# Lightweight End-to-End Speech Enhancement Generative Adversarial Network Using Sinc Convolutions
## 8 Conclusions  
This paper proposes Sinc-SEGAN, a system that merges the Sinc convolution layer with the optimized SEGAN to capture more underlying representative speech characteristics. Sinc-SEGAN processes raw speech waveforms directly to prevent distortion caused by imperfect phase estimation. We investigate two different deployments of the Sinc convolution: (i) sitting before the first layer of the encoder and the discriminator, and behind the last layer of the decoder, referred to as Sinc-SEGAN-add, and (ii) acting as a substitute of the first standard convolutional layers of the encoder and the discriminator, and the last standard convolutional layer of the decoder, referred to as Sinc-SEGAN-sub. Ablation tests are conducted for the influence of the input length, number of Sinc filters, and kernel size of Sinc convolution. To train the proposed system more efficiently, we also employ three data augmentation methods in the time domain. Experimental results show that Sinc-SEGAN-sub yields enhanced signals with higher-level perceptual quality and speech intelligibility, even with drastically reduced system parameters. By contrast, the proposed Sinc-SEGAN-add overtakes all baseline systems across all classic objective evaluation criteria, with up to \(\sim\)50% fewer parameters compared to the baseline system. Moreover, data augmentation methods further boost the system performance. Analysis of the Sinc filters reveals that the learned filter bank is tuned precisely to select narrow-band speech clues, and is hence suitable for speech enhancement tasks in the time domain. Our future effort will be devoted to applying the Sinc convolution to other classic speech enhancement models, to further mitigate the lack of its application in the field of speech enhancement.  
Conceptualization, L.L.; methodology, L.L.; software, L.L. and W.; validation, L.L.; formal analysis, L.L.; investigation, L.L. and W.; resources, L.L.; data curation, L.L.; writing--original draft preparation, L.L. and W.; writing--review and editing, L.L., W., L.K., T.W. and G.R.; visualization, L.L., W., L.K., T.W. and G.R.; supervision, G.R. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by Technical University of Munich Publishing Fund.  
Not applicable.  
The dataset [39] utilized by this paper is publicly available at the publisher webpage: [[https://datashare.ed.ac.uk/handle/10283/1942](https://datashare.ed.ac.uk/handle/10283/1942)]([https://datashare.ed.ac.uk/handle/10283/1942](https://datashare.ed.ac.uk/handle/10283/1942)) (accessed on 5 February 2021). The authors wish to thank the editors and reviewers for their valuable comments.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CNN & convolutional neural network \\ GAN & generative adversarial network \\ SEGAN & speech enhancement generative adversarial network \\ SNR & Signal-to-Noise Ratio \\ MOS & Mean Opinion Score \\ SSNR & segmental SNR \\ STOI & Short-Time Objective Intelligibility \\ CBAK & MOS prediction of the intrusiveness of background noises \\ CSIG & MOS prediction of the signal distortion attending only to the speech signal \\ COVID & MOS prediction of the overall effect \\ PESQ & perceptual evaluation of speech quality \\ \end{tabular}