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

doi:10.1109/ICSIGSYS.2018.8372770

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

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 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 language	English
Title of host publication	2018 International Conference on Signals and Systems, ICSigSys 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	267-270
Number of pages	4
ISBN (Electronic)	9781538656891
DOIs	https://doi.org/10.1109/ICSIGSYS.2018.8372770
Publication status	Published - 2018 Jun 4
Event	2nd International Conference on Signals and Systems, ICSigSys 2018 - Bali, Indonesia Duration: 2018 May 1 → 2018 May 3

Publication series

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

Conference

Conference	2nd International Conference on Signals and Systems, ICSigSys 2018
Country/Territory	Indonesia
City	Bali
Period	18/5/1 → 18/5/3

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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10.1109/ICSIGSYS.2018.8372770

Cite this

Yang, H., Choe, S., Kim, K., & Kang, H. G. (2018). Deep learning-based speech presence probability estimation for noise PSD estimation in single-channel speech enhancement. In 2018 International Conference on Signals and Systems, ICSigSys 2018 - Proceedings (pp. 267-270). (2018 International Conference on Signals and Systems, ICSigSys 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSIGSYS.2018.8372770

Yang, Haemin ; Choe, Soyeon ; Kim, Keulbit et al. / Deep learning-based speech presence probability estimation for noise PSD estimation in single-channel speech enhancement. 2018 International Conference on Signals and Systems, ICSigSys 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 267-270 (2018 International Conference on Signals and Systems, ICSigSys 2018 - Proceedings).

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title = "Deep learning-based speech presence probability estimation for noise PSD estimation in single-channel speech enhancement",

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.",

author = "Haemin Yang and Soyeon Choe and Keulbit Kim and Kang, {Hong Goo}",

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: {\textcopyright} 2018 IEEE.; 2nd International Conference on Signals and Systems, ICSigSys 2018 ; Conference date: 01-05-2018 Through 03-05-2018",

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Yang, H, Choe, S, Kim, K & Kang, HG 2018, Deep learning-based speech presence probability estimation for noise PSD estimation in single-channel speech enhancement. in 2018 International Conference on Signals and Systems, ICSigSys 2018 - Proceedings. 2018 International Conference on Signals and Systems, ICSigSys 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 267-270, 2nd International Conference on Signals and Systems, ICSigSys 2018, Bali, Indonesia, 18/5/1. https://doi.org/10.1109/ICSIGSYS.2018.8372770

Deep learning-based speech presence probability estimation for noise PSD estimation in single-channel speech enhancement. / Yang, Haemin; Choe, Soyeon; Kim, Keulbit et al.
2018 International Conference on Signals and Systems, ICSigSys 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 267-270 (2018 International Conference on Signals and Systems, ICSigSys 2018 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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AU - Yang, Haemin

AU - Choe, Soyeon

AU - Kim, Keulbit

AU - Kang, Hong Goo

N1 - 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.

PY - 2018/6/4

Y1 - 2018/6/4

N2 - 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.

AB - 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.

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M3 - Conference contribution

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T3 - 2018 International Conference on Signals and Systems, ICSigSys 2018 - Proceedings

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EP - 270

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd International Conference on Signals and Systems, ICSigSys 2018

Y2 - 1 May 2018 through 3 May 2018

ER -

Yang H, Choe S, Kim K, Kang HG. Deep learning-based speech presence probability estimation for noise PSD estimation in single-channel speech enhancement. In 2018 International Conference on Signals and Systems, ICSigSys 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 267-270. (2018 International Conference on Signals and Systems, ICSigSys 2018 - Proceedings). doi: 10.1109/ICSIGSYS.2018.8372770

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

Abstract

Publication series

Conference

Bibliographical note

All Science Journal Classification (ASJC) codes

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