Amin Fazel

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Many source separation algorithms fail to deliver robust performance when applied to signals recorded using high-density microphone arrays where distance between sensor elements is much smaller than the wavelength of the signal of interest. This can be attributed to limited dynamic range (determined by analog-to-digital conversion) of the sensor which is(More)
—In this paper, we present a novel speech feature extraction algorithm based on a hierarchical combination of auditory similarity and pooling functions. The computationally efficient features known as " Sparse Auditory Reproducing Kernel " (SPARK) coefficients are extracted under the hypothesis that the noise-robust information in speech signal is embedded(More)
— Many source separation algorithms fail to deliver robust performance in presence of artifacts introduced by cross-channel redundancy, non-homogeneous mixing and high-dimensionality of the input signal space. In this paper, we propose a novel framework that overcomes these limitations by integrating learning algorithms directly with the process of signal(More)
— In this paper, we present a non-linear filtering approach for extracting noise-robust speech features that can be used in a speaker verification task. At the core of the proposed approach is a time-series regression using Reproducing Kernel Hilbert Space (RKHS) based methods that extracts discriminatory non-linear signatures while filtering out the(More)
— The performance of acoustic source separation algorithms significantly degrades when they applied to signals recorded using miniature microphone arrays where the distances between the microphone elements are much smaller than the wavelength of acoustic signals. This can be attributed to limited dynamic range (determined by analog-to-digital conversion) of(More)
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