標題: Bayesian Group Sparse Learning for Nonnegative Matrix Factorization
作者: Chien, Jen-Tzung
Hsieh, Hsin-Lung
電機資訊學士班
Undergraduate Honors Program of Electrical Engineering and Computer Science
關鍵字: Bayesian sparse learning;group sparsity;nonnegative matrix factorization;source separation
公開日期: 2012
摘要: Nonnegative matrix factorization (NMF) is developed for parts-based representation of nonnegative data with the sparseness constraint. The degree of sparseness plays an important role for model regularization. This paper presents Bayesian group sparse learning for NMF and applies it for single-channel source separation. This method establishes the common bases and individual bases to characterize the shared information and residual noise in observed signals, respectively. Laplacian scale mixture distribution is introduced for sparse coding given a sparseness control parameter. A Markov chain Monte Carlo procedure is presented to infer two groups of parameters and their hyperparameters through a sampling procedure based on the conditional posterior distributions. Experiments on separating the single-channel audio signals into rhythmic and harmonic source signals show that the proposed method outperforms baseline NMF, Bayesian NMF and other group-based NMF in terms of signal-to-interference ratio.
URI: http://hdl.handle.net/11536/22055
ISBN: 978-1-62276-759-5
期刊: 13TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2012 (INTERSPEECH 2012), VOLS 1-3
起始頁: 1550
結束頁: 1553
Appears in Collections:Conferences Paper