Literature DB >> 21973369

Intelligibility of reverberant noisy speech with ideal binary masking.

Nicoleta Roman1, John Woodruff.   

Abstract

For a mixture of target speech and noise in anechoic conditions, the ideal binary mask is defined as follows: It selects the time-frequency units where target energy exceeds noise energy by a certain local threshold and cancels the other units. In this study, the definition of the ideal binary mask is extended to reverberant conditions. Given the division between early and late reflections in terms of speech intelligibility, three ideal binary masks can be defined: an ideal binary mask that uses the direct path of the target as the desired signal, an ideal binary mask that uses the direct path and early reflections of the target as the desired signal, and an ideal binary mask that uses the reverberant target as the desired signal. The effects of these ideal binary mask definitions on speech intelligibility are compared across two types of interference: speech shaped noise and concurrent female speech. As suggested by psychoacoustical studies, the ideal binary mask based on the direct path and early reflections of target speech outperforms the other masks as reverberation time increases and produces substantial reductions in terms of speech reception threshold for normal hearing listeners.
© 2011 Acoustical Society of America

Mesh:

Year:  2011        PMID: 21973369     DOI: 10.1121/1.3631668

Source DB:  PubMed          Journal:  J Acoust Soc Am        ISSN: 0001-4966            Impact factor:   1.840


  5 in total

1.  Simultaneous suppression of noise and reverberation in cochlear implants using a ratio masking strategy.

Authors:  Oldooz Hazrati; Seyed Omid Sadjadi; Philipos C Loizou; John H L Hansen
Journal:  J Acoust Soc Am       Date:  2013-11       Impact factor: 1.840

2.  A deep learning algorithm to increase intelligibility for hearing-impaired listeners in the presence of a competing talker and reverberation.

Authors:  Eric W Healy; Masood Delfarah; Eric M Johnson; DeLiang Wang
Journal:  J Acoust Soc Am       Date:  2019-03       Impact factor: 1.840

3.  A deep learning based segregation algorithm to increase speech intelligibility for hearing-impaired listeners in reverberant-noisy conditions.

Authors:  Yan Zhao; DeLiang Wang; Eric M Johnson; Eric W Healy
Journal:  J Acoust Soc Am       Date:  2018-09       Impact factor: 1.840

4.  The role of binary mask patterns in automatic speech recognition in background noise.

Authors:  Arun Narayanan; DeLiang Wang
Journal:  J Acoust Soc Am       Date:  2013-05       Impact factor: 1.840

5.  Effects of early and late reflections on intelligibility of reverberated speech by cochlear implant listeners.

Authors:  Yi Hu; Kostas Kokkinakis
Journal:  J Acoust Soc Am       Date:  2014-01       Impact factor: 1.840

  5 in total

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