Amin Fazel

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Even though the subject of speaker verification has been investigated for several decades now, numerous challenges and new opportunities in robust recognition techniques are still being explored. In this overview paper we first provide a brief introduction to statistical pattern recognition techniques that are commonly used in speaker verification. This(More)
—Many source separation algorithms fail to deliver robust performance when applied to signals recorded using high-density sensor arrays where the distance between sensor elements is much less than the wavelength of the signals. This can be attributed to limited dynamic range (determined by analog-to-digital conversion) of the sensor which is insufficient to(More)
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)
—Localization of acoustic sources using miniature microphone arrays poses a significant challenge due to fundamental limitations imposed by the physics of sound propagation. With sub-wavelength distances between the microphones, resolving acute localization cues become difficult due to precision artifacts. In this paper we propose a framework which(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(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)
This paper introduces a fully automatic chromosome classification algorithm for Multicolor or Multiplex Fluorescence In-Situ Hybridization (M-FISH) images using Gaussian mixture model technique. M-FISH is a recently developed cellular imaging method for rapid detection of chromosomal abnormalities, where each chromosome is labeled with 5 dyes and(More)
This report describes a fully automatic chromosome classification algorithm for Mul-tiplex Fluorescence In-Situ Hybridization (M-FISH) images using Gaussian mixture model technique. M-FISH is a recently developed chromosome imaging method in which each chromosome is labeled with 5 dyes and is also counterstained with DAPI. The classification problem is(More)