequency response thresholds: Set one or more reference thresholds based on the design criteria of the speaker, the application environment requirements, or the preferences of the audience. These thresholds are usually used to distinguish between the "acceptable" frequency response range and the area that needs correction. For example, if the goal is to achieve a flat frequency response, the threshold may be set to a fixed deviation value, such as ±3dB.

Identify frequency points that exceed the threshold: By comparing each data point on the frequency response curve with the preset threshold, identify those frequency points that deviate too much from the ideal response curve. These points represent the excessive enhancement or reduction of the speaker at a specific frequency, which is the main factor causing distortion or imbalance in sound quality.

Mark the key points in the frequency response curve: Mark the identified abnormal frequency points, which are the focus of equalization processing. Compare the EQ segment to be balanced with the frequency spectrum, and save and record the data of the frequency range in the EQ segment to be balanced.

Determine the frequency band that needs to be equalized: Based on the marked key points, determine the specific frequency range. The frequency response within these ranges will be adjusted by the equalizer for gain or attenuation to improve the uniformity of the overall frequency response and enhance the sound quality. The goal of equalization is to make the final frequency response curve as close to the ideal flat state as possible, or to make artistic adjustments based on specific needs, such as increasing the fullness of the bass or the clarity of the treble.

S103 uses a filter to process the frequency band that needs to be equalized according to the filter parameters, and uses a microphone to collect and analyze the speaker response signal in real time to modify the filter parameters;

The specific steps include:

S601 applies the initial adjustment parameters of the filter to the frequency band that needs to be equalized;

First, based on the previous analysis or preset standards, the basic configuration parameters of the filter are determined, such as cutoff frequency, gain value and quality factor (Q value), which correspond to the uneven areas identified in the speaker response or the specific frequency bands that need to be enhanced/attenuated. These initial parameters are loaded into the filter module in the digital signal processor (DSP) for preliminary equalization processing for specific frequency bands.

S602 transmits a Chirp signal after applying the initial adjustment parameters, and uses a microphone to collect and analyze the speaker response in real time;

Next, the new chirp signal with the initial equalization settings embedded in it is played again through the audio output device. At this point, the microphone in the system not only captures the signal from the speaker, but also uses real-time signal processing technology to immediately perform spectrum analysis on the collected response signal to intuitively reflect the frequency response characteristics under the current equalization settings.

S603 uses a monitoring system to collect speaker responses in real time and dynamically adjusts filter parameters based on real-time data feedback;

When applying the filter parameters to the original signal in the initial stage, a very small amplitude is used, usually 10% or less of the filter parameters. The initial parameter calculation , Apply the adjusted filter parameters to signal processing.

S604 dynamically adjusts the input signal gain and uses a transition function to smooth the parameter adjustment;

Use the monitoring system to collect frequency response data in real time, dynamically adjust the filter parameters and input signal gain according to the real-time data feedback, ensure the output voltage is stable, and calculate the gain. . is the initial gain value, is the transition function.

In order to avoid sound quality fluctuations or transient distortion caused by sudden changes in filter parameters, a smooth transition processing mechanism is introduced. Mathematical transition functions (such as exponential decay, window function, etc.) are used to gradually apply changes between new and old parameters to ensure the continuity and stability of sound quality during the adjustment process. The transition function is gradually applied during the adjustment process to ensure a smooth transition. The transition function is .

Where t is the current time or the number of iterations; T is the total transition time or the total number of iterations.

After the adjustment parameters are modified in S605, steps S601 to S604 are repeated, and the frequency response curve is recorded after each iteration, and the adjustment effect is evaluated.

Calculate the step size of each parameter adjustment, gradually increase the filter parameters, and approach the target value. Calculate the parameter adjustment step size: The filter parameters are increased gradually, and the signal is retransmitted after each adjustment and the response is detected, where is the target filter parameter value, is the current filter parameter value, To adjust the number of steps.

Based on the parameters adjusted in the above steps, the system re-enters the next cycle, applies the updated filter settings again, and continues to monitor and optimize. After each iteration, the system records the current frequency response curve, compares it with the previous curve, and intuitively evaluates the improvement effect of the equalization processing. This iterative process continues until the predetermined performance indicators or optimization standards are achieved, ensuring that the output frequency response of the final speaker is highly balanced and optimized.

Use the Chebyshev digital filter design method to calculate the filter coefficients, implement the recursive structure or non-recursive structure of the filter, and use the formula to gradually adjust the filter coefficients.

The recursive structure is represented as ,

The non-recursive structure is represented by ,

The iterative adjustment is expressed as ,

in is the filter output, is the filter input, and are the filter coefficients, and are the order of the filter, are the current filter coefficients, are the updated filter coefficients, is the adjustment amount.

S104 uses a simulation tool to verify the effect of the adjusted filter parameters.

Use simulation tools to verify the effect of filter parameter adjustment to ensure smooth transition of frequency response without obvious mutations.

What is disclosed above is only a preferred embodiment of the present invention, and it certainly cannot be used to limit the scope of rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiment and equivalent changes made according to the claims of the present invention still fall within the scope of the invention.

Claims (6)

1. A Chirp signal equalization optimization method for adjusting parameters of a progressive filter is characterized in that,

Comprising the following steps: playing the Chirp signal by using a loudspeaker, and using a microphone to collect the response of the Chirp signal emitted by the loudspeaker to obtain a response signal;

performing fast Fourier transform on the response signals to determine a frequency segment to be equalized;

Processing the frequency band to be equalized by adopting a filter according to the filter parameters, and acquiring and analyzing a loudspeaker response signal in real time by adopting a microphone to correct the filter parameters;

and verifying the effect of the adjusted filter parameters by using a simulation tool.

2. The method for Chirp signal equalization optimization with progressive filter parameter adjustment of claim 1,

The specific steps of playing the Chirp signal by using a loudspeaker and acquiring the response of the Chirp signal emitted by the loudspeaker by using a microphone to obtain a response signal include:

determining working parameters of a Chirp signal;

Generating a Chirp signal by adopting audio output equipment based on the working parameters so as to cover the working frequency band of the loudspeaker;

connecting a loudspeaker with the audio output equipment, and playing a Chirp signal through the loudspeaker;

The microphone is placed in front of the loudspeaker to record the response of the Chirp signal emitted by the loudspeaker, and a response signal is obtained.

3. The method for Chirp signal equalization optimization with progressive filter parameter adjustment of claim 2,

The operating parameters include a start frequency, an end frequency, a duration, and a signal amplitude.

4. The method for Chirp signal equalization optimization with progressive filter parameter adjustment of claim 3,

The specific step of performing fast fourier transform on the response signal to determine the frequency band to be equalized includes:

Preprocessing the collected response signals;

performing fast Fourier transform on the preprocessed response signals to obtain a frequency response curve of the loudspeaker;

Setting a threshold value of the frequency response to identify frequency points exceeding the threshold value, marking key points in a frequency response curve, and determining a frequency segment needing to be balanced.

5. The method for Chirp signal equalization optimization with progressive filter parameter adjustment of claim 4,

The specific steps of preprocessing the collected response signals comprise:

denoising the signal by using wavelet transform;

carrying out mean value normalization processing on the signals;

The signal is processed using a hanning window function.

6. The method for Chirp signal equalization optimization with progressive filter parameter adjustment of claim 5,

The specific steps of processing the frequency band to be equalized by adopting a filter according to the filter parameters, and acquiring and analyzing the loudspeaker response signals in real time by adopting a microphone to correct the filter parameters include:

s601, applying initial adjustment parameters of a filter to a frequency band to be balanced;

s602, transmitting a Chirp signal after initial adjustment parameters are applied, and acquiring and analyzing loudspeaker responses in real time by using a microphone;

S603, acquiring loudspeaker response in real time by using a monitoring system, and dynamically adjusting filter parameters according to real-time data feedback;

s604, dynamically adjusting the gain of an input signal, and smoothing parameter adjustment by using a transition function;

And S605, after modifying the adjustment parameters, repeating the steps S601-S604, recording a frequency response curve after each iteration, and evaluating the adjustment effect.