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
4936
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 375
375 International Conference Sangmin Lee, Woojin Chung, Seyun Um, and Hong-Goo Kang "UniCoM: A Universal Code-Switching Speech Generator" in EMNLP Findings, 2025
374 International Conference Woongjib Choi, Byeong Hyeon Kim, Hyungseob Lim, Inseon Jang, Hong-Goo Kang "Neural Spectral Band Generation for Audio Coding" in INTERSPEECH, 2025
373 International Conference Jihyun Kim, Doyeon Kim, Hyewon Han, Jinyoung Lee, Jonguk Yoo, Chang Woo Han, Jeongook Song, Hoon-Young Cho, Hong-Goo Kang "Quadruple Path Modeling with Latent Feature Transfer for Permutation-free Continuous Speech Separation" in INTERSPEECH, 2025
372 International Conference Byeong Hyeon Kim,Hyungseob Lim,Inseon Jang,Hong-Goo Kang "Towards an Ultra-Low-Delay Neural Audio Coding with Computational Efficiency" in INTERSPEECH, 2025
371 International Conference Stijn Kindt,Jihyun Kim,Hong-Goo Kang,Nilesh Madhu "Efficient, Cluster-Informed, Deep Speech Separation with Cross-Cluster Information in AD-HOC Wireless Acoustic Sensor Networks" in International Workshop on Acoustic Signal Enhancement (IWAENC), 2024
370 International Conference Yeona Hong, Hyewon Han, Woo-jin Chung, Hong-Goo Kang "StableQuant: Layer Adaptive Post-Training Quantization for Speech Foundation Models" in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2025
369 International Conference Sangmin Lee, Woojin Chung, Hong-Goo Kang "LAMA-UT: Language Agnostic Multilingual ASR through Orthography Unification and Language-Specific Transliteration" in Association for the Advancement of Artificial Intelligence (AAAI), 2025
368 International Journal Hyewon Han, Xiulian Peng, Doyeon Kim, Yan Lu, Hong-Goo Kang "Dual-Branch Guidance Encoder for Robust Acoustic Echo Suppression" in IEEE Transactions on Audio, Speech and Language Processing (TASLP), vol.33, pp.627 - 639, 2025
367 International Journal Hyungseob Lim, Jihyun Lee, Byeong Hyeon Kim, Inseon Jang, Hong-Goo Kang "Perceptual Neural Audio Coding with Modified Discrete Cosine Transform" in IEEE Journal of Special Topics in Signal Processing (JSTSP), 2024
366 International Conference Juhwan Yoon, Hyungseob Lim, Hyeonjin Cha, Hong-Goo Kang "StylebookTTS: Zero-Shot Text-to-Speech Leveraging Unsupervised Style Representation" in APSIPA ASC, 2024