Deep neural network Based Low-latency Speech Separation with Asymmetric analysis-Synthesis Window Pair
Abstract
Time-frequency masking or spectrum prediction computed via short symmetric windows are commonly used in low-latency deep neural network (DNN) based source separation. In this paper, we propose the usage of an asymmetric analysis-synthesis window pair which allows for training with targets with better frequency resolution, while retaining the low-latency during inference suitable for real-time speech enhancement or assisted hearing applications. In order to assess our approach across various model types and datasets, we evaluate it with both speaker-independent deep clustering (DC) model and a speaker-dependent mask inference (MI) model. We report an improvement in separation performance of up to 1.5 dB in terms of source-to-distortion ratio (SDR) while maintaining an algorithmic latency of 8 ms.
Cite
@article{arxiv.2106.11794,
title = {Deep neural network Based Low-latency Speech Separation with Asymmetric analysis-Synthesis Window Pair},
author = {Shanshan Wang and Gaurav Naithani and Archontis Politis and Tuomas Virtanen},
journal= {arXiv preprint arXiv:2106.11794},
year = {2021}
}
Comments
Accepted to EUSIPCO-2021