FRCRN: Boosting Feature Representation using Frequency Recurrence for Monaural Speech Enhancement

TL;DR

This paper introduces FRCRN, a novel speech enhancement model that uses frequency recurrence with FSMN to improve feature representation across frequency bands, leading to state-of-the-art results.

Contribution

The paper proposes a Frequency Recurrent CRN (FRCRN) that incorporates frequency recurrence via FSMN to enhance feature modeling in monaural speech enhancement.

Findings

Achieved state-of-the-art performance on benchmark datasets.

Secured 2nd place in ICASSP 2022 DNS challenge fullband track.

Improved speech quality and accuracy metrics.

Abstract

Convolutional recurrent networks (CRN) integrating a convolutional encoder-decoder (CED) structure and a recurrent structure have achieved promising performance for monaural speech enhancement. However, feature representation across frequency context is highly constrained due to limited receptive fields in the convolutions of CED. In this paper, we propose a convolutional recurrent encoder-decoder (CRED) structure to boost feature representation along the frequency axis. The CRED applies frequency recurrence on 3D convolutional feature maps along the frequency axis following each convolution, therefore, it is capable of catching long-range frequency correlations and enhancing feature representations of speech inputs. The proposed frequency recurrence is realized efficiently using a feedforward sequential memory network (FSMN). Besides the CRED, we insert two stacked FSMN layers between the encoder and the decoder to model further temporal dynamics. We name the proposed framework as Frequency Recurrent CRN (FRCRN). We design FRCRN to predict complex Ideal Ratio Mask (cIRM) in complex-valued domain and optimize FRCRN using both time-frequency-domain and time-domain losses. Our proposed approach achieved state-of-the-art performance on wideband benchmark datasets and achieved 2nd place for the real-time fullband track in terms of Mean Opinion Score (MOS) and Word Accuracy (WAcc) in the ICASSP 2022 Deep Noise Suppression (DNS) challenge (https://github.com/modelscope/ClearerVoice-Studio).