標題: Layered Nonnegative Matrix Factorization for Speech Separation
作者: Hsu, Chung-Chien
Chien, Jen-Tzung
Chi, Tai-Shih
電機學院
College of Electrical and Computer Engineering
關鍵字: Layered NMF;dictionary learning;NMF;speech separation
公開日期: 2015
摘要: This paper proposes a layered nonnegative matrix factorization (L-NMF) algorithm for speech separation. The standard NMF method extracts parts-based bases out of nonnegative training data and is often used to separate mixed spectrograms. The proposed L-NMF algorithm comprises of several layers of standard NMF blocks. During training, each layer of the L-NMF is initialized separately and then fine-tuned by minimizing the propagated reconstruction error. More complicated bases of the training data are emerged in deeper layers of the L-NMF by progressively combining parts-based bases extracted in the first layer. In other words, these complicated bases contain collective information of the parts-based bases. The bases deciphered by all layers are then used to separate spectrograms in the conventional NMF way. Simulation results show the proposed L-NMF outperforms the standard NMF in terms of the source-to-distortion ratio (SDR).
URI: http://hdl.handle.net/11536/136224
ISBN: 978-1-5108-1790-6
期刊: 16TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2015), VOLS 1-5
起始頁: 628
結束頁: 632
Appears in Collections:Conferences Paper