Literature DB >> 23464040

Speech intelligibility in reverberation with ideal binary masking: effects of early reflections and signal-to-noise ratio threshold.

Nicoleta Roman1, John Woodruff.   

Abstract

Ideal binary masking is a signal processing technique that separates a desired signal from a mixture by retaining only the time-frequency units where the signal-to-noise ratio (SNR) exceeds a predetermined threshold. In reverberant conditions there are multiple possible definitions of the ideal binary mask in that one may choose to treat the target early reflections as either desired signal or noise. The ideal binary mask may therefore be parameterized by the reflection boundary, a predetermined division point between early and late reflections. Another important parameter is the local SNR threshold used in labeling the time-frequency units as either target or background. Two experiments were designed to assess the impact of these two parameters on speech intelligibility with ideal binary masking for normal-hearing listeners in reverberant conditions. Experiment 1 shows that in order to achieve intelligibility improvements only the early reflections should be preserved by the binary mask. Moreover, it shows that the effective SNR should be accounted for when deciding the local threshold optimal range. Experiment 2 shows that with long reverberation times, intelligibility improvements are only obtained when the reflection boundary is 100 ms or less. Also, the experiment suggests that binary masking can be used for dereverberation.

Mesh:

Year:  2013        PMID: 23464040     DOI: 10.1121/1.4789895

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


  6 in total

1.  The optimal threshold for removing noise from speech is similar across normal and impaired hearing-a time-frequency masking study.

Authors:  Eric W Healy; Jordan L Vasko; DeLiang Wang
Journal:  J Acoust Soc Am       Date:  2019-06       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.  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.  Comparison of a target-equalization-cancellation approach and a localization approach to source separation.

Authors:  Jing Mi; Matti Groll; H Steven Colburn
Journal:  J Acoust Soc Am       Date:  2017-11       Impact factor: 1.840

6.  Time-Frequency Masking in the Complex Domain for Speech Dereverberation and Denoising.

Authors:  Donald S Williamson; DeLiang Wang
Journal:  IEEE/ACM Trans Audio Speech Lang Process       Date:  2017-04-20
  6 in total

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