A fast fixed-point algorithm for independent component analysis
Neural Computation
Neural Computation
Discrete Time Processing of Speech Signals
Discrete Time Processing of Speech Signals
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Our work applies a unifying Bayesian-Independent Component Analysis (BICA) framework in the context of speech enhancement and robust Automatic Speech Recognition (ASR). The corrupted speech waveform is reshaped in overlapping speech flames, and is assumed to be composed as a linear sum of the underlying clean speech and noise. Subsequently, a linear sum of latent independent functions is proposed to span each clean flame. Two different techniques are applied following a Bayesian formulation: In the first case the posterior probability of a clean speech flame is formed conditioned on the noisy one on which a maximum a posteriori (MAP) approach is applied, leading to Sparse Code Shrinkage (SCS) - a fairly new statistical technique originally presented to applied mathematics and image denoising, but its much promising potential for speech enhancement has not yet been exploited. In the second case, viewed within the Variational Bayes framework, the model for noisy speech generation is stated ill a block-based fashion as a noisy, blind source separation problem from which we infer the independent basis functions that span the space of a speech fame and their mixing matrix, thus reconstructing directly the corresponding clean frames.