{"title":"A Compressed Sensing Approach to Blind Separation of Speech Mixture Based on a Two-Layer Sparsity Model","authors":"Guangzhao Bao, Z. Ye, Xu Xu, Yingyue Zhou","doi":"10.1109/TASL.2012.2234110","DOIUrl":null,"url":null,"abstract":"This paper discusses underdetermined blind source separation (BSS) using a compressed sensing (CS) approach, which contains two stages. In the first stage we exploit a modified K-means method to estimate the unknown mixing matrix. The second stage is to separate the sources from the mixed signals using the estimated mixing matrix from the first stage. In the second stage a two-layer sparsity model is used. The two-layer sparsity model assumes that the low frequency components of speech signals are sparse on K-SVD dictionary and the high frequency components are sparse on discrete cosine transformation (DCT) dictionary. This model, taking advantage of two dictionaries, can produce effective separation performance even if the sources are not sparse in time-frequency (TF) domain.","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2012.2234110","citationCount":"43","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Audio Speech and Language Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TASL.2012.2234110","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 43
Abstract
This paper discusses underdetermined blind source separation (BSS) using a compressed sensing (CS) approach, which contains two stages. In the first stage we exploit a modified K-means method to estimate the unknown mixing matrix. The second stage is to separate the sources from the mixed signals using the estimated mixing matrix from the first stage. In the second stage a two-layer sparsity model is used. The two-layer sparsity model assumes that the low frequency components of speech signals are sparse on K-SVD dictionary and the high frequency components are sparse on discrete cosine transformation (DCT) dictionary. This model, taking advantage of two dictionaries, can produce effective separation performance even if the sources are not sparse in time-frequency (TF) domain.
期刊介绍:
The IEEE Transactions on Audio, Speech and Language Processing covers the sciences, technologies and applications relating to the analysis, coding, enhancement, recognition and synthesis of audio, music, speech and language. In particular, audio processing also covers auditory modeling, acoustic modeling and source separation. Speech processing also covers speech production and perception, adaptation, lexical modeling and speaker recognition. Language processing also covers spoken language understanding, translation, summarization, mining, general language modeling, as well as spoken dialog systems.