Abstract
A novel post-filtering method using generative adversarial networks (GANs) is proposed to correct the effect of a nonlinear distortion caused by time-frequency (TF) masking. TF masking is a powerful framework for attenuating interfering sounds, but it can yield an unpleasant distortion of speech (e.g., a musical noise). A GAN-based autoencoder was recently shown to be effective for single-channel speech enhancement, however, using this technique for the post-processing of TF masking cannot help in nonlinear distortion reduction because some TF components are missing after TF-masking. Furthermore, the missing information is difficult embed using an autoencoder. In order to recover such missing components, an auxiliary reference signal that includes the target source components is concatenated with an enhanced signal, is then used as the input to the GAN-based autoencoder. Experimental comparisons show that the proposed post-filtering yields improvements in speech quality over TF-masking.
| Original language | English |
|---|---|
| Title of host publication | 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1669-1673 |
| Number of pages | 5 |
| ISBN (Electronic) | 9789881476852 |
| DOIs | |
| Publication status | Published - 2019 Mar 4 |
| Event | 10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Honolulu, United States Duration: 2018 Nov 12 → 2018 Nov 15 |
Publication series
| Name | 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings |
|---|
Conference
| Conference | 10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 |
|---|---|
| Country | United States |
| City | Honolulu |
| Period | 18/11/12 → 18/11/15 |
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ASJC Scopus subject areas
- Information Systems
Cite this
Adversarial autoencoder for reducing nonlinear distortion. / Tawara, Naohiro; Kobayashi, Tetsunori; Fujieda, Masaru; Katagiri, Kazuhiro; Yazu, Takashi; Ogawa, Tetsuji.
2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1669-1673 8659540 (2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Adversarial autoencoder for reducing nonlinear distortion
AU - Tawara, Naohiro
AU - Kobayashi, Tetsunori
AU - Fujieda, Masaru
AU - Katagiri, Kazuhiro
AU - Yazu, Takashi
AU - Ogawa, Tetsuji
PY - 2019/3/4
Y1 - 2019/3/4
N2 - A novel post-filtering method using generative adversarial networks (GANs) is proposed to correct the effect of a nonlinear distortion caused by time-frequency (TF) masking. TF masking is a powerful framework for attenuating interfering sounds, but it can yield an unpleasant distortion of speech (e.g., a musical noise). A GAN-based autoencoder was recently shown to be effective for single-channel speech enhancement, however, using this technique for the post-processing of TF masking cannot help in nonlinear distortion reduction because some TF components are missing after TF-masking. Furthermore, the missing information is difficult embed using an autoencoder. In order to recover such missing components, an auxiliary reference signal that includes the target source components is concatenated with an enhanced signal, is then used as the input to the GAN-based autoencoder. Experimental comparisons show that the proposed post-filtering yields improvements in speech quality over TF-masking.
AB - A novel post-filtering method using generative adversarial networks (GANs) is proposed to correct the effect of a nonlinear distortion caused by time-frequency (TF) masking. TF masking is a powerful framework for attenuating interfering sounds, but it can yield an unpleasant distortion of speech (e.g., a musical noise). A GAN-based autoencoder was recently shown to be effective for single-channel speech enhancement, however, using this technique for the post-processing of TF masking cannot help in nonlinear distortion reduction because some TF components are missing after TF-masking. Furthermore, the missing information is difficult embed using an autoencoder. In order to recover such missing components, an auxiliary reference signal that includes the target source components is concatenated with an enhanced signal, is then used as the input to the GAN-based autoencoder. Experimental comparisons show that the proposed post-filtering yields improvements in speech quality over TF-masking.
UR - http://www.scopus.com/inward/record.url?scp=85063472429&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063472429&partnerID=8YFLogxK
U2 - 10.23919/APSIPA.2018.8659540
DO - 10.23919/APSIPA.2018.8659540
M3 - Conference contribution
T3 - 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings
SP - 1669
EP - 1673
BT - 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -