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2015, Blind source separation by multiresolution analysis using AMUSE algorithm
Algorithms for blind source separation have been extensively studied in the last years. This paper proposes the use of multiresolution analysis in three decomposition levels of the wavelet transform, such as a preprocessing step, and the AMUSE algorithm to separate the source signals in distinct levels of resolution. The results show that there is an improvement in the estimation of the signals and the of mixing matrix even in noisy environment if compared to the use of AMUSE only.
2008 International Conference on Wavelet Analysis and Pattern Recognition
Blind source separation using analytic wavelet transform2008 •
2011 •
In this work we proposed a new method that allows the blind source separation by the analysis of independent components known as FASTICA in the domain of Wavelet to observe his behavior on signs captured in a real environment. The problem that tries to be solved in Blind Source Separation (BSS) consists of recovering signs statistically independent. Nevertheless, certain difficulties appear when this system is applied to real signs, on the one hand the effect of the reverberation does that the mixtures gathered by the microphones are convolution mix; and on the other hand, these mixtures will not be totally independent. We did two experiments. With the first experiment we separated 2 audio signals with a very low percentage of error. With the second experiment we recorded 3 different audio sources with an array of 3 microphones, and then from one audio recorded source 3 signals were separated, we appreciate that in each source one signal was amplified and the other two signals were fallen down. From the results, the method that we proposed is able to separate from one mixed audio signal 2 or even 3 independent signals.
IET Signal Processing
Non-negative matrix factorisation for blind source separation in wavelet transform domain2015 •
Iet Signal Processing
Non-Negative Matrix Factorization for Blind Source Separation in Wavelet Transform Domain2014 •
2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings
Wavelet Based Independent Component Analysis for Multi-Channel Source Separation2006 •
We consider the problem of separating instantaneous mixtures of different sound sources in multi-channel audio signals. Several methods have been developed to solve this problem. Independent component analysis (ICA) is certainly the most known method and the most used. ICA exploits the non-Gaussianity of the sources in the mixtures. In this study, we propose an improved signal separation algorithm where
2004 •
Several contributions in literature have recently proposed techniques based on assumption of source sparsity in some representation domain to give a solution to the problem of blind source separation in the underdetermined case. This work investigates how to employ wavelet based sparse representation of signals in an already existing algorithm for the problem under study, in order to improve separability of sources, in comparison to application of short time Fourier transform. Different wavelet transforms are considered. Moreover, this approach allows to perform a suitable de-noising operation after the separation algorithm, by thresholding the wavelet coefficients corresponding to extracted sources. This occurs at a very low computational cost, resulting in a further improvement of source recovering when noise is present at mixture level. Experimental results confirm the effectiveness of what implemented.
2006 •
ABSTRACT This paper addresses the problem of blind source separation in the situation where the mixing process is dynamic. We first present a new ICA algorithm for the static mixing problem that exploits a wavelet representation of the signals. This outperforms standard ICA in our experiments thus allowing the unmixing to be estimated from a smaller number of samples.
Lecture Notes in Computer Science
Wavelet De-noising for Blind Source Separation in Noisy Mixtures2004 •
2008 •
2007 IEEE International Conference on Image Processing
Robust Blind Separation of Statistically Dependent Sources using Dual Tree Wavelets2007 •
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