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Deep neural network Based Low-latency Speech Separation with Asymmetric analysis-Synthesis Window Pair

2021-06-22 14:05:54
Shanshan Wang, Gaurav Naithani, Archontis Politis, Tuomas Virtanen
arXiv_SD
           
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.

Abstract (translated)

URL

https://arxiv.org/abs/2106.11794

PDF

https://arxiv.org/pdf/2106.11794.pdf

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