Blind separation of audio signals using trigonometric transforms and wavelet denoising

This paper deals with the problem of blind separation of audio signals from noisy mixtures. It proposes the application of a blind separation algorithm on the discrete cosine transform (DCT) or the discrete sine transform (DST) of the mixed signals, instead of performing the separation on the mixtur...

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Bibliographic Details
Published in:International journal of speech technology 2010-03, Vol.13 (1), p.1-12
Main Authors: Hammam, Hossam, Elazm, Atef Abou, Elhalawany, Mohamed E., Abd El-Samie, Fathi E.
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Audio signals
Blinds
Engineering
Mathematical analysis
Separation
Signal,Image and Speech Processing
Social Sciences
Time domain
Transforms
Wavelet
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Summary:This paper deals with the problem of blind separation of audio signals from noisy mixtures. It proposes the application of a blind separation algorithm on the discrete cosine transform (DCT) or the discrete sine transform (DST) of the mixed signals, instead of performing the separation on the mixtures in the time domain. Wavelet denoising of the noisy mixtures is recommended in this paper as a preprocessing step for noise reduction. Both the DCT and the DST have an energy compaction property, which concentrates most of the signal energy in a few coefficients in the transform domain, leaving most of the transform domain coefficients close to zero. As a result, the separation is performed on a few coefficients in the transform domain. Another advantage of signal separation in transform domains is that the effect of noise on the signals in the transform domains is smaller than that in the time domain due to the averaging effect of the transform equations, especially when the separation algorithm is preceded by a wavelet denoising step. The simulation results confirm the superiority of transform domain separation to time domain separation and the importance of the wavelet denoising step.
ISSN:1381-2416
1572-8110
DOI:10.1007/s10772-010-9066-0