Distributed microphone array sound source positioning method based on space sparsity

Abstract

The invention discloses a distributed microphon array sound source positioning method based on space sparsity, comprising steps of performing signal characteristic extraction through two steps of discrete cosine transform (discrete cosine transform DCT), utilizing the characteristics to construct a sparsity positioning model in order to comprehensively utilize the short time or long time characteristics of the voice signal, reducing the dimension of the model, utilizing a dictionary learning method to perform correction on the model, adopting an approximating I0 norm reconstruction algorithm to perform reconstruction on a sparse signal to find a sound source. The invention has no special requirement on the array structure, requires less calculation, effectively improves the capability of resisting noise and reverberation and positioning accuracy, and is suitable for indoor sound positioning and widely applicable to the video conference system, the voice recognition system and the intelligent robots.

Description

technical field [0001] The invention belongs to the technical field of sound source localization, and in particular relates to a spatial sparsity-based distributed microphone array sound source localization method. Background technique [0002] As an important research direction in the localization field, sound source localization based on microphone array has broad application prospects. In the civilian field, it is widely used in teleconferencing systems, video conferencing systems and videophone systems. By locating the speaker, it can realize the real-time tracking of the sound source and the external camera at the conference site. The microphone array in the hearing aid is used as a pre-processing system for speech enhancement. By detecting the position information of the sound source, the array is directed to the direction of the sound source of interest, thereby suppressing interference and noise in other directions. In the military field, microphone array sound sour...

AI Technical Summary

Problems solved by technology

In localization applications, large coefficients often correspond to sound source targets, while small coefficients may correspond to noise, so commonly used greedy algorithms and convex optimization algorithms may weaken the contribution of large coefficients in the reconstruction process, but do not apply to small coefficients. Too many constraints, resulting in a decrease in reconstruction accuracy, and even misjudgment of noise as a positioning target when the signal-to-noise ratio is low

On the other hand, reverberation is ubiquitous in the indoor environment, and the reflection and scattering of sound by walls and ceilings has time-varying characteristics and is unpredictable, so that there are often large errors between the actual signal and the sparse positioning model, which ultimately affects the positioning performance

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