Deep learning-based speech presence probability estimation for noise PSD estimation in single-channel speech enhancement

Haemin Yang, Soyeon Choe, Keulbit Kim, Hong Goo Kang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In single-channel speech enhancement, it is essential to determine noise reduction factors to successfully remove noise while minimizing speech distortion. These factors are typically set by a function of noise power spectral density (PSD) in time-frequency domain, and the state-of-the-art algorithm also introduces additional processes to estimate speech presence probability (SPP) to further enhance the estimation. Due to many tuning parameters, however, it is not easy to implement an algorithm that reliably estimates SPP in noise varying environment. We proposed a combination of deep learning network and an effective training method to enhance the performance of the SPP estimation module. The proposed approach is regarded as a hybrid approach, with the noise reduction factor still estimated by the conventional statistic-based single channel enhancement algorithms. The advantages and disadvantages of the proposed approach compared to deep learning approach of single channel speech enhancement are also investigated.

Original languageEnglish
Title of host publication2018 International Conference on Signals and Systems, ICSigSys 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages267-270
Number of pages4
ISBN (Electronic)9781538656891
DOIs
Publication statusPublished - 2018 Jun 4
Event2nd International Conference on Signals and Systems, ICSigSys 2018 - Bali, Indonesia
Duration: 2018 May 12018 May 3

Publication series

Name2018 International Conference on Signals and Systems, ICSigSys 2018 - Proceedings

Conference

Conference2nd International Conference on Signals and Systems, ICSigSys 2018
Country/TerritoryIndonesia
CityBali
Period18/5/118/5/3

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science and ICT (2017-11-0469).

Funding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea( NRF) funded by the Ministry of Science and ICT (2017- 11-0469).

Publisher Copyright:
© 2018 IEEE.

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Radiology Nuclear Medicine and imaging
  • Instrumentation

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