Nonnegative matrix factorization with Markov-Chained bases for modeling time-varying patterns in music spectrograms

Authors:
Masahiro Nakano;Jonathan Le Roux;Hirokazu Kameoka;Yu Kitano;Nobutaka Ono;Shigeki Sagayama
Affiliations:
Graduate School of Information Science and Technology, The University of Tokyo;NTT Communication Science Laboratories, NTT Corporation;NTT Communication Science Laboratories, NTT Corporation;Graduate School of Information Science and Technology, The University of Tokyo;Graduate School of Information Science and Technology, The University of Tokyo;Graduate School of Information Science and Technology, The University of Tokyo
Venue:
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Year:
2010

Citing 2
Cited 2

Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis

Neural Computation
Monaural Sound Source Separation by Nonnegative Matrix Factorization With Temporal Continuity and Sparseness Criteria

IEEE Transactions on Audio, Speech, and Language Processing

A non-negative approach to language informed speech separation

LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Temporally-Constrained convolutive probabilistic latent component analysis for multi-pitch detection

LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a new sparse representation for polyphonic music signals. The goal is to learn the time-varying spectral patterns of musical instruments, such as attack of the piano or vibrato of the violin in polyphonic music signals without any prior information. We model the spectrogram of music signals under the assumption that they are composed of a limited number of components which are composed of Markov-chained spectral patterns. The proposed model is an extension of nonnegative matrix factorization (NMF). An efficient algorithm is derived based on the auxiliary function method.