3 June 2011 Filterbank-based independent component analysis for acoustic mixtures
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Independent component analysis (ICA) for acoustic mixtures has been a challenging problem due to very complex reverberation involved in real-world mixing environments. In an effort to overcome disadvantages of the conventional time domain and frequency domain approaches, this paper describes filterbank-based independent component analysis for acoustic mixtures. In this approach, input signals are split into subband signals and decimated. A simplified network performs ICA on the decimated signals, and finally independent components are synthesized. First, a uniform filterbank is employed in the approach for basic and simple derivation and implementation. The uniform-filterbank-based approach achieves better separation performance than the frequency domain approach and gives faster convergence speed with less computational complexity than the time domain approach. Since most of natural signals have exponentially or more steeply decreasing energy as the frequency increases, the spectral characteristics of natural signals introduce a Bark-scale filterbank which divides low frequency region minutely and high frequency region widely. The Bark-scale-filterbank-based approach shows faster convergence speed than the uniform-filterbank-based one because it has more whitened inputs in low frequency subbands. It also improves separation performance as it has enough data to train adaptive parameters exactly in high frequency subbands.
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Hyung-Min Park, "Filterbank-based independent component analysis for acoustic mixtures", Proc. SPIE 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX, 80580B (3 June 2011); doi: 10.1117/12.887334; https://doi.org/10.1117/12.887334

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