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
536
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 329
56 Domestic Journal 권유환, 정수환, 강홍구 "화자 인식을 위한 적대학습 기반음성 분리 프레임워크에 대한 연구" in 한국음향학회지, vol.39, 제 5호, pp.447-453, 2020
55 Domestic Conference 오태양, 정기혁, 강홍구 "화자 및 발화 스타일 임베딩을 통한 다화자 음성합성 시스템 음질 향상" in 전자공학회 하계학술대회, pp.980-982, 2020
54 Domestic Conference 이성현, 강홍구 "딥러닝 기반 종단 간 다채널 음질 개선 알고리즘" in 전자공학회 하계학술대회, pp.968-970, 2020
53 Domestic Conference 임정운, 김지현, 강홍구 "메타러닝을 이용한 SAR 영상 자동표적 인식" in 한국항공우주학회 2020 춘계학술대회, pp.353-354, 2020
52 Domestic Journal 오상신, 엄세연, 장인선, 안충현, 강홍구 "k-평균 알고리즘을 활용한 음성의 대표 감정 스타일 결정 방법" in 한국음향학회지, vol.38, 제 5호, pp.614-620, 2019
51 Domestic Conference 양원, 정수환, 강홍구 "비학습 데이터 적응화 기법을 이용한 딥러닝 기반 한국어 음성 인식 기술" in 한국음향학회 추계발표대회, 2018
50 Domestic Conference 최소연, 정수환, 강홍구 "임베딩 매트릭스를 기반으로 한 비정상적 잡음 제거 알고리즘의 분석과 딥러닝 음질개선 방법들과의 성능비교" in 한국음향학회 추계발표대회, 2018
49 Domestic Conference 양해민, 강홍구 "잡음 예측을 위한 심층 신경망기반 음성 존재 확률 계산법" in 대한전자공학회 추계학술대회, 2017
48 Domestic Conference 오상신, 정수환, 강홍구 "음성 인식 기반의 방송미디어 디바이스 제어 및 편집 시스템 구현" in 대한전자공학회 추계학술대회, 2017
47 Domestic Conference 김정규, 박영철, 강홍구 "저사양 TV 사운드 설계환경을 위한 IIR 필터 기반 주파수 등화기" in 대한전자공학회 학술대회, 2017