Performance Evaluation of Directionally Constrained Filterbank ICA on Blind Source Separation of Noisy Observations
Separation performance of directionally constrained filter- bank ICA is evaluated in presence of noise with different spectral properties. Stationarity of mixing channels is exploited to introduce directional constraint on the adaptive subband separation networks of filterbank- based blind source separation approach. Directional constraints on demixing network improves separation of source signals from noisy convolved mixtures, when significant spectral overlap exists between the noise and the convolved mixtures. Observations corrupted with low frequency noises exhibit slight improvement in the separation performance as there is less spectral overlap. Initialization and constraining of subband demixing network in accordance to the spatial location of source signals results in faster convergence and effective permutation correction, irrespective, of the nature of additive noise.
KeywordsIndependent Component Analysis Speech Signal Independent Component Analysis Blind Source Separation Separation Performance
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