文献类型：期刊文献

英文题名：Improved monaural speech segregation based on computational auditory scene analysis

作者：Wang Yu[1];Lin Jiajun[1];Chen Ning[1];Yuan Wenhao[1]

机构：[1]E China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2013

卷号：2013

期号：1

外文期刊名：EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING

收录：;EI(收录号：20134616962601);WOS:【SCI-EXPANDED(收录号:WOS:000316328100001)】；

基金：This study was supported by the National Natural Science Foundation of China (Grant nos. 60903186, 61271349).

语种：英文

外文关键词：Speech segregation; Computational Auditory Scene Analysis (CASA); Threshold selection; Morphological image processing

摘要：A lot of effort has been made in Computational Auditory Scene Analysis (CASA) to segregate target speech from monaural mixtures. Based on the principle of CASA, this article proposes an improved algorithm for monaural speech segregation. To extract the energy feature more accurately, the proposed algorithm improves the threshold selection for response energy in initial segmentation stage. Since the resulting mask map often contains broken auditory element groups after grouping stage, a smoothing stage is proposed based on morphological image processing. Through the combination of erosion and dilation operations, we suppress the intrusions by removing the unwanted particles and enhance the segregated speech by complementing the broken auditory elements. Systematic evaluation shows that the proposed segregation algorithm improves the output signal-to-noise ratio by an average of 8.55 dB and cuts the percentage of noise residue by an average of 25.36% compared with the mixture, yielding a significant improvement for speech segregation.