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Monaural speech segregation in rever...

Jin, Zhaozhang.

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Monaural speech segregation in reverberant environments.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Monaural speech segregation in reverberant environments./
作者:	Jin, Zhaozhang.
面頁冊數:	155 p.
附註:	Source: Dissertation Abstracts International, Volume: 71-12, Section: B, page: 7598.
Contained By:	Dissertation Abstracts International71-12B.
標題:	Engineering, Computer. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3429645
ISBN:	9781124292212

Monaural speech segregation in reverberant environments.
Jin, Zhaozhang.

Monaural speech segregation in reverberant environments. - 155 p.

Source: Dissertation Abstracts International, Volume: 71-12, Section: B, page: 7598.

Thesis (Ph.D.)--The Ohio State University, 2010.

Room reverberation is a major source of signal degradation in real environments. While listeners excel in "hearing out" a target source from sound mixtures in noisy and reverberant conditions, simulating this perceptual ability remains a fundamental challenge. The goal of this dissertation is to build a computational auditory scene analysis (CASA) system that separates target voiced speech from its acoustic background in reverberant environments. A supervised learning approach to pitch-based grouping of reverberant speech is proposed, followed by a robust multipitch tracking algorithm based on a hidden Markov model (HMM) framework. Finally, a monaural CASA system for reverberant speech segregation is designed by combining the supervised learning approach and the multipitch tracker.

ISBN: 9781124292212Subjects--Topical Terms:

1669061
Engineering, Computer.

Monaural speech segregation in reverberant environments.
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500 $a Source: Dissertation Abstracts International, Volume: 71-12, Section: B, page: 7598.
500 $a Adviser: DeLiang Wang.
502 $a Thesis (Ph.D.)--The Ohio State University, 2010.
520 $a Room reverberation is a major source of signal degradation in real environments. While listeners excel in "hearing out" a target source from sound mixtures in noisy and reverberant conditions, simulating this perceptual ability remains a fundamental challenge. The goal of this dissertation is to build a computational auditory scene analysis (CASA) system that separates target voiced speech from its acoustic background in reverberant environments. A supervised learning approach to pitch-based grouping of reverberant speech is proposed, followed by a robust multipitch tracking algorithm based on a hidden Markov model (HMM) framework. Finally, a monaural CASA system for reverberant speech segregation is designed by combining the supervised learning approach and the multipitch tracker.
520 $a Monaural speech segregation in reverberant environments is a particularly challenging problem. Although inverse filtering has been proposed to partially restore the harmonicity of reverberant speech before segregation, this approach is sensitive to specific source/receiver and room configurations. Assuming that the true target pitch is known, our first study lends to a novel supervised learning approach to monaural segregation of reverberant voiced speech, which learns to map a set of pitch-based auditory features to a grouping cue encoding the posterior probability of a time-frequency (T-F) unit being target dominant given observed features. We devise a novel objective function for the learning process, which directly relates to the goal of maximizing signal-to-noise ratio. The model trained using this objective function yields significantly better T-F unit labeling. A segmentation and grouping framework is utilized to form reliable segments under reverberant conditions and organize them into streams. Systematic evaluations show that our approach produces very promising results under various reverberant conditions and generalizes well to new utterances and new speakers.
520 $a Multipitch tracking in real environments is critical for speech signal processing. Determining pitch in both reverberant and noisy conditions is another difficult task. In the second study, we propose a robust algorithm for multipitch tracking in the presence of background noise and room reverberation. A new channel selection method is utilized to extract periodicity features. We derive pitch scores for each pitch state, which estimate the likelihoods of the observed periodicity features given pitch candidates. An HMM integrates these pitch scores and searches for the best pitch state sequence. Our algorithm can reliably detect single and double pitch contours in noisy and reverberant conditions.
520 $a Building on the first two studies, we propose a CASA approach to monaural segregation of reverberant voiced speech, which performs multipitch tracking of reverberant mixtures and supervised classification. Speech and nonspeech models are separately trained, and each learns to map pitch-based features to the posterior probability of a T-F unit being dominated by the source with the given pitch estimate. Because interference can be either speech or nonspeech, a likelihood ratio test is introduced to select the correct model for labeling corresponding T-F units. Experimental results show that the proposed system performs robustly in different types of interference and various reverberant conditions, and has a significant advantage over existing systems.
590 $a School code: 0168.
650 4 $a Engineering, Computer. $3 1669061
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710 2 $a The Ohio State University. $3 718944
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790 1 0 $a Wang, DeLiang, $e advisor
790 $a 0168
791 $a Ph.D.
792 $a 2010
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