Low Latency Online Blind Source Separation Based on Joint Optimization with Blind Dereverberation
Tetsuya Ueda, Tomohiro Nakatani, Rintaro Ikeshita, Keisuke Kinoshita, Shoko Araki, Shoji Makino
Abstract
This paper presents a new low-latency online blind source separation (BSS) algorithm. Although algorithmic delay of a frequency domain online BSS can be reduced simply by shortening the short-time Fourier transform (STFT) frame length, it degrades the source separation performance in the presence of reverberation. This paper proposes a method to solve this problem by integrating BSS with Weighted Prediction Error (WPE) based dereverberation. Although a simple cascade of online BSS after online WPE upgrades the separation performance, the overall optimality is not guaranteed. Instead, this paper extends a recently proposed batch processing algorithm that can jointly optimize dereverberation and separation so that it can perform online processing with low computational cost and little processing delay (< 12 ms). The results of a source separation experiment in a noisy car environment suggest that the proposed online method has better separation performance than the simple cascaded methods.
BibTeX
@inproceedings{icassp2021_lowlatencyonline,
title = {Low Latency Online Blind Source Separation Based on Joint Optimization with Blind Dereverberation},
author = {Tetsuya Ueda and Tomohiro Nakatani and Rintaro Ikeshita and Keisuke Kinoshita and Shoko Araki and Shoji Makino},
booktitle = {ICASSP 2021},
year = {2021}
}