Online Divergence Switching for Superresolution-Based Nonnegative Matrix Factorization

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Information about Online Divergence Switching for Superresolution-Based Nonnegative...
Technology

Published on March 10, 2014

Author: NAIST_IS

Source: slideshare.net

2014 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing Speech Analysis(2),2PM2-2 Online Divergence Switching for Superresolution-Based Nonnegative Matrix Factorization Daichi Kitamura, Hiroshi Saruwatari, Satoshi Nakamura (Nara Institute of Science and Technology, Japan) Yu Takahashi, Kazunobu Kondo (Yamaha Corporation, Japan) Hirokazu Kameoka (The University of Tokyo, Japan)

Outline • 1. Research background • 2. Conventional methods – – – – Nonnegative matrix factorization Supervised nonnegative matrix factorization Directional clustering Hybrid method • 3. Proposed method – Online divergence switching for hybrid method • 4. Experiments • 5. Conclusions 2

Outline • 1. Research background • 2. Conventional methods – – – – Nonnegative matrix factorization Supervised nonnegative matrix factorization Directional clustering Hybrid method • 3. Proposed method – Online divergence switching for hybrid method • 4. Experiments • 5. Conclusions 3

Research background • Music signal separation technologies have received much attention. Applications • Automatic music transcription • 3D audio system, etc. • Music signal separation based on nonnegative matrix factorization (NMF) is a very active research area. • The separation performance of supervised NMF (SNMF) markedly degrades for the case of many source mixtures. We have been proposed a new hybrid separation method for stereo music signals. 4

Research background • Our proposed hybrid method Input stereo signal Spatial separation method (Directional clustering) SNMF-based separation method (Superresolution-based SNMF) Separated signal 5

Research background • Optimal divergence criterion in superresolution-based SNMF depends on the spatial conditions of the input signal. • Our aim in this presentation We propose a new optimal separation scheme for this hybrid method to separate the target signal with high accuracy for any types of the spatial condition. 6

Outline • 1. Research background • 2. Conventional methods – – – – Nonnegative matrix factorization Supervised nonnegative matrix factorization Directional clustering Hybrid method • 3. Proposed method – Online divergence switching for hybrid method • 4. Experiments • 5. Conclusions 7

NMF [Lee, et al., 2001] • NMF – is a sparse representation algorithm. – can extract significant features from the observed matrix. Frequency Amplitude Basis matrix Activation matrix (spectral patterns) (Time-varying gain) Frequency Observed matrix (spectrogram) Time Amplitude Time Basis Ω: Number of frequency bins

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