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

Autor:	Wang, Shanshan, Naithani, Gaurav, Politis, Archontis, Virtanen, Tuomas
Rok vydání:	2021
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Computer Science - Sound
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to EUSIPCO-2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2106.11794 Zobrazit plný text záznamu View this record from Arxiv