Search Results - "Fazel, Amin"
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1
CAD-AEC: Context-Aware Deep Acoustic Echo Cancellation
Published in ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (01-05-2020)“…Deep-leaming based acoustic echo cancellation (AEC) methods have been shown to outperform the classical techniques. The main drawback of the learning-based AEC…”
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Conference Proceeding -
2
Investigation on the effect of using oatmeal and date liquid sugar on the chemical and sensory properties of local Sabzevar cookies
Published in Ulūm va ṣanāyi̒-i ghaz̠āyī (01-12-2021)Get full text
Journal Article -
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Sparse Auditory Reproducing Kernel (SPARK) Features for Noise-Robust Speech Recognition
Published in IEEE transactions on audio, speech, and language processing (01-05-2012)“…In this paper, we present a novel speech feature extraction algorithm based on a hierarchical combination of auditory similarity and pooling functions. The…”
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Journal Article -
4
Far-field acoustic source localization and bearing estimation using Σ Δ learners
Published in IEEE transactions on circuits and systems. I, Regular papers (01-04-2010)“…Localization of acoustic sources using miniature microphone arrays poses a significant challenge due to fundamental limitations imposed by the physics of sound…”
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Journal Article -
5
Resolution Enhancement in S Learners for Superresolution Source Separation
Published in IEEE transactions on signal processing (01-03-2010)“…Many source separation algorithms fail to deliver robust performance when applied to signals recorded using high-density sensor arrays where the distance…”
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Journal Article -
6
Resolution Enhancement in IL I Learners for Superresolution Source Separation
Published in IEEE transactions on signal processing (01-03-2010)“…Many source separation algorithms fail to deliver robust performance when applied to signals recorded using high-density sensor arrays where the distance…”
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Journal Article -
7
Resolution Enhancement in [Formula Omitted] Learners for Superresolution Source Separation
Published in IEEE transactions on signal processing (01-03-2010)“…Many source separation algorithms fail to deliver robust performance when applied to signals recorded using high-density sensor arrays where the distance…”
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Journal Article -
8
Resolution Enhancement in ΣΔ Learners for Superresolution Source Separation
Published in IEEE transactions on signal processing (2010)Get full text
Journal Article -
9
Far-Field Acoustic Source Localization and Bearing Estimation Using [Formula Omitted] Learners
Published in IEEE transactions on circuits and systems. I, Regular papers (01-04-2010)“…Localization of acoustic sources using miniature microphone arrays poses a significant challenge due to fundamental limitations imposed by the physics of sound…”
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Journal Article -
10
Far-Field Acoustic Source Localization and Bearing Estimation Using capital sigma Delta Learners
Published in IEEE transactions on circuits and systems. I, Regular papers (01-04-2010)“…Localization of acoustic sources using miniature microphone arrays poses a significant challenge due to fundamental limitations imposed by the physics of sound…”
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Journal Article -
11
Sparse kernel cepstral coefficients (SKCC): Inner-product based features for noise-robust speech recognition
Published in 2011 IEEE International Symposium of Circuits and Systems (ISCAS) (01-05-2011)“…In this paper we present a novel speech feature extraction algorithm based on sparse auditory coding and regression techniques in a reproducing kernel Hilbert…”
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Conference Proceeding -
12
Resolution Enhancement in \Sigma\Delta Learners for Superresolution Source Separation
Published in IEEE transactions on signal processing (01-03-2010)“…Many source separation algorithms fail to deliver robust performance when applied to signals recorded using high-density sensor arrays where the distance…”
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Journal Article -
13
Robust signal processing methods for miniature acoustic sensing, separation, and recognition
Published 01-01-2012“…One of several emerging areas where micro-scale integration promises significant breakthroughs is in the field of acoustic sensing. However, separation,…”
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Dissertation -
14
Non-linear filtering in reproducing Kernel Hilbert Spaces for noise-robust speaker verification
Published in 2009 IEEE International Symposium on Circuits and Systems (ISCAS) (01-05-2009)“…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…”
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Conference Proceeding -
15
SynthASR: Unlocking Synthetic Data for Speech Recognition
Published 14-06-2021“…End-to-end (E2E) automatic speech recognition (ASR) models have recently demonstrated superior performance over the traditional hybrid ASR models. Training an…”
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Journal Article -
16
Sigma-delta resolution enhancement for far-field acoustic source separation
Published in 2008 IEEE International Conference on Acoustics, Speech and Signal Processing (01-03-2008)“…Many source separation algorithms fail to deliver robust performance when applied to signals recorded using high-density microphone arrays where distance…”
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Conference Proceeding -
17
Sigma-delta learning for super-resolution independent component analysis
Published in 2008 IEEE International Symposium on Circuits and Systems (01-01-2008)“…Many source separation algorithms fail to deliver robust performance in presence of artifacts introduced by cross-channel redundancy, non-homogeneous mixing…”
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Conference Proceeding Journal Article -
18
Robust signal processing methods for miniature acoustic sensing, separation, and recognition
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Dissertation -
19
Sigma-delta learning for super-resolution source separation on high-density microphone arrays
Published in 2010 IEEE International Symposium on Circuits and Systems (ISCAS) (01-05-2010)“…The performance of acoustic source separation algorithms significantly degrades when they applied to signals recorded using miniature microphone arrays where…”
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Conference Proceeding -
20
Benchmarking TinyML Systems: Challenges and Direction
Published 10-03-2020“…Recent advancements in ultra-low-power machine learning (TinyML) hardware promises to unlock an entirely new class of smart applications. However, continued…”
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Journal Article