IEICE TRANSACTIONS on Information
Speech Quality Enhancement for In-Ear Microphone Based on Neural Network
Summary :
Speech captured by an in-ear microphone placed inside an occluded ear has a high signal-to-noise ratio; however, it has different sound characteristics compared to normal speech captured through air conduction. In this study, a method for blind speech quality enhancement is proposed that can convert speech captured by an in-ear microphone to one that resembles normal speech. The proposed method estimates an input-dependent enhancement function by using a neural network in the feature domain and enhances the captured speech via time-domain filtering. Subjective and objective evaluations confirm that the speech enhanced using our proposed method sounds more similar to normal speech than that enhanced using conventional equalizer-based methods.
- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.8 pp.1594-1597
- Publication Date
- 2019/08/01
- Publicized
- 2019/05/15
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018EDL8249
- Type of Manuscript
- LETTER
- Category
- Speech and Hearing
Authors
Hochong PARK
Kwangwoon University
Yong-Shik SHIN
RippleBuds Ltd.
Seong-Hyeon SHIN
Kwangwoon University
Keyword
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Hochong PARK, Yong-Shik SHIN, Seong-Hyeon SHIN, "Speech Quality Enhancement for In-Ear Microphone Based on Neural Network" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 8, pp. 1594-1597, August 2019, doi: 10.1587/transinf.2018EDL8249.
Abstract: Speech captured by an in-ear microphone placed inside an occluded ear has a high signal-to-noise ratio; however, it has different sound characteristics compared to normal speech captured through air conduction. In this study, a method for blind speech quality enhancement is proposed that can convert speech captured by an in-ear microphone to one that resembles normal speech. The proposed method estimates an input-dependent enhancement function by using a neural network in the feature domain and enhances the captured speech via time-domain filtering. Subjective and objective evaluations confirm that the speech enhanced using our proposed method sounds more similar to normal speech than that enhanced using conventional equalizer-based methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDL8249/_p
Copy
@ARTICLE{e102-d_8_1594,
author={Hochong PARK, Yong-Shik SHIN, Seong-Hyeon SHIN, },
journal={IEICE TRANSACTIONS on Information},
title={Speech Quality Enhancement for In-Ear Microphone Based on Neural Network},
year={2019},
volume={E102-D},
number={8},
pages={1594-1597},
abstract={Speech captured by an in-ear microphone placed inside an occluded ear has a high signal-to-noise ratio; however, it has different sound characteristics compared to normal speech captured through air conduction. In this study, a method for blind speech quality enhancement is proposed that can convert speech captured by an in-ear microphone to one that resembles normal speech. The proposed method estimates an input-dependent enhancement function by using a neural network in the feature domain and enhances the captured speech via time-domain filtering. Subjective and objective evaluations confirm that the speech enhanced using our proposed method sounds more similar to normal speech than that enhanced using conventional equalizer-based methods.},
keywords={},
doi={10.1587/transinf.2018EDL8249},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Speech Quality Enhancement for In-Ear Microphone Based on Neural Network
T2 - IEICE TRANSACTIONS on Information
SP - 1594
EP - 1597
AU - Hochong PARK
AU - Yong-Shik SHIN
AU - Seong-Hyeon SHIN
PY - 2019
DO - 10.1587/transinf.2018EDL8249
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2019
AB - Speech captured by an in-ear microphone placed inside an occluded ear has a high signal-to-noise ratio; however, it has different sound characteristics compared to normal speech captured through air conduction. In this study, a method for blind speech quality enhancement is proposed that can convert speech captured by an in-ear microphone to one that resembles normal speech. The proposed method estimates an input-dependent enhancement function by using a neural network in the feature domain and enhances the captured speech via time-domain filtering. Subjective and objective evaluations confirm that the speech enhanced using our proposed method sounds more similar to normal speech than that enhanced using conventional equalizer-based methods.
ER -
English
