IEICE TRANSACTIONS on Fundamentals
Automatic Model Order Selection for Convolutive Non-Negative Matrix Factorization
Summary :
Exploring a parsimonious model that is just enough to represent the temporal dependency of time serial signals such as audio or speech is a practical requirement for many signal processing applications. A well suited method for intuitively and efficiently representing magnitude spectra is to use convolutive non-negative matrix factorization (CNMF) to discover the temporal relationship among nearby frames. However, the model order selection problem in CNMF, i.e., the choice of the number of convolutive bases, has seldom been investigated ever. In this paper, we propose a novel Bayesian framework that can automatically learn the optimal model order through maximum a posteriori (MAP) estimation. The proposed method yields a parsimonious and low-rank approximation by removing the redundant bases iteratively. We conducted intuitive experiments to show that the proposed algorithm is very effective in automatically determining the correct model order.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E99-A No.10 pp.1867-1870
- Publication Date
- 2016/10/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E99.A.1867
- Type of Manuscript
- LETTER
- Category
- Speech and Hearing
Authors
Yinan LI
PLA University of Science and Technology
Xiongwei ZHANG
PLA University of Science and Technology
Meng SUN
PLA University of Science and Technology
Chong JIA
PLA University of Science and Technology
Xia ZOU
PLA University of Science and Technology
Keyword
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Yinan LI, Xiongwei ZHANG, Meng SUN, Chong JIA, Xia ZOU, "Automatic Model Order Selection for Convolutive Non-Negative Matrix Factorization" in IEICE TRANSACTIONS on Fundamentals,
vol. E99-A, no. 10, pp. 1867-1870, October 2016, doi: 10.1587/transfun.E99.A.1867.
Abstract: Exploring a parsimonious model that is just enough to represent the temporal dependency of time serial signals such as audio or speech is a practical requirement for many signal processing applications. A well suited method for intuitively and efficiently representing magnitude spectra is to use convolutive non-negative matrix factorization (CNMF) to discover the temporal relationship among nearby frames. However, the model order selection problem in CNMF, i.e., the choice of the number of convolutive bases, has seldom been investigated ever. In this paper, we propose a novel Bayesian framework that can automatically learn the optimal model order through maximum a posteriori (MAP) estimation. The proposed method yields a parsimonious and low-rank approximation by removing the redundant bases iteratively. We conducted intuitive experiments to show that the proposed algorithm is very effective in automatically determining the correct model order.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E99.A.1867/_p
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@ARTICLE{e99-a_10_1867,
author={Yinan LI, Xiongwei ZHANG, Meng SUN, Chong JIA, Xia ZOU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Automatic Model Order Selection for Convolutive Non-Negative Matrix Factorization},
year={2016},
volume={E99-A},
number={10},
pages={1867-1870},
abstract={Exploring a parsimonious model that is just enough to represent the temporal dependency of time serial signals such as audio or speech is a practical requirement for many signal processing applications. A well suited method for intuitively and efficiently representing magnitude spectra is to use convolutive non-negative matrix factorization (CNMF) to discover the temporal relationship among nearby frames. However, the model order selection problem in CNMF, i.e., the choice of the number of convolutive bases, has seldom been investigated ever. In this paper, we propose a novel Bayesian framework that can automatically learn the optimal model order through maximum a posteriori (MAP) estimation. The proposed method yields a parsimonious and low-rank approximation by removing the redundant bases iteratively. We conducted intuitive experiments to show that the proposed algorithm is very effective in automatically determining the correct model order.},
keywords={},
doi={10.1587/transfun.E99.A.1867},
ISSN={1745-1337},
month={October},}
Copy
TY - JOUR
TI - Automatic Model Order Selection for Convolutive Non-Negative Matrix Factorization
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1867
EP - 1870
AU - Yinan LI
AU - Xiongwei ZHANG
AU - Meng SUN
AU - Chong JIA
AU - Xia ZOU
PY - 2016
DO - 10.1587/transfun.E99.A.1867
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E99-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 2016
AB - Exploring a parsimonious model that is just enough to represent the temporal dependency of time serial signals such as audio or speech is a practical requirement for many signal processing applications. A well suited method for intuitively and efficiently representing magnitude spectra is to use convolutive non-negative matrix factorization (CNMF) to discover the temporal relationship among nearby frames. However, the model order selection problem in CNMF, i.e., the choice of the number of convolutive bases, has seldom been investigated ever. In this paper, we propose a novel Bayesian framework that can automatically learn the optimal model order through maximum a posteriori (MAP) estimation. The proposed method yields a parsimonious and low-rank approximation by removing the redundant bases iteratively. We conducted intuitive experiments to show that the proposed algorithm is very effective in automatically determining the correct model order.
ER -
English
