Double sound source localization method based on consistent focusing transform least square method

Abstract

The invention discloses a double sound source localization method based on a consistent focusing transform least square method. According to the method, a predesigned six-element circular microphone array is applied to acquire sound source signals, and the covariance matrix of the acquired signals is acquired; a focusing transform matrix is defined by utilizing the center frequency point of frequency range, and the focusing transform matrix is solved by the least square method; and a signal spatial spectrum corresponding to each center frequency point is acquired by utilizing the center frequency points of different bandwidths, a consistent focusing matrix and a multiple signal classification method, and then the average estimation value of the signal spatial spectrum is obtained by utilizing the average value of the frequency points and a time snapshot estimation method (MUSIC) so that a sound source azimuth angle estimation value is acquired. The method is high in sound source localization estimation accuracy so that an azimuth ambiguity problem can be effectively overcome.

Description

technical field [0001] The invention relates to the field of sound source localization, in particular to a dual sound source localization method based on the consistent focus transformation least square method. Background technique [0002] In array signal processing, using Direction of Arrival (DOA) to estimate sound source position is a new research direction. In sonar detection (see literature: Wang Yan, Zou Nan, Liang Guolong. Hydrophone array position Near-field active correction method [J]. Acta Physica Sinica, 2015, 64(2): 0243041-10), speech recognition and tracking (see literature: Qi Yubo, Zhou Shihong, Zhang Renhe, Ren Yun. A method based on β-warping transform Passive sound source distance estimation method based on operator[J]. Acta Physica Sinica, 2015,64(7):0743011-6), robot movement in unknown environment (see literature: Ju Tailiang. Research on sound source localization algorithm based on microphone array [D]. Doctoral dissertation (Chengdu: University of ...

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Problems solved by technology

Literature (Jingdong Chen, Jacob Benesty, Yiteng Huang. Timedelay estimation in room acoustic environments: an overview [J]. EURASIP Journalon Applied Signal Processing, 2006, 26503: 1-19) gives a review of TDOA orientation estimation technology; in a single sound source, due to the interference of noise, reverberation, etc. , will affect the signal collected between the microphone pairs, and reduce the accuracy of the positioning estimation result. The literature (FrancescoNesta, MaurizioOmologo. Generalized state coherence transform for multidimensional TDOA estimation of multiple sources [J]. IEEETransactionsonAudio, Speech, and Language Processing, 2012, 20(1): 246-260.) gives an improvement The TDOA estimation method of

Further research found that localizing multiple sound sources simultaneously is a more difficult problem

In fact, even a brief sound will cause a slight overlap in the collected speech signals, which may lead to inaccurate localization of multiple source signals (see literature: JacekP. .IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2007:18-21)

For the multi-sound source localization problem, one is the traditional Multiple Signal Classification (MultipleSignalClassification, MUSIC) method, which belongs to the classical subspace method. This method is aimed at narrowband signals, and performs feature decomposition according to the subspace covariance matrix of the collected signals, and then Estimate the source signal orientation (see literature: DumiduS.Talagala, WenZhang. BroadbandDOAEstimationUsingSensorArraysonComplex-ShapedRigidBodies[J].IEEETransactionsonAudio,Speech,andLanguageProcessing,2013,21(8):1573-1585.), because the frequency bandwidth of the voice signal is generally [300Hz , 3000Hz], the classical subspace method is only for narrow-band signals, which will lead to inaccurate sound source localization results; the other is a source localization method based on independent component analysis, which first uses the Blind Source Separation (BSS) method to obtain 分离后单个信号,然后再运用对单个源信号进行定位的方法进行定位估计(见文献:AnthonyLombard,YuanhangZheng,HerbertBuchner,WalterKellermann.TDOAEstimationforMultipleSoundSourcesinNoisyandReverberantEnvironmentsUsingBroadbandIndependentComponentAnalysis[J].IEEETransactionsonAudio,Speech,andLanguageProcessing,2011,19(6):1490 -1503.), but this kind of source location method will destroy the integrity of the source signal because the voice signal is separated first and then positioned, which will make the location result unstable and the location accuracy not high

In addition, as the number of microphones increases, the positioning accuracy of the direction of arrival (DOA) estimation will also increase, but in actual situations, the linear microphone array model is usually used, which will cause positioning ambiguity problems (the sound source cannot be accurately distinguished at the front or back of the array)

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