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README.md

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 This is a pre-trained version of Fast FullSubNet, a real-time denoising model trained on the Deep Noise Suppression Challenge dataset of 2020 (DNS-INTERSPEECH-2020).
 
-# Instructions
+## How to run
 
 https://fullsubnet.readthedocs.io/en/latest/usage/getting_started.html
 
-# Code
+## Code
 
 https://github.com/Audio-WestlakeU/FullSubNet
 
 Note: The code doesn't support real-time streaming out of the box. See [issue-67](https://github.com/Audio-WestlakeU/FullSubNet/issues/67) for details.
 
-# Paper
+## Paper
 
 [Fast FullSubNet: Accelerate Full-band and Sub-band Fusion Model for Single-channel Speech Enhancement
 Xiang Hao, Xiaofei Li](https://arxiv.org/abs/2212.09019)
 
-# Performance
+> For many speech enhancement applications, a key feature is that system runs on a real-time, latency-sensitive, battery-powered platform, which strictly limits the algorithm latency and computational complexity. In this work, we propose a new architecture named Fast FullSubNet dedicated to accelerating the computation of FullSubNet. Specifically, Fast FullSubNet processes sub-band speech spectra in the mel-frequency domain by using cascaded linear-to-mel full-band, sub-band, and mel-to-linear full-band models such that frequencies involved in the sub-band computation are vastly reduced. After that, a down-sampling operation is proposed for the sub-band input sequence to further reduce the computational complexity along the time axis. Experimental results show that, compared to FullSubNet, Fast FullSubNet has only 13\% computational complexity and 16\% processing time, and achieves comparable or even better performance.
+
+## Performance
 
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