Deep learning for binaural sound source localization with low signal-to-noise ratio - 文章详情页

作者：

Zhao, Fengnian (Zhao, Fengnian.) | Li, Ruwei (Li, Ruwei.) | Pan, Dongmei (Pan, Dongmei.)

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摘要：

A　novel　deep　learning　(DL)　method　is　proposed　for　binaural　sound　source　localization　with　low　SNR.　Firstly,　the　binaural　sound　signals　are　decomposed　into　several　channels　by　using　Gammatone　filter.　Secondly,　the　4　feature　parameters　of　Head-related　Transfer　Function,　interaural　time　difference　(ITD),　interaural　coherence　(IC),　interaural　level　difference　(ILD),　and　interaural　phase　difference　(IPD)　are　extracted.　Thirdly,　ITD　and　IC　go　through　a　Deep　Belief　Network　(DBN)　to　determine　the　quadrant　of　the　sound　source　and　reduce　the　positioning　range.　Then,　ITD,　IC,　ILD,　and　IPD　go　through　a　Deep　Neural　Network　(DNN)　to　obtain　the　azimuthal　angle　within　90　degrees.　Experimental　results　show　that　the　proposed　algorithm　can　solve　the　front-back　confusion,　and　obtain　a　superior　performance　with　lower　complexity　and　higher　precision　under　low　SNR　conditions.　©　2021　Institute　of　Physics　Publishing.　All　rights　reserved.

关键词：

Acoustic generators Deep learning Deep neural networks Integrated circuits Signal processing Signal to noise ratio

作者机构：

[ 1 ] [Zhao, Fengnian]Lab of Acoustical and Optical Information Processing, College of Information and Communication, Beijing University of Technology, Beijing, China
[ 2 ] [Li, Ruwei]Lab of Acoustical and Optical Information Processing, College of Information and Communication, Beijing University of Technology, Beijing, China
[ 3 ] [Pan, Dongmei]Lab of Acoustical and Optical Information Processing, College of Information and Communication, Beijing University of Technology, Beijing, China

通讯作者信息：

[li, ruwei]lab of acoustical and optical information processing, college of information and communication, beijing university of technology, beijing, china

电子邮件地址：

liruwei@bjut.edu.cn

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