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Time-domain speech enhancement using generative adversarial networks

Authors :
Universitat Politècnica de Catalunya. Doctorat en Teoria del Senyal i Comunicacions
Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla
Pascual de la Puente, Santiago
Serra, Joan
Bonafonte Cávez, Antonio
Universitat Politècnica de Catalunya. Doctorat en Teoria del Senyal i Comunicacions
Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla
Pascual de la Puente, Santiago
Serra, Joan
Bonafonte Cávez, Antonio
Publication Year :
2019

Abstract

Speech enhancement improves recorded voice utterances to eliminate noise that might be impeding their intelligibility or compromising their quality. Typical speech enhancement systems are based on regression approaches that subtract noise or predict clean signals. Most of them do not operate directly on waveforms. In this work, we propose a generative approach to regenerate corrupted signals into a clean version by using generative adversarial networks on the raw signal. We also explore several variations of the proposed system, obtaining insights into proper architectural choices for an adversarially trained, convolutional autoencoder applied to speech. We conduct both objective and subjective evaluations to assess the performance of the proposed method. The former helps us choose among variations and better tune hyperparameters, while the latter is used in a listening experiment with 42 subjects, confirming the effectiveness of the approach in the real world. We also demonstrate the applicability of the approach for more generalized speech enhancement, where we have to regenerate voices from whispered signals.<br />Peer Reviewed<br />Postprint (author's final draft)

Details

Database :
OAIster
Notes :
12 p., application/pdf, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1151825459
Document Type :
Electronic Resource