Single channel audio source separation

Authors:
Bin. Gao;W. L. Woo;S. S. Dlay
Affiliations:
School of Electrical, Electronic and Computer Engineering, University of Newcastle, Newcastle, United Kindom;School of Electrical, Electronic and Computer Engineering, University of Newcastle, Newcastle, United Kindom;School of Electrical, Electronic and Computer Engineering, University of Newcastle, Newcastle, United Kindom
Venue:
WSEAS Transactions on Signal Processing
Year:
2008

Citing 14
Cited 1

Blind source separation and deconvolution: the dynamic component analysis algorithm

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A maximum likelihood approach to single-channel source separation

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Independent Component Analysis: A Flexible Nonlinearity and Decorrelating Manifold Approach

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Computational Auditory Scene Analysis: Principles, Algorithms, and Applications

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General approach to blind source separation

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A family of frequency- and time-domain contrasts for blind separation of convolutive mixtures of temporally dependent signals

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Underdetermined blind source separation based on sparse representation

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Single-Channel Speech Separation Using Soft Mask Filtering

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Blind Source Separation of Postnonlinear Convolutive Mixture

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Monaural Speech Separation Based on Computational Auditory Scene Analysis and Objective Quality Assessment of Speech

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A Bayesian Approach for Blind Separation of Sparse Sources

IEEE Transactions on Audio, Speech, and Language Processing
An iterative inversion approach to blind source separation

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Monaural speech segregation based on pitch tracking and amplitude modulation

IEEE Transactions on Neural Networks
Nonlinear signal separation for multinonlinearity constrained mixing model

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Activity index variance as an indicator of the number of signal sources

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Abstract

Blind source separation is an advanced statistical tool that has found widespread use in many signal processing applications. However, the crux topic based on one channel audio source separation has not fully developed to enable its way to laboratory implementation. The main idea approach to single channel blind source separation is based on exploiting the inherent time structure of sources known as basis filters in time domain that encode the sources in a statistically efficient manner. This paper proposes a technique for separating single channel recording of audio mixture using a hybrid of maximum likelihood and maximum a posteriori estimators. In addition, the algorithm proposes a new approach that accounts for the time structure of the speech signals by encoding them into a set of basis filters that are characteristically the most significant.