EP1344198B1 - Method and arrangement for processing noise signals from a noise source - Google Patents

Description

The invention relates to a method for processing noise signals a noise source, e.g. a moving one Vehicle, a workshop, in a room, e.g. in the neighborhood. Moreover, the invention relates to an arrangement for processing noise signals from a noise source.

To comply with legal noise limits, e.g. when operating a machine in a workshop, when starting and landing of aircraft, in noise critical zones, e.g. in Residential areas or when passing by vehicles, are object-side Measures for noise reduction known, which the environment, machine, aircraft or Reduce traffic noise and consequently the working environment, living environment and to improve ride comfort. For example for sound reduction of objects, e.g. from street or Rail vehicles, aircraft or machinery, low noise Exhaust and intake systems, largely resonance-free engines or sound-absorbing bodies known. Disadvantageous is that the object-side measures for noise reduction and as a result, the lowering of the noise level limited are. The noise level influencing measures or Environmental conditions, such as low noise road or meteorological Environmental conditions are currently being considered to the observance of the noise limits only rudimentarily taken into account.

In addition, usually stationary, passive measuring devices for recording and monitoring immission values, such as. of benzene, soot limits. In doing so, if necessary also the sound immission value occurring at this location of the measuring device measured. Such a passive, location-based Sound immission measurement is not for identification and classification of the noise level generating Sound sources suitable. In addition, over the object-side Measures going beyond noise reduction not possible.

In US 4806931 a system for the detection and detection of Siren sounds of special vehicles described. Traffic lights become dependent on directional information the sounds are controlled, the direction information recorded via directional microphones.

In the system described in US 5619616 is based on the noise of a vehicle using fuzzy logic and neural networks the vehicle class of the vehicle determined.

JP 09-167296 A discloses a method of measuring traffic flow described at the sounds of vehicles over Microphones are detected and noise parameters are determined.

The object of the invention is therefore to provide a method for processing from noise signals from a noise source specify that one of the most simple and secure Noise source caused noise emission or noise emission is detected and determined. In addition, one is for Specify implementation of the method particularly suitable arrangement.

The first object is achieved by Method for processing noise signals of a noise source, in which several noise signals are detected locally, by means of a sound analysis based on signal characteristics examined and the noise source underlying parameters be determined. By such a common detection several noise signals and their local and / or temporal Analysis is a location, identification, classification and evaluation of the noise signal generating noise source allows. The noise signals are preferred detected at the same time. In doing so, the procedure can be both closed Spaces, as well as outdoors are used. Thus is an identification of critical noises in the Outdoors, e.g. from a loud bang, or from time to time fluctuating noises in a room, which e.g. on one Function or operation error or a load of a rotating Machine in a machine hall point, allows. Using suitable measuring sensors and faster Signal processing for monitoring of ongoing machinery, such as engines or turbines, can be based on the sound analysis Information on possible malfunctions won become. By the sound analysis of the signal characteristics of the detected Noise signals and resulting from the determination parameters of the sound or noise signals causing noise source is a documentation of temporal and / or local behavior of the noise source allows. Alternatively or additionally, based on the determined Noise signals and the determined parameters of underlying noise source Measures to reduce noise or noise reduction, e.g. can be noise reducing Regulatory and / or control measures be performed directly at the noise source.

The invention is based on the consideration that for compliance outdoor noise limits, e.g. in residential areas or near hospitals, or in closed Spaces, e.g. in factory or machine halls, in this Environment detected and monitored sound immission should be. It should not just the sound immission value be detected as local size. Rather, it should be this Sound immission values based sound or noise source determined, located, classified and evaluated. These are advantageously as signal characteristics of or each detected noise signal whose amplitude, frequency and / or phase determined and analyzed. For example based on a level or amplitude comparison of the different ones Location-based noise signals a location-based evaluation the noise source causing these noise signals allows. For example, from three locally different Places detected noise signals with temporally significant Characteristics is determined by transit time measurement and triangulation the noise source is located. In addition, based on the Amplitude or the sound intensity level of the respective noise signals Conclusions about the sound power of the noise source possible.

Conveniently, the sound analysis is based on a time, Frequency and / or level analysis performed. This is for example for a characterizing the respective noise signal Sound spectrum the dependence of the sound pressure level from the frequency by frequency analysis, e.g. one Fast Fourier Transformation (FFT for short) determined. Based on the fast Fourier transformation may be preferred Sample rate, block length or interval of the underlying Sound or noise signal can be determined.

Advantageously, the parameters of the noise source whose Type, position and / or condition determined. This is for example on the basis of the sound analysis determined Features of the noise signal, e.g. of the interval, or based the combination of several noise signals the type of noise source, e.g. a siren of an emergency vehicle, or the position of the noise source determined. As condition the noise source is determined by the sound analysis of the noise signals a movement of the noise source or an operating condition the noise source determined.

When a noise limit is exceeded expediently carried out the sound analysis. This is a differentiated sound analysis allows. For example a sound analysis of the measured noise signals and from it resulting in a location, identification, classification and Assessment of the underlying noise source when in existence of critical noises, e.g. in a detonation bang outdoors or at a bang caused by a traffic accident, or a time-varying Noise, which indicates a troubled running of a rotating Machine indicates. Preferably, for as a result a sound limit exceeded noise analysis a PRE trigger to keep a temporary Ring buffer used.

Conveniently, at least one of the signal characteristics the noise signal stored in the form of a noise pattern. For example, the frequency spectrum or the level spectrum of repetitive noise signals for a later identification or identification of the same future noise signals in the form of patterns deposited. For a particularly fast identification and / or classification the noise signals and thus the noise source becomes at least one of the signal characteristics of the noise signal compared with stored noise patterns. This is a particularly simple and fast determination and assignment of parameters of the underlying noise source allows.

Alternatively or additionally, preferably external data, especially meteorological data, optical data, time data, Time data, in the sound analysis of the detected noise signals considered. This allows possible interference signals, such as. of rain noise, gathered from the outdoors Noise signals are eliminated. In addition, you can the stored signal characteristics, noise signals or Noise patterns associated with the acquired time data, in particular time data, for evaluations, e.g. Statistics, be used. Thus, the quality of identification the underlying noise source improved. Furthermore, long-term considerations of local noise immissions outdoors or in a room allows.

In addition, preferably optical data, e.g. on Image of an object with its surroundings or an image of a Raums, grasped. Based on the optical data, possible absorption or reflection points identified and at the Sound analysis to be considered. Furthermore, through the from the image obtained data of the noise source and the resulting derivable parameters, such as type, shape, dimension and / or Condition, e.g. Movement, to check the plausibility of the acoustic detected noise signals and the determined therefrom Signal characteristics and parameters of the noise source used become. Thus, a particularly secure identification and Classification of the noise source or the object or the Event allows.

For determining and classifying the signal characteristics of the noise signal and / or the parameter of the noise source advantageously used a self-learning system. As self-learning system become different forms of artificial Intelligence, e.g. neural networks, fuzzy logic and / or expert systems used. This is a consideration of blurred values, e.g. of "loud" or "less noisy" rainfall sounds. About that In addition, such systems are also for classification can be used, e.g. for a consideration of age-related Changes in the parameters of the identified noise source. Alternatively or additionally, the deposited Noise patterns using neural networks based on the current or currently detected noise signals of the identified noise source customized.

Advantageously, the signal characteristics and / or the parameters a control and / or a control system, a Information system and / or fed to an alarm system. The use of the detected noise signals and / or the determined Parameters of the noise source e.g. as Stell- or Nominal value in a control and / or regulation system, e.g. a load control of a passing vehicle, which a driving noise exceeding the noise limits caused, allows a limitation or reduction the noise level.

The second object is achieved by an arrangement with a plurality of noise sensors for location-based detection of noise signals and with a central data processing unit for sound analysis of the noise signals based on at least one signal feature and for Determination of at least one characterizing the noise source Parameter. By such use of a Plurality of noise sensors used for location-based detection in different places outdoors or in a closed Space are arranged by their logical Linkage and the sound analysis of the detected noise signals a location, identification, classification and rating of noise sources, e.g. passing vehicle or ongoing turbine of a power plant, or events, e.g. on Brake noise, a bang, allows. As a data processing unit For example, serves a central personal computer or another programmable controller.

Appropriately, as noise sensors are directional microphones intended. Depending on the type and design of the arrangement Various directional microphones provided. For example for a directional assignment of the noise source several Directional microphones, e.g. in all four Cardinal directions, largely in one place in the room or in the Free and arranged a microphone with omnidirectional characteristics. Depending on the type and design acoustic acoustic sensors or noise sensors distributed in different places in the Room or be arranged outdoors. The noise sensors are for the spatial and / or temporal assignment of the noise source with the central data processing unit by means of Data transmission units connected. Alternatively or In addition, airborne transducers, structure-borne sound transducers Capture of object or position related acoustic Signals provided.

The data processing unit preferably comprises a means for determining the amplitude, frequency and / or phase of or every sound signal. In this case, the means, in particular for determining the amplitude, phase or frequency spectrum the noise signals. This is for example by the determined pulse sequence of the sound or noise signals one beyond the usual sound detection Identification, classification and evaluation of the noise signals generating noise source possible. For example are sudden energy releases, such as these e.g. occur due to mechanical deformations in an accident, identified by the characterizing sound pulses and classifiable.

Conveniently, a means for determining type, position and / or state of the noise source. To the means comprises a sound analysis module, for an amplitude, Frequency and / or phase analysis, on. For example The sound analysis module is used to analyze the noise amplitude and the sound sequence, in particular the sound pulse sequence. Depending on the logical link of the Noise signals which are detected at different locations and based on the amplitude analysis, for example, the Insonification of the characterizing a single noise source Noise signals and the position of the noise source determined.

In a further advantageous embodiment, the data processing unit comprises a means of monitoring a noise limit. By such monitoring, e.g. one maximum permissible noise limit by at least one the detected noise signals for a monitored area or a room to be monitored is an event-driven one Sound analysis allows. Alternatively, depending on the type and Execution of the data processing unit a permanent Sound analysis for the area or area concerned Space to be executed.

Preferably, a data storage for the deposit is at least one of the signal characteristics of the noise signal in the form of a Noise pattern provided. For example, the as stationary, cyclic or transiently identified and / or localized noise sources by their associated frequency, Amplitude and / or phase spectra of the noise signals, e.g. whose sound pulse sequences, characterized, which be deposited in the form of patterns. This includes the data processing unit expediently a database with a Noise pattern library. At the same time, the database becomes continuous by currently detected and characteristic noise signals and their associated spectra are updated and added.

In a preferred embodiment, a means for comparison at least one of the signal characteristics of the noise signal with stored noise patterns for determination and assignment of parameters of the underlying noise source intended. By such a comparison block is a particularly easy and fast identification of sound sequences characterizing respective noise sources and thus a quick identification of the noise source allows. In a preferred embodiment, an agent intended to analyze the parameters which the parameters Based on the sound analysis in several iteration steps for Detection of significant aspects or patterns within of a sound, such as of significant frequency patterns.

In addition, preferably, an optical system for detection provided by optical data. The taking of a picture the environment of the distributed noise sensors in a room or outdoors based on the optical system allows a supplementary determination of the noise source or a Plausibility check to the identified by the sound analysis Noise source. In addition, absorption or reflective surfaces identified and in the sound analysis the noise signals are taken into account. Further is in a room to be monitored, e.g. a workshop, one Room or building security, i. a burglar alarm, both optically and acoustically enabled.

For consideration of the noise signals influencing Data is preferably a capture unit for capture provided by meteorological data. Hereby are e.g. heavy rain or hail noise from the noise signals Eliminable in the sound analysis. Preferably is also a means of identification and classification the signal characteristics of the noise signal and / or the parameter of the noise source based on a self-learning Systems provided. Here are different forms of artificial intelligence, e.g. neural networks and / or fuzzy logic, used. The identification, localization and Classification of the noise signals and / or the underlying Noise source in stationary, cyclic or transient takes place on the basis of fuzzy values and their logical Connections.

Advantageously, an external control and / or regulation system intended. Through the sound analysis executed detection and evaluation of the noise signals can For example, external safety systems are activated become. Alternatively, the noise signals for noise-reducing Control and / or regulating systems are used.

The advantages achieved by the invention are in particular in that for a permanent monitoring of sound and Noise emissions and for a secure identification of noise-causing noise sources, objects or events recorded several noise signals location-related and such analyzed by means of a sound analysis based on signal characteristics be that at least one of the noise source underlying Parameter is determined. By such a determination a parameter of the noise-emitting noise source, e.g. a humming sound of a rotating machine in one Engine hangar or a bang by a traffic accident is an application of the arrangement both in closed rooms, e.g. in factories or production halls, or in the surrounding area, e.g. along a highway, given. Here are based the recorded data statements about the stationary, cyclic or transient behavior of noise sources in particular simple way allows.

Fig 1

schematisch eine Anordnung zur Verarbeitung von Geräuschsignalen mit mehreren Geräuschsensoren und einer zentralen Datenverarbeitungseinheit,

Fig 2

ein Diagramm für ein erstes Geräuschmuster,

Fig 3

ein Diagramm für ein zweites Geräuschmuster,

Fig 4

ein Diagramm für ein drittes Geräuschmuster,

Fig 5

ein Diagramm für ein viertes Geräuschmuster,

Fig 6

schematisch eine Alternative für die Anordnung gemäß Figur 1,

Fig 7

schematisch eine weitere Alternative für die Anordnung gemäß Figur 1, und

Fig 8

schematisch eine weitere Alternative für die Anordnung gemäß Figur 1.

Embodiments of the invention will be explained in more detail with reference to a drawing. Show:

Fig. 1

schematically an arrangement for processing noise signals with a plurality of noise sensors and a central data processing unit,

Fig. 2

a diagram for a first noise pattern,

Fig. 3

a diagram for a second noise pattern,

Fig. 4

a diagram for a third noise pattern,

Fig. 5

a diagram for a fourth noise pattern,

Fig. 6

schematically an alternative for the arrangement of Figure 1,

Fig. 7

schematically another alternative for the arrangement of Figure 1, and

Fig. 8

schematically another alternative for the arrangement of Figure 1.

Corresponding parts are in all figures with the provided the same reference numerals.

FIG. 1 shows an arrangement 1 for processing noise signals SQ1 to SQ3 a noise source G1, G2 or G3. For location-based detection of the noise signals SQ1 to SQ3 are a plurality of noise sensors M1 to M7 at different ones Places arranged outdoors. For example, is to location-based detection of mimicking in a residential area 2 Noise of the sound sensor M6 provided. To capture from noise signals SQ2 or SQ1, caused by the noise source G2, e.g. an industrial plant 4, or by the noise source G1, e.g. a fan of an air conditioner in one 6 shopping mall, caused is the noise sensor M7 or M5 provided. For a driving on a roadway 8 Motorcycles 10 are for direct detection of the noise signals SQ3 of the noise source G3, e.g. of the engine, along the Roadway 8 a plurality of noise sensors M1 to M4 arranged.

The noise sensors M1 to M7 are not closer by one illustrated data transmission unit with a central data processing unit 12 for sound analysis of the means of Noise sensors M1 to M7 detected noise signals SQ1 to SQ3 and to determine parameter P at the moment of Meter reading unknown and unidentified noise source G1 to G3 connected. As a data transmission unit for example, wireless or wired systems, e.g. Radio systems or data bus systems provided. As a data processing unit 12, for example, serves a personal computer an environmental monitoring station monitoring immission values. As noise sensors M1 to M7 are for example directional microphones, acoustic transducers, airborne or structure-borne sound sensors, used.

1. das von der Geräuschquelle G1, einem Lüfter einer Klimaanlage des Einkaufszentrums 6, ausgehende Geräuschsignal SQ1 in unmittelbarer Nähe zum Wohngebiet,

2. das von der Geräuschquelle G2, einem Presswerk der Industrieanlage 4, ausgehende Geräuschsignal SQ2 einige hundert Meter vom Wohngebiet entfernt,

3. das von der Geräuschquelle G3, dem Motorrad 10, ausgehende Geräuschsignal SQ3, und

4. ein von der Fahrbahn 8 einer Umgehungsstraße um das Wohngebiet 2, die einen 180°-Kreisbogen um den Geräuschsensor M6 beschreibt, ausgehendes Geräuschsignal SQ 4 der Geräuschquelle G4. Die zulässige Höchstgeschwindigkeit auf der Umgehungsstraße beträgt beispielsweise 100 km/h.

By means of the noise sensor M6 arranged in the residential area 2, the following noise signals SQ1 to SQ4 of the four noise sources G1 to G4 are detected:

1. the noise signal SQ1 originating from the noise source G1, a fan of an air conditioner of the shopping center 6, in the immediate vicinity of the residential area,

2. the noise signal SQ2 emanating from the noise source G2, a pressing plant of the industrial plant 4, a few hundred meters from the residential area,

3. The sound source G3, the motorcycle 10, outgoing noise signal SQ3, and

4. one of the lane 8 of a bypass around the residential area 2, which describes a 180 ° arc around the noise sensor M6, outgoing noise signal SQ 4 of the noise source G4. For example, the maximum speed allowed on the bypass is 100 km / h.

At the M6 sound sensor or control microphone will be at the time t = 0 receiving noise signals SQ1 to SQ4 by means of a sound analysis, in particular an amplitude, frequency or phase analysis, to determine parameters P of the noise signals SQ1, SQ2, SQ3 or SQ4 generating Sound source G1, G2, G3 and G4, in particular for identification from the noise sources G1 to G4 descriptive noise patterns SM1 to SM4, examined. A noise pattern SM1 to SM4 characterizes characteristic noise levels (or noise levels) over the frequency and the Time of the associated noise sources G1 to G4.

Depending on the type and structure of the data processing unit 12 can the sound analysis of the detected noise signals SQ1 to SQ4 if a permissible or maximum noise limit is exceeded, in particular a limit value for the noise level, and thus depending on predefinable and / or current acoustic or optical conditions become. For example, in one by an optical System 14 captured image which is a critical situation e.g. a traffic accident or an accident in the press plant, e.g. a fire, by a corresponding signal the sound analysis by means of the data processing system 12 be executed. By such an event-driven Sound analysis can be the arrangement 1 for both an acoustic and / or optical location / localization, identification, Classification and / or evaluation of noise signals SQ1 to SQ4 and / or noise sources G1 to G4 are performed. For example, in one due to the means of the optical Systems 14 detected optical data detected a fire become. In combination with the acoustic evaluation of im at the same moment, noise signals detected SQ by means of at least one of the noise sensors M1 to M7 may be one preceding explosion or detonation can be identified.

In Figure 2 is an example of the noise source G1 (= the Fan of the air conditioner) descriptive noise pattern SM1 shown. For example, the fan runs at a constant speed Speed and thereby generates stationary single tones that as Airborne sound and thus noise signals SQ1 are emitted. These noise signals SQ1 are characterized by its speed and the Number of its rotor blades determined. That through the single tones, which are received as noise signals SQ1, resulting Noise pattern SM1 of the fan is in Figure 2 in the form of a Campbell diagram shown. The Campbell chart shows while functions of two variables - here level above Frequency and time. The Campbell diagram for the fan of the Air conditioning of the shopping center is characterized by such characteristic Single tones coined as having fixed frequencies appear over the time constant level in the diagram. she are recognizable as straight lines parallel to the time axis and cut the frequency axis at time t = 0 s at the respective Frequency.

FIG. 3 shows by way of example a description of the noise source G2 Noise pattern SM2 shown. The noise source G2, the industrial plant 4, e.g. a press shop for Sheet metal processing, presses a molding every second. That here generated noise signal SQ2 has a typical pulse character on. The bandwidth of the associated frequency range runs, for example, from 30 Hz to 6800 Hz. The noise pattern SM2 is exemplary in the form of a Campbell diagram shown. The Campbell diagram for the press shop is characterized by the characteristic single pulses or noise signals SQ2 shaped parallel to the frequency axis of 30 Hz to 6800 Hz at intervals of one second. The representing the respective single pulse or the noise signal SQ2 Line describes the frequency-related volume of the single pulse according to texture scaling.

FIG. 4 shows by way of example a noise pattern SM3 describing the noise source G3. The noise source G3, eg a motorcycle 10, bends at walking speed from the residential area 2 into the bypass at the point P1 (see FIG. 1). The motorcycle 10 accelerates with a smooth change in the engine speed of 1000 min ^-1 at time t ₁ = 0 s to 11000 min ^-1 by time t ₂ = 10 s. The dominated by the ignition frequency f engine noise and thus the receiving noise signal SQ3 results z. B. for a 4-cylinder 4-stroke engine as a sweep (= changing sound) with the second engine order (double engine speed) as a frequency. This sweep thus runs of f ₁ = 33.3 Hz (second engine order at 1000 min ^-1) at time t ₁ = 0 s to f ₂ = 366.6 Hz (2nd engine order at 11,000 min ^-1) at time t ₂ = 10 s. The volume of this sweep increases continuously. Due to the circular arrangement of the bypass around the noise sensor M6 or the control microphone in the residential area 2 (see Figure 1), the distance between the moving noise or noise source G3 (ie the motorcycle 10) and the noise sensor (M6) is approximately constant. Thus, no frequency shift occurs after the acoustic Doppler effect. Thus, the noise pattern SM3 shown in FIG. 4 in the form of a Campbell diagram runs linearly for the noise source G3. The Campbell diagram for the motorcycle 10 and thus for the noise source G3 is described by the characteristic curve of the sweep as a result of the ignition frequency change during the acceleration process. This characteristic curve can be recognized as a diagonal line connecting the points P1 (t ₁ = 0 s, f ₁ = 33.3 Hz) and P2 (t ₁ = 10 s, f ₂ = 366.6 Hz). The increase in volume during this speed change is described by texture scaling.

FIG. 5 shows by way of example a further noise pattern SM4 for noise signals SQ6 which are received by means of the noise sensor M6 and which describe a combination of buzzing and striking noises. For this purpose, the motorcycle turns 10 (= noise source G3) with walking speed from the residential area 2 in the bypass (point P1 in Figure 1). The motorcycle 10 accelerates with a smooth change of the engine speed of 1000 min ^-1 at the time t ₁ = 0 s to 11000 min ^-1 at the time t ₂ = 10 s. In the profile of the tire of the motorcycle 10, a stone has been clamped, which hits the asphalt once every revolution of the wheel and thereby generates a pulse of the bandwidth 90 Hz to 5 kHz. This beating noise, together with the changing firing frequency of the high-revving engine, is detected by the M6 noise sensor in residential area 2.

The resulting noise pattern SM4 is shown in Figure 5 in the form of a Campbell diagram. The noise pattern SM4 comprises overlapping the noise source G3charakterisierende noise signals SQ3 and SQ4, ie, the engine and the driving noise. The oblique line between the frequencies f1 and f2 describes the changing firing frequency of the high-revving engine and thus the noise signal SQ3. The lines running parallel to the frequency axis describe the beating noises of the stone on the asphalt and thus the noise signal SQ4. The time interval Δt between two strokes corresponds to one wheel revolution. This decreases from the initial speed (n ₁ = 1000 min ^-1) to the final speed (n ₂ = 11000 min ^-1) of the engine continuously from .DELTA.t ₁ to .DELTA.t. ₂

In operation of the data processing unit 12, the detected Noise signals SQ1 to SQ4 by means of a sound analysis examined on the basis of signal characteristics such that this Underlying noise source G1 to G4 are assigned and the noise source G1 to G4 underlying parameters P, such as Fan in operation or motorcycle 10 drives or stands, be determined. Depending on the type and structure of the data processing unit 12 is the sound analysis in dependence from a noise level detected at the noise sensor M6, the has exceeded a noise limit. The Data processing unit 12 includes for limit value monitoring a corresponding agent, e.g. a corresponding one implemented in software function block.

The sound analysis can be carried out on the basis of various analyzes, eg time, frequency and / or level analyzes. The sound analysis includes algorithms that the respective noise signal SQ1 to SQ4 for characteristic signal characteristics such. B. fixed frequencies (fan), short broadband pulses (press shop) and Sweeps (accelerating motorcycle) examine. Such an algorithm is z. Example, the method described below for identifying characteristic signal characteristics of the noise signals SQ1 to SQ4 an underlying noise pattern SM1 to SM4. The characteristic signal characteristics of the noise pattern SM1 to SM4 are used as identification criteria for the respective noise pattern SM1 to SM4, on the basis of which a comparison with noise patterns SM _a to SM _z stored in a database of the data processing unit 12 and with noise patterns SM1 to 5 detected by the noise sensors M1 to M7 SM4 is done. This comparison makes it possible to assign the sound signals SQ1 to SQ4 detected in the microphone M6 to the source of noise G1 to G4.

For example, the detected noise signals SQ1 to SQ4 of the measuring points or noise sensors M1 to M7 as time data deposited in a ring buffer. When exceeded a threshold or noise limit, z. B. on the microphone M6 in residential area 2, the contents of the ring memory with a predefinable lead time before the occurrence of an excess the noise limit stored. characteristic Noise or signal characteristics are determined by the Sound analysis according to the graphic aspects in the Campbell diagram analyzed. A pixel in the Campbell diagram (depending on Resolution of the Fast Fourier Transform (FFT)) a volume value of an analyzed frequency and time bandwidth within the coverage areas. Graphical connections (compare noise patterns SM1 to SM4 in FIGS. correspond to acoustic signal characteristics based on the database comparison and comparison with other noise signals SQ1 to SQ4 of other noise sensors M1 to M7 (eg. Near-field microphones, directional microphones) or concrete causers Sound sources G1 to G4 are assigned.

A preferred evaluation of detected noise signals SQ1 to SQ4 is e.g. As the fast Fourier transform (called FFT for short) of the microphone signals and the calculation of the so-called A-weighted sound pressure level. The A-weighted sound pressure level is defined as follows: LP = 10log 10 P P 0 2 with P = measured alternating sound pressure P 0 = 2 c -5 P a

Relevant evaluation criteria of the FFT are for example Sample rate (fixed at eg 25 kHz) or block length.

When to identify a noise pattern SM1 to SM4 is to dissolve densely spaced frequencies is to choose a different block length than when the time is close to each other Impulses (according to the principle of noise patterns SM1 to SM4).

A sound or pattern analysis can be several independent Processes include, as evaluation criteria For example, apply different block lengths of the FFT. This exemplary choice of the value of a rating criterion can be from the current process itself or from external specifications depend. Preferably, the data processing unit 12 to a means for analyzing the parameter P using the Sound analysis, the parameter analysis in several iteration steps is done to significant aspects or noise patterns SM1 to SM4 within a detected noise signal Recognize SQ1 to SQ4, e.g. Frequency and Volume of a buzzer, bandwidth, volume and time Distance of a repeated beating noise. there can the evaluation criteria of the sound analysis based on Input variables are changed.

The input signal can be analogous to the acoustic pattern recognition also an optical pattern recognition (over time) done. In addition, an unillustrated optical is in addition System for capturing optical data of the environment or a room provided. Based on the comparison of the analyzes can describe correlations of causes and effects, be evaluated and saved. Another use case can z. B. in a specific detection specification special Operations exist. This can be z. B. the targeted search after high-revving motorcycles or arriving commercial vehicles be whose occurrence from the detected noise signals SQ1 to SQ4 is filtered out. Another application is e.g. given during noise-critical maintenance, if because of the night's sleep under normal weather and traffic conditions this can not be done so can in the case of a loud noise like raining rain or high traffic (due to a detour due to Accident) the noise-critical activity nevertheless admitted become. Depending on the type and design of the data processing unit 12, the consideration of data from external systems, such as. of optical, meteorological or navigation systems, in sound analysis based on input variables, e.g. Limit value overruns, and / or quality characteristics be determined and controlled.

FIG. 6 shows an embodiment of the arrangement 1 for a spatial and temporal assessment of noise sources G1 to G4. The arrangement 1 comprises five noise sensors M1 to M5, which are at a measuring point, e.g. close to each other on one Lamppost on a carriageway or on a carrier in one Factory building, arranged. Four of the five noise sensors M1 to M4 have a horizontal directional characteristic in all four directions. One of the five noise sensors M5 has a vertical directional characteristic, in particular a ball characteristic, on. A the Noise limit exceeding noise signal SQ1 to SQ4 is by means of the noise sensor M5 with omnidirectional characteristic detected. Based on the sound or pattern analysis of the data processing unit 12 becomes at least one signal feature of the noise signal SQ1 to SQ4, e.g. Level, frequency, phase, examined and identified. The sound pattern determined during this process SM1 to SM4 will be using the four directional microphones or noise sensors M1 to M4 receiving noise signals SQ1 to SQ4 compared to equality, thereby using that Noise sensor M1 to M4 with the same noise pattern SM1 to SM4 and the strongest level determines the direction can be.

FIG. 7 shows a further embodiment of the arrangement 1 with several noise sensors M1 to M5. The noise sensors M1 to M5 are microphones with a vertical omnidirectional characteristic on an inspection site of an industrial plant arranged evenly distributed. Alternatively, these can also in a closed room, e.g. in a workshop of the Industrial plant 4, be arranged. Noise signals SQ1 to SQ4 of the same noise source G1 to G4, e.g. the noise signal SQ1 of a passing vehicle 14 or the noise signal SQ2 Industrial Plant 4, is located locally various locations arranged noise sensors M1 to M5 as a function of the traveled sound path and the resulting sound transit time to different Receive times. Based on the given position of the noise sensors M1 to M5 and the determined sound path or sound propagation time for the respective noise sensor M1 to M5 becomes the position of the noise or sound source G1 or G2, i. the vehicle 14 or the industrial plant 4, determined.

Another embodiment of the arrangement 1 is shown in FIG shown. The arrangement 1 comprises six noise sensors M1 to M6. The noise sensors M1 to M6 are as microphones with Ball characteristic executed. The noise sensors M1 to M6 are at different measuring points in the study area arranged. The noise sensors M1 to M4 are along the Roadway 8 arranged. The noise sensor M5 is in close range the industrial plant 4 arranged. The noise sensor M6 is located in the residential area 2. In operation of the arrangement 1, by means of the noise sensor M6 a the noise limit Exceeding noise detected. That this sound signal SQ1 underlying noise pattern SM1 is with the one received by the other noise sensors M1 to M4 Noise patterns SM1 or received by the sound sensor M5 Noise pattern SM2 compared. With agreement of Noise patterns SM1 (M1 to M4) = SM1 (M6) of different Noise sensors M1 to M4 or M6 is an identification and classification of the noise source SQ1 allows. Based A level analysis is also an evaluation of the detected noise signal SQ1 and thus also an evaluation of the noise source G1 given. For example, using a combined Frequency and level analysis, taking into account external Influences or data, such as under elimination of Noise or other noise such as rain, one Statement about the state of the noise source G1, e.g. the Vehicle 14 accelerates or brakes, allows.

Claims (22)

1. Method for processing noise signals (SQ1 to SQ4) from a noise source (G1 to G4),
   whereby several location-related noise signals (SQ1 to SQ4) are detected and examined by means of sound analysis using signal characteristics, and whereby parameters on which the noise source (G1 to G4) is based are determined,
   characterised in that
   the sound analysis is performed if a noise limit value is exceeded.
2. Method according to claim 1,
   characterised in that
   the amplitude, frequency and/or phase of the noise signal (SQ1 to SQ4) are determined as signal characteristics.
3. Method according to claim 1 or 2,
   characterised in that
   the sound analysis is performed using a time, frequency and/or level analysis.
4. Method according to any of claims 1 to 3,
   characterised in that
   the type, position and/or state of the noise source (G1 to G4) are determined as its parameter.
5. Method according to any of claims 1 to 4,
   characterised in that
   the sound analysis performed as a result of the noise limit value being exceeded is used as a trigger for maintaining a temporary ring memory.
6. Method according to any of claims 1 to 5,
   characterised in that
   at least one of the signal characteristics of the noise signal (SQ1 to SQ4) is stored in the form of a noise pattern (SM1 to SM4).
7. Method according to any of claims 1 to 6,
   characterised in that
   at least one of the signal characteristics of the noise signal (SQ1 to SQ4) is compared to stored noise patterns (SM_a to SM_z) for the determination and allocation of parameters of the noise source (G1 to G4) on which the noise signal is based.
8. Method according to any of claims 1 to 7,
   characterised in that
   external data, in particular meteorological data, optical data, time data, operating parameters, are taken into account.
9. Method according to any of claims 1 to 8,
   characterised in that
   a self-learning system is used for the determination and classification of the signal characteristics of the noise signal (SQ1 to SQ4) and/or the parameters of the noise source (G1 to G4).
10. Method according to any of claims 1 to 9,
    characterised in that
    the signal characteristics and/or the parameters are fed to an open loop and/or a closed loop system.
11. Arrangement (1) for processing noise signals (SQ1 to SQ4) from a noise source (G1 to G4),
    whereby a plurality of noise sensors (M1 to M7) for the detection of location-related noise signals (SQ1 to SQ4) and a central data processing unit (12) for the sound analysis of the noise signals (SQ1 to SQ4) using at least one signal characteristic and for the determination of at least one parameter characterising the noise source (G1 to G4) are provided,
    characterised in that
    means for monitoring a noise limit value are provided.
12. Arrangement according to claim 11,
    characterised in that
    directional microphones are provided as noise sensors (M1 to M7).
13. Arrangement according to claim 11 or 12,
    characterised in that
    means for the determination of the amplitude, frequency and/or phase of the noise signal (SQ1 to SQ4) are provided.
14. Arrangement according to any of claims 11 to 13,
    characterised in that
    means for the determination of the type, position and/or state of the noise source (G1 to G4) are provided.
15. Arrangement according to any of claims 11 to 14,
    characterised in that
    a data memory for storing at least one of the signal characteristics of the noise signal (SQ1 to SQ4) in the form of a noise pattern (SM1 to SM4) is provided.
16. Arrangement according to any of claims 11 to 15,
    characterised in that
    a data bank including a noise pattern library is provided.
17. Arrangement according to any of claims 11 to 16,
    characterised in that
    means for comparing at least one of the signal characteristics of the noise signal (SQ1 to SQ4) to stored noise patterns (SM_a to SM_z) for the determination and allocation of parameters of the noise source (G1 to G4) on which the noise signal is based are provided.
18. Arrangement according to any of claims 11 to 17,
    characterised in that
    an optical system for the acquisition of optical data is provided.
19. Arrangement according to any of claims 11 to 18,
    characterised in that
    a recording unit for the acquisition of meteorological data is provided.
20. Arrangement according to any of claims 11 to 19,
    characterised in that
    means for the determination and classification of the signal characteristics of the noise signal (SQ1 to SQ4) and/or the parameters of the noise source (G1 to G4) using a self-learning system are provided.
21. Arrangement according to any of claims 11 to 20,
    characterised in that
    an external open loop or closed loop system is provided.
22. Arrangement according to any of claims 11 to 21,
    characterised in that
    means for the analysis of the parameters (P) using sound analysis in several iterative steps for identifying significant aspects within a noise are provided.

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