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
378
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 326
276 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
275 International Conference Haemin Yang, Soyeon Choe, Keulbit Kim, Hong-Goo Kang "Deep learning-based speech presence probability estimation for noise PSD estimation in single-channel speech enhancement" in ICSigSys, 2018
274 International Conference Min-Jae Hwang, Eunwoo Song, Kyungguen Byun, Hong-Goo Kang "Modeling-by-Generation-Structured Noise Compensation Algorithm for Glottal Vocoding Speech Synthesis System" in ICASSP, 2018
273 International Conference Jinyoung Lee, Chahyeon Eom, Youngsu Kwak, Hong-Goo Kang, Chungyoung Lee "DNN-based Wireless Positioning in An Outdoor Environment" in ICASSP, 2018
272 International Conference Seung-chul Shin, Sangyeop Lee, Taeho Lee, Kyoungwoo Lee, Yong Seung Lee, Hong-Goo Kang "Two electrode based healthcare device for continuously monitoring ECG and BIA signals" in BHI, 2018
271 International Journal JeeSok Lee, Soo-Whan Chung, Min-Seok Choi, Hong-Goo Kang "Generic uniform search grid generation algorithm for far-field source localization" in The Journal of the Acoustical Society of America, vol.143, 2018
270 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
269 International Conference Eunwoo Song, Frank K. Soong, Hong-Goo Kang "Perceptual quality and modeling accuracy of excitation parameters in DLSTM-based speech synthesis systems" in ASRU, 2017
268 Domestic Conference 양해민, 강홍구 "잡음 예측을 위한 심층 신경망기반 음성 존재 확률 계산법" in 대한전자공학회 추계학술대회, 2017
267 Domestic Conference 오상신, 정수환, 강홍구 "음성 인식 기반의 방송미디어 디바이스 제어 및 편집 시스템 구현" in 대한전자공학회 추계학술대회, 2017