SPARSITY AND LOW-RANK AMPLITUDE BASED BLIND SOURCE SEPARATION

被引：0

作者：

Feng, Fangchen ^{[1
]}

Kowalski, Matthieu ^{[1
]}

机构：

[1] Univ Paris Saclay, Univ Paris Sud, L2S, CNRS,CENT SUPELEC, Gif Sur Yvette, France

来源：

2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2017年

关键词：

Blind source separation; sparsity; low-rank; multichannel non-negative matrix factorization; NONNEGATIVE MATRIX FACTORIZATION; AUDIO SOURCE SEPARATION; MIXTURES;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

This paper presents a new method for blind source separation problem in reverberant environments with more sources than microphones. Based on the sparsity property in the time-frequency domain and the low-rank assumption of the spectrogram of the source, the STRAUSS (SparsiTy and low-Rank AmplitUde based Source Separation) method is developed. Numerical evaluations show that the proposed method outperforms the existing multichannel NMF approaches, while it is exclusively based on amplitude information.

引用

页码：571 / 575

页数：5

共 50 条

[41] A DIAGONAL PLUS LOW-RANK COVARIANCE MODEL FOR COMPUTATIONALLY EFFICIENT SOURCE SEPARATION
Liutkus, Antoine
Yoshii, Kazuyoshi
[J]. 2017 IEEE 27TH INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING, 2017,
[42] Low-Rank PSD Approximation in Input-Sparsity Time
Clarkson, Kenneth L.
Woodruff, David P.
[J]. PROCEEDINGS OF THE TWENTY-EIGHTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, 2017, : 2061 - 2072
[43] Is Input Sparsity Time Possible for Kernel Low-Rank Approximation?
Musco, Cameron
Woodruff, David P.
[J]. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017), 2017, 30
[44] Unsupervised feature extraction by low-rank and sparsity preserving embedding
Zhan, Shanhua
Wu, Jigang
Han, Na
Wen, Jie
Fang, Xiaozhao
[J]. NEURAL NETWORKS, 2019, 109 : 56 - 66
[45] RECONCILIATION OF GROUP SPARSITY AND LOW-RANK MODELS FOR IMAGE RESTORATION
Zha, Zhiyuan
Wen, Bihan
Yuan, Xin
Zhou, Jiantao
Zhu, Ce
[J]. 2020 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME), 2020,
[46] Accelerated MR parameter mapping with low-rank and sparsity constraints
Zhao, Bo
Lu, Wenmiao
Hitchens, T. Kevin
Lam, Fan
Ho, Chien
Liang, Zhi-Pei
[J]. MAGNETIC RESONANCE IN MEDICINE, 2015, 74 (02) : 489 - 498
[47] MULTIVARIATE LINEAR REGRESSION WITH LOW-RANK AND ROW-SPARSITY
SUN, Jun
Shang, Pan
Xu, Qiuyun
Chen, Bingzhen
[J]. PACIFIC JOURNAL OF OPTIMIZATION, 2022, 18 (02): : 349 - 366
[48] Learning Low-Rank Structured Sparsity in Recurrent Neural Networks
Wen, Weijing
Yang, Fan
Su, Yangfeng
Zhou, Dian
Zeng, Xuan
[J]. 2020 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS), 2020,
[49] A Non-local Low-rank and Sparsity based Framework for Depth Map Inpainting
Jonna, Sankaraganesh
Medhi, Moushumi
[J]. 2021 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE), 2021,
[50] LOW-RANK BLIND NONNEGATIVE MATRIX DECONVOLUTION
Anh Huy Phan
Tichavsky, Petr
Cichocki, Andrzej
Koldovsky, Zbynek
[J]. 2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2012, : 1893 - 1896

← 1 2 3 4 5 →