CN104794894B - A kind of vehicle whistle noise monitoring arrangement, system and method - Google Patents
A kind of vehicle whistle noise monitoring arrangement, system and method Download PDFInfo
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Abstract
The invention discloses a kind of vehicle whistle noise monitoring system and its method, the noise monitoring system includes blow a whistle Noise Identification module, noise source locating module, photographing module.Noise Identification of blowing a whistle module is less than or greater than the sound wave for noise of blowing a whistle, then detected to determine noise signal of blowing a whistle according to the frequency characteristic of vehicle whistle noise by adding band-pass filter to fall frequency in sound pick-up front end.More auditory localizations of the noise source locating module based on ESPRIT algorithms, the translation invariance for the signal subspace that ESPRIT algorithms are brought using the translation invariance of a kind of array, tried to achieve by constructing special pencil of matrix and generalized eigen decomposition being carried out to it with sound source to corresponding translation operator, to estimate so as to obtain directioin parameter.The noise source that photographing module pair determines seeks a shooting, and carries out violation mark processing, and then the violation vehicle photo of mark is stored or sends relevant traffic administrative department in real time.
Description
Technical Field
The invention belongs to the technical field of audio signal processing, mode recognition and array signal processing, and relates to an automobile whistle monitoring device, system and method.
Background
The accurate and rapid judgment of the coordinate position of the mobile sound source is a core target of the sound positioning technology. At present, a microphone array sound source positioning technology is the mainstream technology, and is a sound positioning method which uses a microphone pick-up array to pick up a voice signal and uses a digital signal processing technology, and has good space selection characteristics. Based on this principle, there are three methods for sound source localization:
the method is derived from high-resolution spectrum estimation technology, such as a minimum variance spectrum estimation method, a eigenvalue solution and the like, and requires that sound source signals have stationarity, and in order to reduce the influence of external interference factors and meet special conditions of the application of the technology, the calculation amount of the system needs to be increased exponentially, so that higher requirements are provided for hardware equipment of the system.
the technology directly controls the microphone to point to the direction of the maximum power beam of the sound source signal by processing the voice signal received by the microphone array.
the method is a wireless positioning technology, and realizes a positioning function by measuring the time difference of sound source signals emitted by a mobile sound source reaching each sound pickup.
Disclosure of Invention
Based on the phenomena of frequent prohibition of motor vehicle whistle and low manual monitoring efficiency in urban population dense areas. The invention aims to solve the phenomenon and provides an automobile whistle noise monitoring system and an automobile whistle noise monitoring method. And detecting the vehicles which violate the whistling on the whistling-prohibited road, and using a road camera to search for points for shooting.
The invention provides an automobile whistling noise monitoring system, which comprises:
the whistle noise identification module is used for identifying the picked sound waves and finally determining a whistle noise signal of the automobile;
the noise source positioning module is used for determining an automobile whistle noise source according to the automobile whistle noise signal identified by the whistle noise identification module;
and the camera module is used for shooting the determined automobile whistling noise source.
The invention also provides a method for monitoring the automobile whistling noise, which comprises the following steps:
identifying the picked sound waves, and finally determining an automobile whistling noise signal;
determining an automobile whistle noise source according to the automobile whistle noise signal identified by the whistle noise identification module;
and shooting the determined automobile whistling noise source.
The invention also provides an automobile whistle noise monitoring system, which comprises:
the whistle noise identification module comprises a plurality of pickup units, wherein the pickup units are respectively used for picking up sound waves of the surrounding environment, identifying the picked sound waves and finally determining a whistle noise signal of the automobile;
a noise source localization module comprising a first microphone array and a second microphone array, the microphone arrays comprising a first microphone array and a second microphone array distributed on an X-axis and a Y-axis, respectively, and the first microphone array and the second microphone array comprising equal-amount microphone elements with uniform intervals; the first microphone array and the second microphone array determine an automobile whistle noise source according to the automobile whistle noise signals identified by the whistle noise identification module;
and the camera module comprises a plurality of cameras which are respectively used for shooting the determined automobile whistling noise source.
And the point searching shooting module of the road camera carries out point searching shooting of the camera according to the three-dimensional point determined by the sound source positioning module. Then, the shot picture is identified, and the identified illegal vehicle picture is stored or transmitted to a traffic management department in real time.
Drawings
FIG. 1 is a schematic block diagram of an automotive whistling noise monitoring system according to the present invention;
FIG. 2 is a system diagram of an automotive whistling sound identification system of the present invention;
FIG. 3 is a schematic diagram of the source distribution of the array of the present invention;
FIG. 4 is an installation diagram of the system device of the present invention.
Detailed Description
In order that the objects, technical solutions and advantages of the present invention will become more apparent, the present invention will be further described in detail with reference to the accompanying drawings in conjunction with the following specific embodiments.
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a block diagram of a car blast noise monitoring system according to the present invention. As shown in fig. 1, the system comprises three modules, namely a whistling noise identification module, a noise source positioning module and a camera module. The automobile whistling noise monitoring system provided by the invention picks up sound source signals by using the microphone array sound pick-up device, and then the picked sound source signals are processed by the central processing unit.
The recognition module comprises a plurality of pickup units, and is used for carrying out band-pass filtering on picked sound waves, filtering road noises with frequencies smaller than and larger than automobile whistling sounds and only reserving audio signals near the whistling sounds. Then, the identification module carries out short-time energy over-limit detection on the audio signals near the reserved whistle sound, and detects a suspected automobile whistle sound signal. And finally, carrying out frequency matching to determine the whistle sound signal.
Optionally, the plurality of sound pickup units band-pass filter the picked sound waves according to an upper threshold value and a lower threshold value, and the upper threshold value and the lower threshold value can be determined according to the statistical whistle sound frequency of the general motor vehicle. Optionally, according to the experimentally measured characteristics of the whistling sound of the motor vehicle, a band-pass filter with cut-off frequencies of 200Hz and 5KHz is added at the front end of the microphone sensor, so that the road noise can be filtered to the maximum extent, and meanwhile, the signal component of the whistling sound of the motor vehicle in the intermediate frequency part is retained.
And performing analog-to-digital conversion on the processed band-pass signal to further determine whether the processed band-pass signal is whistling noise.
The identification module of the invention also carries out short-time energy over-limit detection on the audio signals near the whistling sound obtained after band-pass filtering. This is because, due to the time-varying nature of the signal, the signal is divided into frames of a certain length using a window function, and the characteristics of the signal are considered to be substantially unchanged during these short times. In the invention, a Hamming window with a wider main lobe is used to obtain a smoother frequency spectrum.
The invention adopts an endpoint detection method based on time domain short-time energy, and the endpoint detection aims to detect the position of a voice signal suspected of whistling of an automobile.
The short-time energy of a frame of sound signal x (n) is defined as:
wherein w (N) is a window function, N is a window length and is the length of a sound processing frame, x (N) and x (m) are N, and the frame signal at the time of m. Most of road noise is removed from the sound signal after the first step of band-pass filtering, and here, the detection threshold can be set as ElWhen E isn＞ElAnd judging that a suspected automobile whistling sound signal appears in the current sound frame.
Wherein E isl＝min[0.03(Emax-Emin)+Er，4Er]
Wherein E ismaxIs the maximum value of energy, EminAs energy minimum, ErIs the average of all framing energies.
After the suspected automobile whistling signal frames are detected, the identification module further performs frequency comparison to determine whistling signals from the suspected automobile whistling signal frames. Specifically, the identification module converts a suspected automobile whistling sound signal detected by the short-time energy method into a frequency domain, and identifies the whistling sound by using the statistical characteristics of the whistling sound and the noise frequency spectrum. The automobile whistle noise frequency characteristics are matched according to the preset whistle noise frequency characteristics, and if the automobile whistle noise frequency characteristics are matched, the suspected automobile whistle sound signals are determined to be confirmed whistle sound signals.
In the invention, the multi-noise source positioning module positions the sound source according to the whistle sound signal determined by the identification module. Optionally, the multiple noise source positioning module obtains a translation operator corresponding to the sound source by constructing a special matrix beam and performing generalized characteristic decomposition on the special matrix beam based on the translation invariance of the signal subspace caused by the translation invariance of the first-class array by using an ESPRIT algorithm, so as to obtain the direction parameter estimation. In this step, the three-dimensional coordinates of the signal source can be calculated from the signal source direction determined for each sound pickup unit. The implementation of this module will be described in detail below.
In the invention, the multi-noise sound source positioning module comprises a first microphone array and a second microphone array which are arranged in the sound pickup device. Fig. 3 shows an array distribution schematic of a first microphone array and a second microphone array in the present invention. As shown in fig. 3, the first microphone array and the second microphone array are distributed as uniform linear arrays X and Y with array elements N distributed on the X axis and the Y axis, the array elements at the original points are two linear arrays X, Y are common, the first microphone array and the second microphone array are composed of the same array elements, the first microphone array and the second microphone array are symmetrical about a plane, and the distance between the center positions of the two arrays is L, and L is set according to actual needs.
Optionally, in the present invention, when three-dimensionally positioning the determined whistling sound signal, an azimuth angle of the noise source relative to the array center point may be calculated based on an ESPRIT algorithm, assuming that the center of the first microphone array is a three-dimensional coordinate origin (0, 0, 0), an array element pitch of the linear array is d, an output noise of the array is zero mean, and a variance is σ2White gaussian noise is statistically independent and uncorrelated with noise sources.
If M statistically independent noise sources exist in the space, the frequency and the incidence angle of the noise sources relative to the reference array element are respectively fi，θi，The received signal vectors for linear array X and linear array Y are respectively
Wherein: s (t) [ S ]l(t)，...，sk(t)，...sM(t)]TIs a whistling noise vector;
a direction matrix representing a linear array X;
a direction matrix representing the linear array Y;
ax(fi，θi，φi) Representing the receiving function of the i noise source, and different direction matrixes of different receiving array elements are different;
Nx(t) and Ny(t) noise signals representing non-whistle noise received by the linear arrays, respectively.
In order to obtain the joint estimation of the two-dimensional arrival angle and the frequency of the multi-whistle noise signal, the invention is based on a joint estimation method of an ESPRIT algorithm, but the invention is not limited to be only applicable to the system. In the microphone array, received signal vectors of front N-1 array elements and rear N-1 array elements of a sub-array X are respectively marked as X1(t)、X2(t), the received signal vectors of the first N-1 array elements and the last N-1 array elements of the subarray Y are respectively marked as Y1(t)、Y2(t) then there are
Wherein:
Axis a directional matrix of an X array, AyIs a direction matrix of the Y array.
To estimate the frequency of the signal, a delay τ is added to the received signal of the array.
Only the first N-1 array elements in the X subarray need to be delayed by one section, and the data obtained after delay is recorded as X3According to the estimation of the space two-dimensional spectrum, the following results are obtained:
wherein
From the principle of the rotation invariant subspace, if the spatial noise is white, X can be constructed1(t) autocovariance matrixX1(t) and X2(t) cross covariance matrixY1(t) autocovariance matrixY1(t) and Y2(t) cross covariance matrixX1(t) and X3(t) co-ordinating partyDifference matrixIf the spatial noise is Gaussian white noise, the 5 covariance matrixes are denoised
Respectively obtainGeneralized eigenvalues of 3 matrix pairs:
vfi＝exp(-j2πfiτ)，
wherein, i is 1, 2, M is the number of whistling noise sources.
By combining the three formulas, the frequency, the azimuth angle and the depression angle of the noise source can be solved:
vfi＝exp(-j2πfiτ)
wherein, thetaiAnd phiiRespectively an azimuth angle and a depression angle of the ith noise source; f. ofiIs the frequency of the ith noise source, and tau is the receiving line array X3Relative to the receiving linear array X1Delay of the received signal; d is the distance between array elements in the first microphone array and the second microphone array, and c is the speed of light; vfi、vXi、vYiAre respectively a matrix pairThe generalized eigenvalues of (1). In the process of solving the equation, a special matrix bundle is constructed and generalized eigen decomposition is carried out on the special matrix bundle, and vf is assumedi，vXi，vYiAre respectively a matrixThe generalized eigenvalues of (1), then the matrixAs follows:
wherein,is X1(t) self-coordinationThe variance matrix is used to determine the variance of the received signal,is X1(t) and X2(t) a cross-covariance matrix of,is Y1(t) an auto-covariance matrix of,is Y1(t) and Y2(t) a cross-covariance matrix of,is X1(t) and X3(t) cross covariance matrix, I is identity matrix, σ2Is the variance of Gaussian white noise; x1(t) and X2(t) receiving signal vectors of the front N-1 array elements and the rear N-1 array elements of the sub-array X of the first microphone array respectively; y is1(t) and Y2(t) receiving signal vectors of the front N-1 array elements and the rear N-1 array elements of the sub-array Y of the first microphone array respectively; x3And (t) is a signal vector of signals received by the first N-1 array element pairs in the subarray X of the first microphone array after being delayed by tau.
According to the received signal vectors of the corresponding array elements of the first microphone array and the second microphone array, the spatial coordinate of the ith noise source can be calculated
zi＝(zi1+zi2)/2
Wherein x isi、yiAnd ziRespectively are the spatial coordinates of the ith noise source; thetai1Andrespectively setting a direction angle and a pitch angle of the ith noise source relative to the central point of the first microphone array; thetai2Andrespectively the direction angle and the pitch angle of the ith noise source relative to the central point of the second microphone array.
The camera module of the present invention comprises a plurality of cameras uniformly distributed between a first microphone array and a second microphone array of the multi-noise sound source localization module, as shown in fig. 4; and after the multi-noise sound source positioning module obtains the space coordinates of the whistle sound signals, the camera module adjusts and controls the camera to rotate and focus, and the noise source is shot. Specifically, all the cameras can be selected to shoot at the noise source, or at least one camera closest to the noise source can be selected to shoot at the noise source.
In which violation marking is performed on a picture taken by a camera.
Wherein the marked photos are stored or transmitted in real time to the relevant traffic authorities.
And adjusting and controlling the camera to carry out point searching shooting on the nearest point noise source.
The number of the cameras can be determined according to the automobile whistle frequency counted on the road section.
And shooting the automobile which violates the whistle, and storing the picture in a memory of the camera or transmitting the picture to a related traffic management department in real time.
Wherein, the picture of the noise source is shot and marked with a whistling violation.
The memory in the camera is regularly processed to prevent the memory from being over-full and incapable of shooting.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. An automotive whistle noise monitoring system comprising:
the whistle noise identification module is used for identifying the picked sound waves and finally determining a whistle noise signal of the automobile;
the noise source positioning module is used for determining an automobile whistle noise source according to the automobile whistle noise signal identified by the whistle noise identification module;
the camera module comprises a plurality of cameras and is used for shooting the determined automobile whistle noise source, performing camera point finding shooting according to the three-dimensional point determined by the sound source positioning module, identifying the shot picture and storing or transmitting the identified illegal vehicle picture to a traffic management department in real time;
the whistling noise identification module identifies the picked sound waves as follows:
performing band-pass filtering on the picked sound waves to filter other noise signals of which the frequency is not outside the range of the automobile whistling noise signals;
short-time energy over-limit detection is carried out on the sound waves subjected to band-pass filtering, and suspected automobile whistling noise signals are detected;
matching the frequency of the suspected automobile whistle noise signal with the frequency of a preset automobile whistle noise signal to determine the automobile whistle noise signal;
the short-time energy over-limit detection of the sound wave subjected to band-pass filtering specifically comprises the following steps:
dividing the band-pass filtered sound wave into a plurality of frame signals by using a window function, wherein the characteristics of the signals in each frame signal are unchanged;
calculating the short-time energy of each frame signal and comparing the short-time energy with an energy detection threshold value;
when the short-time energy of the frame signal is larger than the energy detection threshold value, determining the frame signal as a suspected automobile whistling noise signal;
the short-time energy of each frame signal is calculated as follows:
wherein E isnTaking the short-time energy of the nth frame signal as w (N) as a window function, N as a window length, and x (N) as the nth frame signal;
the energy monitoring threshold value is calculated as follows:
El＝min[0.03(Emax-Emin)+Er,4Er]
wherein E ismaxFor the energy maximum in all frame signals, EminIs the minimum of the energy in all frame signals, ErAveraging the energy of all frame signals；
The noise source positioning module determines an automobile whistle noise source according to the automobile whistle noise signal identified by the whistle noise identification module:
acquiring signal vectors of each automobile whistling noise source received by a microphone array; the microphone array comprises a first microphone array and a second microphone array which are respectively distributed on an X axis and a Y axis, and the first microphone array and the second microphone array comprise equal-quantity microphone elements which are uniformly spaced;
estimating and obtaining the frequency, the azimuth angle and the depression angle of each automobile whistle noise source based on an ESPRIT algorithm according to the acquired signal of each automobile whistle noise source;
and calculating to obtain the space coordinates of each automobile whistle noise source according to the frequency, the azimuth angle and the depression angle of each automobile whistle noise source.
2. The system of claim 1, wherein the blast noise identification module utilizes a plurality of pickup devices for picking up sound waves; the noise source positioning module positions a noise source by using a microphone array arranged in the pickup device; the camera module utilizes a plurality of cameras to shoot the automobile whistling noise source.
3. The system of claim 2, wherein the frequency, azimuth and depression of each of the automotive whistle noise sources is calculated as follows:
vfi＝exp(-j2πfiτ)，
wherein, thetaiAnd phiiRespectively an azimuth angle and a depression angle of the ith noise source; f. ofiIs the frequency of the ith noise source, and tau is the time delay; d is the distance between array elements in the first microphone array and the second microphone array, and c is the speed of light; vfi、vXiAnd vYiRespectively, intermediate values are calculated.
4. The system of claim 1, wherein the spatial coordinates of each of the automotive whistling noise sources are calculated as follows:
zi＝(zi1+zi2)/2
wherein x isi、yiAnd ziRespectively are the spatial coordinates of the ith noise source; thetai1Andrespectively setting a direction angle and a pitch angle of the ith noise source relative to the central point of the first microphone array; thetai2Andrespectively setting the direction angle and the pitch angle of the ith noise source relative to the central point of the second microphone array; and L is the center position distance of the first microphone array and the second microphone array.
5. The system of claim 1, wherein the short-time energy of each frame signal is calculated as follows:
wherein E isnTaking the short-time energy of the nth frame signal as w (N) as a window function, N as a window length, and x (N) as the nth frame signal;
the energy monitoring threshold value is calculated as follows:
El＝min[0.03(Emax-Emin)+Er,4Er]
wherein E ismaxFor the energy maximum in all frame signals, EminIs the minimum of the energy in all frame signals, ErIs the average of all frame signal energies.
6. A method of monitoring automotive whistle noise comprising:
identifying the picked sound waves, and finally determining an automobile whistling noise signal;
determining an automobile whistle noise source according to the automobile whistle noise signal identified by the whistle noise identification module;
shooting the determined automobile whistling noise source;
wherein the picked-up sound waves are identified as follows:
performing band-pass filtering on the picked sound waves to filter other noise signals of which the frequency is not outside the range of the automobile whistling noise signals;
short-time energy over-limit detection is carried out on the sound waves subjected to band-pass filtering, and suspected automobile whistling noise signals are detected;
matching the frequency of the suspected automobile whistle noise signal with the frequency of a preset automobile whistle noise signal to determine the automobile whistle noise signal;
the short-time energy over-limit detection of the sound wave subjected to band-pass filtering specifically comprises the following steps:
dividing the band-pass filtered sound wave into a plurality of frame signals by using a window function, wherein the characteristics of the signals in each frame signal are unchanged;
calculating the short-time energy of each frame signal and comparing the short-time energy with an energy detection threshold value;
when the short-time energy of the frame signal is larger than the energy detection threshold value, determining the frame signal as a suspected automobile whistling noise signal;
the short-time energy of each frame signal is calculated as follows:
wherein E isnTaking the short-time energy of the nth frame signal as w (N) as a window function, N as a window length, and x (N) as the nth frame signal;
the energy monitoring threshold value is calculated as follows:
El＝min[0.03(Emax-Emin)+Er,4Er]
wherein E ismaxFor the energy maximum in all frame signals, EminIs the minimum of the energy in all frame signals, ErIs the average of all frame signal energies.
7. An automotive whistle noise monitoring system comprising:
the whistle noise identification module comprises a plurality of pickup units, wherein the pickup units are respectively used for picking up sound waves of the surrounding environment, identifying the picked sound waves and finally determining a whistle noise signal of the automobile;
a noise source localization module comprising a first microphone array and a second microphone array, the microphone arrays comprising a first microphone array and a second microphone array distributed on an X-axis and a Y-axis, respectively, and the first microphone array and the second microphone array comprising equal-amount microphone elements with uniform intervals; the first microphone array and the second microphone array determine an automobile whistle noise source according to the automobile whistle noise signals identified by the whistle noise identification module;
the camera module comprises a plurality of cameras and is used for shooting the determined automobile whistling noise sources respectively;
the whistling noise identification module identifies the picked sound waves as follows:
performing band-pass filtering on the picked sound waves to filter other noise signals of which the frequency is not outside the range of the automobile whistling noise signals;
short-time energy over-limit detection is carried out on the sound waves subjected to band-pass filtering, and suspected automobile whistling noise signals are detected;
matching the frequency of the suspected automobile whistle noise signal with the frequency of a preset automobile whistle noise signal to determine the automobile whistle noise signal;
the short-time energy over-limit detection of the sound wave subjected to band-pass filtering specifically comprises the following steps:
dividing the band-pass filtered sound wave into a plurality of frame signals by using a window function, wherein the characteristics of the signals in each frame signal are unchanged;
calculating the short-time energy of each frame signal and comparing the short-time energy with an energy detection threshold value;
when the short-time energy of the frame signal is larger than the energy detection threshold value, determining the frame signal as a suspected automobile whistling noise signal;
the short-time energy of each frame signal is calculated as follows:
wherein E isnTaking the short-time energy of the nth frame signal as w (N) as a window function, N as a window length, and x (N) as the nth frame signal;
the energy monitoring threshold value is calculated as follows:
El＝min[0.03(Emax-Emin)+Er,4Er]
wherein E ismaxFor the energy maximum in all frame signals, EminIs the minimum of the energy in all frame signals, ErIs the average of all frame signal energies.
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