A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is
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 framedoi:10.21437/interspeech.2020-2631 dblp:conf/interspeech/WesthausenM20 fatcat:xn7qcs6iqneillz5uasbrge52a