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Time-frequency masking strategies for single-channel low-latency speech enhancement using neural networks

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publication16th International Workshop on Acoustic Signal Enhancement, IWAENC 2018
PublisherIEEE
Pages51-55
Number of pages5
ISBN (Electronic)9781538681510
DOIs
Publication statusPublished - 2 Nov 2018
Publication typeA4 Article in a conference publication
EventInternational Workshop on Acoustic Signal Enhancement - Tokyo, Japan
Duration: 17 Sep 201820 Sep 2018

Conference

ConferenceInternational Workshop on Acoustic Signal Enhancement
CountryJapan
CityTokyo
Period17/09/1820/09/18

Abstract

This paper presents a low-latency neural network based speech enhancement system. Low-latency operation is critical for speech communication applications. The system uses the time-frequency (TF) masking approach to retain speech and remove the non-speech content from the observed signal. The ideal TF mask are obtained by supervised training of neural networks. As the main contribution different neural network models are experimentally compared to investigate computational complexity and speech enhancement performance. The proposed system is trained and tested on noisy speech data where signal-to-noise ratio (SNR) ranges from -5 dB to +5 dB and the results show significant reduction of non-speech content in the resulting signal while still meeting a low-latency operation criterion, which is here considered to be less than 20 ms.

Keywords

  • Neural networks, Speech enhancement

Publication forum classification

Field of science, Statistics Finland