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

International Journal
2018-08-01 22:09
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 360
360 International Conference Seyun Um, Doyeon Kim, Hong-Goo Kang "PARAN: Variational Autoencoder-based End-to-End Articulation-to-Speech System for Speech Intelligibility" in INTERSPEECH, 2024
359 International Conference Jihyun Kim, Stijn Kindt, Nilesh Madhu, Hong-Goo Kang "Enhanced Deep Speech Separation in Clustered Ad Hoc Distributed Microphone Environments" in INTERSPEECH, 2024
358 International Conference Woo-Jin Chung, Hong-Goo Kang "Speaker-Independent Acoustic-to-Articulatory Inversion through Multi-Channel Attention Discriminator" in INTERSPEECH, 2024
357 International Conference Juhwan Yoon, Woo Seok Ko, Seyun Um, Sungwoong Hwang, Soojoong Hwang, Changhwan Kim, Hong-Goo Kang "UNIQUE : Unsupervised Network for Integrated Speech Quality Evaluation" in INTERSPEECH, 2024
356 International Conference Yanjue Song, Doyeon Kim, Hong-Goo Kang, Nilesh Madhu "Spectrum-aware neural vocoder based on self-supervised learning for speech enhancement" in EUSIPCO, 2024
355 International Conference Hyewon Han, Naveen Kumar "A cross-talk robust multichannel VAD model for multiparty agent interactions trained using synthetic re-recordings" in Hands-free Speech Communication and Microphone Arrays (HSCMA, Satellite workshop in ICASSP), 2024
354 International Conference Yanjue Song, Doyeon Kim, Nilesh Madhu, Hong-Goo Kang "On the Disentanglement and Robustness of Self-Supervised Speech Representations" in International Conference on Electronics, Information, and Communication (ICEIC) (*awarded Best Paper), 2024
353 International Conference Yeona Hong, Miseul Kim, Woo-Jin Chung, Hong-Goo Kang "Contextual Learning for Missing Speech Automatic Speech Recognition" in International Conference on Electronics, Information, and Communication (ICEIC), 2024
352 International Conference Juhwan Yoon, Seyun Um, Woo-Jin Chung, Hong-Goo Kang "SC-ERM: Speaker-Centric Learning for Speech Emotion Recognition" in International Conference on Electronics, Information, and Communication (ICEIC), 2024
351 International Conference Hejung Yang, Hong-Goo Kang "On Fine-Tuning Pre-Trained Speech Models With EMA-Target Self-Supervised Loss" in ICASSP, 2024