Complex Spectral Mapping With Attention Based Convolution Recurrent Neural Network for Speech Enhancement

Abstract

Speech enhancement has benefited from the success of deep learning in terms of intelligibility and perceptual quality. Conventional time-frequency (TF) domain methods focus on predicting TF-masks or speech spectrum,via a naive convolution neural network or recurrent neural network.Some recent studies were based on Complex spectral Mapping convolution recurrent neural network (CRN) . These models skiped directly from encoder layers' output and decoder layers' input ,which maybe thoughtless. We proposed an attention mechanism based skip connection between encoder and decoder layers,namely Complex Spectral Mapping With Attention Based Convolution Recurrent Neural Network (CARN).Compared with CRN model,the proposed CARN model improved more than 10% relatively at several metrics such as PESQ,CBAK,COVL,CSIG and son,and outperformed the place 1st model in both real time and non-real time track of the DNS Challenge 2020 at these metrics.

Interspeech2021 submitted

References in corpus (6)

• Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation
• The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Speech Quality and Testing Framework
• DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement
• Supervised Speech Separation Based on Deep Learning: An Overview
• Demucs: Deep Extractor for Music Sources with extra unlabeled data remixed
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Cited by in corpus (2)

• Self-attending RNN for Speech Enhancement to Improve Cross-corpus Generalization
• A Deep Learning Loss Function based on Auditory Power Compression for Speech Enhancement