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Electrical Engineering and Systems Science > Audio and Speech Processing

arXiv:1910.09522 (eess)
[Submitted on 21 Oct 2019]

Title:Comparative Study between Adversarial Networks and Classical Techniques for Speech Enhancement

Authors:Tito Spadini, Ricardo Suyama
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Abstract:Speech enhancement is a crucial task for several applications. Among the most explored techniques are the Wiener filter and the LogMMSE, but approaches exploring deep learning adapted to this task, such as SEGAN, have presented relevant results. This study compared the performance of the mentioned techniques in 85 noise conditions regarding quality, intelligibility, and distortion; and concluded that classical techniques continue to exhibit superior results for most scenarios, but, in severe noise scenarios, SEGAN performed better and with lower variance.
Subjects: Audio and Speech Processing (eess.AS); Machine Learning (cs.LG)
Cite as: arXiv:1910.09522 [eess.AS]
  (or arXiv:1910.09522v1 [eess.AS] for this version)
  https://doi.org/10.48550/arXiv.1910.09522

Submission history

From: Tito Spadini [view email]
[v1] Mon, 21 Oct 2019 17:28:12 UTC (253 KB)
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