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Sound source identification method based on Bayesian compressed sensing

A technology of Bayesian compression and sound source identification, which is applied to the measurement of ultrasonic/sonic/infrasonic waves, speech analysis, instruments, etc., and can solve problems such as unstable reconstruction performance

Pending Publication Date: 2020-09-15
CHONGQING UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

However, the defect of this algorithm is that the reconstruction performance in the low frequency range is unstable and there is a large error

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  • Sound source identification method based on Bayesian compressed sensing
  • Sound source identification method based on Bayesian compressed sensing
  • Sound source identification method based on Bayesian compressed sensing

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0075] see Figure 1 to Figure 6 , a sound source recognition method based on Bayesian compressed sensing, comprising the following steps:

[0076] 1) Build a sound source recognition system based on Bayesian compressed sensing, including a microphone array sensor, a multi-channel data collector and a data processor. The microphone array sensor includes M microphones distributed in the sound source detection space.

[0077] 2) Each microphone separately monitors the time-domain analog sound pressure signals of N equivalent sound sources, and sends them to a multi-channel data collector at the same time. The N equivalent sound sources are distributed around the sound source.

[0078] 3) The multi-channel data collector converts the received time-domain analog sound pressure signal into a digital sound pressure signal p, and sends it to the data processor.

[0079] 4) The data processor establishes a sound source identification model, and obtains a transfer matrix A between t...

Embodiment 2

[0137] A sound source recognition method based on Bayesian compressed sensing, comprising the following steps:

[0138] 1) Build a sound source recognition system based on Bayesian compressed sensing, including a microphone array sensor, a multi-channel data collector and a data processor. The microphone array sensor includes M microphones distributed in the sound source detection space.

[0139] 2) Each microphone separately monitors the time-domain analog sound pressure signals of N equivalent sound sources, and sends them to a multi-channel data collector at the same time. The N equivalent sound sources are distributed around the sound source.

[0140] 3) The multi-channel data collector converts the received time-domain analog sound pressure signal into a digital sound pressure signal p, and sends it to the data processor.

[0141] 4) The data processor establishes a sound source identification model, and obtains a transfer matrix A between the sound source and the micro...

Embodiment 3

[0147] A method for identifying a sound source based on Bayesian compressed sensing, the main steps of which are shown in Embodiment 2, wherein the main steps of establishing the transfer matrix A between the sound source and the microphone array sensor are as follows:

[0148] 1) Determine the sound pressure signal p(m) measured by the mth microphone as follows:

[0149]

[0150] In the formula, m=1,2,...,M. is the free-field Green's function. k is the wave number, is the distance from the equivalent source to the measurement surface. q n is the sound source intensity of the virtual equivalent source.

[0151] 2) Formula (1) is expressed in the form of vector matrix, namely:

[0152] p = Aq. (2)

[0153] In the formula, A is the M×N dimensional sound field transfer matrix. p is the M-dimensional sound pressure vector, and its elements are the single-block beat signals of the corresponding sensors. q is an N-dimensional sound source intensity column vector, and i...

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Abstract

The invention discloses a sound source identification method based on Bayesian compressed sensing. The method comprises the following main steps: 1) establishing a sound source identification system based on Bayesian compressed sensing; 2) each microphone respectively monitoring time domain analog sound pressure signals of N equivalent sound sources; 3) converting the received time domain analog sound pressure signals into digital sound pressure signals p by a multi-channel data collector; 4) acquiring a transfer matrix A between the sound sources and a microphone array sensor; 5) establishinga prior probability distribution function model of the digital sound pressure signals p and a to-be-identified sound source q; 6) establishing a posterior probability distribution function model of the to-be-identified sound source q; 7) updating hyper-parameters of the to-be-identified sound source q; and 8) a data processor performing iterative computation on the hyper-parameters of the to-be-identified sound source q by using a parameter updating formula to obtain an identification result of the to-be-identified sound source q. According to the invention, the disadvantage of narrow applicable frequency range of TRESM and WBH methods is effectively overcome, and the frequency range of sound source identification is widened.

Description

technical field [0001] The invention relates to the field of sound source recognition, in particular to a sound source recognition method based on Bayesian compressed sensing. Background technique [0002] Acoustic holography and beamforming technology are two noise source identification methods based on acoustic arrays, which have the advantages of fast measurement speed, high acoustic imaging efficiency, and the ability to measure moving sound sources. It can realize the visualization of the sound field, which is convenient for more intuitive identification and positioning of the sound source. Among them, acoustic holography technology is a sound source identification method that has developed rapidly in recent years. The basic principle is to record the sound pressure data on the close-range measurement surface of the surface of the measured sound source object, and then reconstruct the spatial sound field through the spatial sound field transformation algorithm. By meas...

Claims

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Application Information

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IPC IPC(8): G01H17/00G10L21/057
CPCG01H17/00G10L21/057
Inventor 昝鸣徐中明张志飞贺岩松
Owner CHONGQING UNIV
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