Bayesian factorization and selection for speech and music separation

Po Kai Yang*, Chung Chien Hsu, Jen-Tzung Chien

*此作品的通信作者

研究成果: Conference article同行評審

5 引文 斯高帕斯(Scopus)

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.

原文English
頁(從 - 到)998-1002
頁數5
期刊Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
出版狀態Published - 1 一月 2014
事件15th Annual Conference of the International Speech Communication Association: Celebrating the Diversity of Spoken Languages, INTERSPEECH 2014 - Singapore, Singapore
持續時間: 14 九月 201418 九月 2014

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