Dual-Signal Transformation LSTM Network for Real-Time Noise Suppression

This paper introduces a dual-signal transformation LSTM network (DTLN) for real-time speech enhancement as part of the Deep Noise Suppression Challenge (DNS-Challenge). This approach combines a short-time Fourier transform (STFT) and a learned analysis and synthesis basis in a stacked-network approach with less than one million parameters. The model was trained on 500 h of noisy speech provided by the challenge organizers. The network is capable of real-time processing (one frame in, one frame out) and reaches competitive results. Combining these two types of signal transformations enables the DTLN to robustly extract information from magnitude spectra and incorporate phase information from the learned feature basis. The method shows state-of-the-art performance and outperforms the DNS-Challenge baseline by 0.24 points absolute in terms of the mean opinion score (MOS).

PDF Abstract

Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Speech Enhancement Deep Noise Suppression (DNS) Challenge DTLN SI-SDR-WB 16.34 # 6
PESQ-NB 3.04 # 5
Speech Enhancement WHAMR! DTLN SI-SDR 2.12 # 2
PESQ 2.23 # 2
ΔPESQ 0.4 # 1

Methods


No methods listed for this paper. Add relevant methods here