Estimation and Correction of Relative Transfer Function for Binaural Speech Separation Networks to Preserve Spatial Cues

Feng，Zicheng1; Tsao，Yu2; Chen，Fei1

2021

2640-009X

978-1-6654-4162-9

2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings

1239-1244

14-17 Dec. 2021

Tokyo, Japan

Deep learning based approaches have achieved great success in mono-channel and multi-channel speech separation, but limited studies have focused on the binaural output, not even to mention the preservation of spatial cues. Existing speech separation networks preserve spatial cues by improving the signal-to-noise ratio (SNR) of the separated speech, regardless of the different requirements between reducing noise and preserving spatial cues. This work proposed a framework to optimize spatial cue preservation for binaural speech separation. It consisted of a relative transfer function (RTF) corrector that modified the distorted RTF of the separated speech into a correct one, and an RTF estimator to extract the correct RTF. A new RTF estimator was designed to obtain an accurate RTF. The framework was evaluated on a binaural version of WSJ0-2mix dataset, which was spatialized by anechoic head-related impulse responses. Experimental results showed that the proposed framework significantly reduced the interaural time difference (ITD) and interaural level difference (ILD) errors of the existing binaural separation networks, but did not notably sacrifice the SNR of the separated speech signals.

Deep learning Transfer functions Estimation Information processing Distortion Real-time systems Noise measurement

第一 ; 通讯

英语

EI

20221211827082

Deep learning ; Separation ; Signal to noise ratio ; Source separation ; Speech ; Speech analysis

Ergonomics and Human Factors Engineering:461.4 ; Information Theory and Signal Processing:716.1 ; Speech:751.5 ; Chemical Operations:802.3 ; Mathematics:921

2-s2.0-85126710873

Scopus

1.Southern University of Science and Technology,Department of Electrical and Electronic Engineering,China
2.Research Center for Information Technology Innovation,Academia Sinica,Taiwan

Feng，Zicheng,Tsao，Yu,Chen，Fei. Estimation and Correction of Relative Transfer Function for Binaural Speech Separation Networks to Preserve Spatial Cues[C],2021:1239-1244.