Open AccessSingle source bins detection‐based localisation scheme for multiple speech sources
Author(s) -
Jia Maoshen,
Sun Jundai,
Deng Feng,
Sun Junyue
Publication year - 2017
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2017.0171
Subject(s) - scheme (mathematics) , computer science , cluster analysis , microphone , convergence (economics) , pattern recognition (psychology) , algorithm , artificial intelligence , mathematics , telecommunications , mathematical analysis , sound pressure , economics , economic growth
A single source bins (SSBs) detection based multiple source localisation scheme is proposed. This scheme is based on detecting the SSBs in mixture signals that are only derived from one source. Specifically, after proposing a ‘DOA convergence’ assumption, K ‐means clustering algorithm is used for SSBs detecting. Thus, the multiple source localisation is converted to a single source one among these SSBs. Moreover, the proposed SSBs detection is applicable to other localisation methods and not limited to specific microphone topology. Experimental results demonstrate the localisation accuracy of the proposed method outperforms the localisation approaches which are based on single source zone detection.