Papers

Phase-Sensitive Joint Learning Algorithms for Deep Learning-Based Speech Enhancement

International Journal
2016~2020
작성자
이진영
작성일
2018-08-01 22:09
조회
2724
Authors : Jinkyu Lee, Jan Skoglund, Turaj Shabestary, Hong-Goo Kang

Year : 2018

Publisher / Conference : IEEE Signal Processing Letters

Volume : 25, issue 8

Page : 1276-1280

This letter presents a phase-sensitive joint learning algorithm for single-channel speech enhancement. Although a deep learning framework that estimates the time-frequency (T-F) domain ideal ratio masks demonstrates a strong performance, it is limited in the sense that the enhancement process is performed only in the magnitude domain, while the phase spectra remain unchanged. Thus, recent studies have been conducted to involve phase spectra in speech enhancement systems. A phase-sensitive mask (PSM) is a T-F mask that implicitly represents phase-related information. However, since the PSM has an unbounded value, the networks are trained to target its truncated values rather than directly estimating it. To effectively train the PSM, we first approximate it to have a bounded dynamic range under the assumption that speech and noise are uncorrelated. We then propose a joint learning algorithm that trains the approximated value through its parameterized variables in order to minimize the inevitable error caused by the truncation process. Specifically, we design a network that explicitly targets three parameterized variables: 1) speech magnitude spectra; 2) noise magnitude spectra; and 3) phase difference of clean to noisy spectra. To further improve the performance, we also investigate how the dynamic range of magnitude spectra controlled by a warping function affects the final performance in joint learning algorithms. Finally, we examined how the proposed additional constraint that preserves the sum of the estimated speech and noise power spectra affects the overall system performance. The experimental results show that the proposed learning algorithm outperforms the conventional learning algorithm with the truncated phase-sensitive approximation.
전체 365
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6 International Journal Jinkyu Lee, Hong-Goo Kang "A Joint Learning Algorithm for Complex-Valued T-F Masks in Deep Learning-Based Single-Channel Speech Enhancement Systems" in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol.27, issue 6, pp.1098-1108, 2019
5 International Journal Seung-Chul Shin, Jinkyu Lee, Soyeon Choe, Hyuk In Yang, Jihee Min, Ki-Yong Ahn, Justin Y. Jeon, Hong-Goo Kang "Dry Electrode-Based Body Fat Estimation System with Anthropometric Data for Use in a Wearable Device" in Sensors, vol.19, issue 9, 2019
4 International Journal Jinkyu Lee, Jan Skoglund, Turaj Shabestary, Hong-Goo Kang "Phase-Sensitive Joint Learning Algorithms for Deep Learning-Based Speech Enhancement" in IEEE Signal Processing Letters, vol.25, issue 8, pp.1276-1280, 2018
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2 International Journal Min-Jae Hwang, JeeSok Lee, MiSuk Lee, Hong-Goo Kang "SVD-Based Adaptive QIM Watermarking on Stereo Audio Signals" in IEEE Transactions on Multimedia, vol.20, issue 1, pp.45-54, 2018
1 International Journal Eunwoo Song, Frank K. Soong, Hong-Goo Kang "Effective Spectral and Excitation Modeling Techniques for LSTM-RNN-Based Speech Synthesis Systems" in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol.25, issue 11, pp.2152-2161, 2017