Adversarial autoencoder for reducing nonlinear distortion

Naohiro Tawara, Tetsunori Kobayashi, Masaru Fujieda, Kazuhiro Katagiri, Takashi Yazu, Tetsuji Ogawa

研究成果: Conference contribution

2 引用 (Scopus)

抜粋

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.

元の言語English
ホスト出版物のタイトル2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings
出版者Institute of Electrical and Electronics Engineers Inc.
ページ1669-1673
ページ数5
ISBN(電子版)9789881476852
DOI
出版物ステータスPublished - 2019 3 4
イベント10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Honolulu, United States
継続期間: 2018 11 122018 11 15

出版物シリーズ

名前2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings

Conference

Conference10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018
United States
Honolulu
期間18/11/1218/11/15

ASJC Scopus subject areas

  • Information Systems

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  • これを引用

    Tawara, N., Kobayashi, T., Fujieda, M., Katagiri, K., Yazu, T., & Ogawa, T. (2019). Adversarial autoencoder for reducing nonlinear distortion. : 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings (pp. 1669-1673). [8659540] (2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/APSIPA.2018.8659540