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PyDTS: A Python Toolkit for Deep Learning Time Series Modelling. [PDF]

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Schirmer PA, Mporas I.
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Underdetermined Blind Source Separation Using Sparse Coding

IEEE Transactions on Neural Networks and Learning Systems, 2017
In an underdetermined mixture system with unknown sources, it is a challenging task to separate these sources from their observed mixture signals, where . By exploiting the technique of sparse coding, we propose an effective approach to discover some 1-D subspaces from the set consisting of all the time-frequency (TF) representation vectors of observed
Liangli Zhen, Dezhong Peng, Zhang Yi, Yong Xiang, Peng Chen   +4 more
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Time-Frequency Approach to Underdetermined Blind Source Separation

IEEE Transactions on Neural Networks and Learning Systems, 2012
This paper presents a new time-frequency (TF) underdetermined blind source separation approach based on Wigner-Ville distribution (WVD) and Khatri-Rao product to separate N non-stationary sources from M(M
Shengli, Xie, Liu, Yang, Jun-Mei, Yang, Guoxu, Zhou, Yong, Xiang   +4 more
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Underdetermined blind source separation based on sparse representation

IEEE Transactions on Signal Processing, 2006
This paper discusses underdetermined (i.e., with more sources than sensors) blind source separation (BSS) using a two-stage sparse representation approach. The first challenging task of this approach is to estimate precisely the unknown mixing matrix. In this paper, an algorithm for estimating the mixing matrix that can be viewed as an extension of the
null Yuanqing Li, S. Amari, A. Cichocki, D.W.C. Ho, null Shengli Xie   +4 more
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Underdetermined Blind Source Separation Based on Relaxed Sparsity Condition of Sources

IEEE Transactions on Signal Processing, 2009
Recently, Aissa-El-Bey et al. have proposed two subspace-based methods for underdetermined blind source separation (UBSS) in time-frequency (TF) domain. These methods allow multiple active sources at TF points so long as the number of active sources at any TF point is strictly less than the number of sensors, and the column vectors of the mixing matrix
null Dezhong Peng, null Yong Xiang
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Underdetermined Blind Source Separation of Bioacoustic Signals

Pertanika Journal of Science and Technology, 2023
Bioacoustic signals have been used as a modality in environmental monitoring and biodiversity research. These signals also carry species or individual information, thus allowing the recognition of species and individuals based on vocals. Nevertheless, vocal communication in a crowded social environment is a challenging problem for automated bioacoustic
Norsalina Hassan, Dzati Athiar Ramli
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Underdetermined Blind Source Separation in Reverberant Environment

2020 International Conference on Wireless Communications and Signal Processing (WCSP), 2020
This work studies a blind source separation method for convolutive mixtures in an underdetermined case. Due to strong reverberation, the mixing model still presents convolution property even in the time-frequency (TF) domain. Based on the convolutive model in TF domain, a blind source separation is developed, and the resulting optimization problem is ...
Shuai Li, Hongqing Liu, Gan Lu, Yi Zhou
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Underdetermined blind source separation using CapsNet

Soft Computing, 2019
In this paper, we consider the problem of separating the speech source signal from the underdetermined convolutive mixture signals using capsule network (CapsNet). The objective of this paper is twofold. They are (1) to improve the underdetermined convolutive blind source separation algorithm in terms of signal-to-distortion ratio, signal-to ...
M. Kumar, V. E. Jayanthi
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Improved DUET for underdetermined blind source separation

SPIE Proceedings, 2011
The source recovery step in underdetermined blind source separation is probed in this paper. DUET is famous method in underdetermined blind source separation. However, it requires the sources are approximately disjoint. To obtain good results in sources recovery, an improved DUET algorithm is proposed for two mixtures. The algorithm utilizes DUET after
Feng Gao, Gongxian Sun, Ming Xiao
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