AeGAN: Time-Frequency Speech Denoising via Generative Adversarial Networks
Abstract
Automatic speech recognition (ASR) systems are of vital importance nowadays in commonplace tasks such as speech-to-text processing and language translation. This created the need for an ASR system that can operate in realistic crowded environments. Thus, speech enhancement is a valuable building block in ASR systems and other applications such as hearing aids, smartphones and teleconferencing systems. In this paper, a generative adversarial network (GAN) based framework is investigated for the task of speech enhancement, more specifically speech denoising of audio tracks. A new architecture based on CasNet generator and an additional feature-based loss are incorporated to get realistically denoised speech phonetics. Finally, the proposed framework is shown to outperform other learning and traditional model-based speech enhancement approaches.
Cite
@article{arxiv.1910.12620,
title = {AeGAN: Time-Frequency Speech Denoising via Generative Adversarial Networks},
author = {Sherif Abdulatif and Karim Armanious and Karim Guirguis and Jayasankar T. Sajeev and Bin Yang},
journal= {arXiv preprint arXiv:1910.12620},
year = {2020}
}
Comments
5 pages, 4 figures and 2 Tables. Accepted in EUSIPCO 2020