標題: Bayesian Factorization and Selection for Speech and Music Separation
作者: Yang, Po-Kai
Hsu, Chung-Chien
Chien, Jen-Tzung
電機工程學系
Department of Electrical and Computer Engineering
關鍵字: nonnegative matrix factorization;model selection;Bayesian learning;source separation
公開日期: 1-Jan-2014
摘要: This paper proposes a new Bayesian nonnegative matrix factorization (NMF) for speech and music separation. We introduce the Poisson likelihood for NMF approximation and the exponential prior distributions for the factorized basis matrix and weight matrix. A variational Bayesian (VB) EM algorithm is developed to implement an efficient solution to variational parameters and model parameters for Bayesian NMF. Importantly, the exponential prior parameter is used to control the sparseness in basis representation. The variational lower bound in VB-EM procedure is derived as an objective to conduct adaptive basis selection for different mixed signals. The experiments on single-channel speech/music separation show that the adaptive basis representation in Bayesian NMF via model selection performs better than the NMF with the fixed number of bases in terms of signal-to-distortion ratio.
URI: http://hdl.handle.net/11536/146420
ISSN: 2308-457X
期刊: 15TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2014), VOLS 1-4
起始頁: 998
結束頁: 1002
Appears in Collections:Conferences Paper