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Rapid Assessment of Non-Verbal Auditory Perception in Normal-Hearing Participants and Cochlear Implant Users
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In: ISSN: 2077-0383 ; Journal of Clinical Medicine ; https://hal.archives-ouvertes.fr/hal-03413817 ; Journal of Clinical Medicine, MDPI, 2021, 10 (10), pp.2093. ⟨10.3390/jcm10102093⟩ (2021)
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Rapid Assessment of Non-Verbal Auditory Perception in Normal-Hearing Participants and Cochlear Implant Users
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In: ISSN: 2077-0383 ; Journal of Clinical Medicine ; https://hal.archives-ouvertes.fr/hal-03375356 ; Journal of Clinical Medicine, MDPI, 2021, 10 (10), pp.2093. ⟨10.3390/jcm10102093⟩ (2021)
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Sound source segregation of multiple concurrent talkers via Short-Time Target Cancellation
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Abstract:
The Short-Time Target Cancellation (STTC) algorithm, developed as part of this dissertation research, is a “Cocktail Party Problem” processor that can boost speech intelligibility for a target talker from a specified “look” direction, while suppressing the intelligibility of competing talkers. The algorithm holds promise for both automatic speech recognition and assistive listening device applications. The STTC algorithm operates on a frame-by-frame basis, leverages the computational efficiency of the Fast Fourier Transform (FFT), and is designed to run in real time. Notably, performance in objective measures of speech intelligibility and sound source segregation is comparable to that of the Ideal Binary Mask (IBM) and Ideal Ratio Mask (IRM). Because the STTC algorithm computes a time-frequency mask that can be applied independently to both the left and right signals, binaural cues for spatial hearing, including Interaural Time Differences (ITDs), Interaural Level Differences (ILDs) and spectral cues, can be preserved in potential hearing aid applications. A minimalist design for a proposed STTC Assistive Listening Device (ALD), consisting of six microphones embedded in the frame of a pair of eyeglasses, is presented and evaluated using virtual room acoustics and both objective and behavioral measures. The results suggest that the proposed STTC ALD can provide a significant speech intelligibility benefit in complex auditory scenes comprised of multiple spatially separated talkers. ; 2020-10-22T00:00:00Z
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Keyword:
Binaural hearing; Cocktail party problem; Computational auditory scene analysis; Electrical engineering; Hearing aid design; Signal processing; Spatial hearing
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URL: https://hdl.handle.net/2144/32082
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Complex acoustic environments: concepts, methods and auditory perception
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Weisser, Adam. - : Sydney, Australia : Macquarie University, 2018
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Early cortical metabolic rearrangement related to clinical data in idiopathic sudden sensorineural hearing loss
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Using Energy Difference for Speech Separation of Dual-microphone Close-talk System
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In: http://www.sensorsportal.com/HTML/DIGEST/may_2013/Special_issue/P_SI_353.pdf (2013)
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Cortical responses to changes in acoustic regularity are differentially modulated by attentional load
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Monaural speech separation and recognition challenge
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: Elsevier, 2011
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Monaural speech separation and recognition challenge
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In: ISSN: 0885-2308 ; EISSN: 1095-8363 ; Computer Speech and Language ; https://hal.archives-ouvertes.fr/hal-00598185 ; Computer Speech and Language, Elsevier, 2009, 24 (1), pp.1. ⟨10.1016/j.csl.2009.02.006⟩ (2009)
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Monaural Speech Segregation by Integrating Primitive and Schema-Based Analysis
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In: DTIC (2008)
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Hearing VS. Listening: Attention Changes the Neural Representations of Auditory Percepts
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A Computational Auditory Scene Analysis System for Speech Segregation and Robust Speech Recognition
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Isolating the Energetic Component of Speech-on-Speech Masking With Ideal Time-Frequency Segregation
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In: DTIC (2006)
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Speech recognition with amplitude and frequency modulations
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In: Zeng, F G; Nie, K; Stickney, G S; Kong, Y Y; Vongphoe, M; Bhargave, A; et al.(2005). Speech recognition with amplitude and frequency modulations. Proceedings of the National Academy of Sciences of the United States of America, 102(7), 2293 - 2298. UC Irvine: Retrieved from: http://www.escholarship.org/uc/item/1tn280m7 (2005)
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ARSTREAM: A Neural Network Model of Auditory Scene Analysis and Source Segregation
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