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K. Yagi, Y. Takahashi, H. Saruwatari, K. Shikano, and K. Kondo, "Music Signal Separation by Orthogonality and Maximum-Distance Constrained Nonnegative Matrix Factorization with Target Signal Information," Paper 2-5, (2012 March.). doi: K. Yagi, Y. Takahashi, H. Saruwatari, K. Shikano, and K. Kondo, "Music Signal Separation by Orthogonality and Maximum-Distance Constrained Nonnegative Matrix Factorization with Target Signal Information," Paper 2-5, (2012 March.). doi:
Abstract: In this paper, we address the separation of multiple instrumental sources based on semi-supervised nonnegative matrix factorization (SNMF) and propose a new constrained SNMF. Recently, various types of SNMF have been proposed. In particular, we focus our attention on one type of SNMF that utilizes information on a priori bases. Indeed, this type of SNMF can achieve better separation performance. However, SNMF without are any constraint between a priori bases and other bases often degrades separation performance. Thus, we propose a new SNMF that imposes a constraint between a priori bases and other bases. An experimental result shows the efficacy of the proposed constrained SNMF.

@article{yagi2012music,
author={yagi, kosuke and takahashi, yu and saruwatari, hiroshi and shikano, kiyohiro and kondo, kazunobu},
journal={journal of the audio engineering society},
title={music signal separation by orthogonality and maximum-distance constrained nonnegative matrix factorization with target signal information},
year={2012},
volume={},
number={},
pages={},
doi={},
month={march},} @article{yagi2012music,
author={yagi, kosuke and takahashi, yu and saruwatari, hiroshi and shikano, kiyohiro and kondo, kazunobu},
journal={journal of the audio engineering society},
title={music signal separation by orthogonality and maximum-distance constrained nonnegative matrix factorization with target signal information},
year={2012},
volume={},
number={},
pages={},
doi={},
month={march},
abstract={in this paper, we address the separation of multiple instrumental sources based on semi-supervised nonnegative matrix factorization (snmf) and propose a new constrained snmf. recently, various types of snmf have been proposed. in particular, we focus our attention on one type of snmf that utilizes information on a priori bases. indeed, this type of snmf can achieve better separation performance. however, snmf without are any constraint between a priori bases and other bases often degrades separation performance. thus, we propose a new snmf that imposes a constraint between a priori bases and other bases. an experimental result shows the efficacy of the proposed constrained snmf.},}

TY - paper
TI - Music Signal Separation by Orthogonality and Maximum-Distance Constrained Nonnegative Matrix Factorization with Target Signal Information
SP - EP -
AU - Yagi, Kosuke
AU - Takahashi, Yu
AU - Saruwatari, Hiroshi
AU - Shikano, Kiyohiro
AU - Kondo, Kazunobu
PY - 2012
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - March 2012 TY - paper
TI - Music Signal Separation by Orthogonality and Maximum-Distance Constrained Nonnegative Matrix Factorization with Target Signal Information
SP - EP -
AU - Yagi, Kosuke
AU - Takahashi, Yu
AU - Saruwatari, Hiroshi
AU - Shikano, Kiyohiro
AU - Kondo, Kazunobu
PY - 2012
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - March 2012
AB - In this paper, we address the separation of multiple instrumental sources based on semi-supervised nonnegative matrix factorization (SNMF) and propose a new constrained SNMF. Recently, various types of SNMF have been proposed. In particular, we focus our attention on one type of SNMF that utilizes information on a priori bases. Indeed, this type of SNMF can achieve better separation performance. However, SNMF without are any constraint between a priori bases and other bases often degrades separation performance. Thus, we propose a new SNMF that imposes a constraint between a priori bases and other bases. An experimental result shows the efficacy of the proposed constrained SNMF.