Speech Enhancement Using Dilated Wave-U-Net: an Experimental Analysis

• Main Authors: Mohamed Nabih Ali Mohamed Nawar, Alessio Brutti, Daniele Falavigna
• Format: Article
• Language: English
• Published: FRUCT 2020-09-01
• Series: Proceedings of the XXth Conference of Open Innovations Association FRUCT
• Subjects: speech enhancement; wave-u-net; dilated convolutional neural network
• Online Access: https://www.fruct.org/publications/fruct27/files/Ali.pdf
• ISSN: 2305-7254; 2343-0737

Speech enhancement is a relevant component in many real-world applications such as hearing aid, mobile telecommunications and healthcare applications. In this paper, we investigate the Dilated Wave-U-Net model: a recently proposed end-to-end neural speech enhancement approach based on the Wave-U-Net architecture. We evaluate the performance of the model on two datasets: the public VCTK dataset, and a contaminated version of Librispeech. In particular, we experiment on using alternative losses based on L1 norm and on a combination of L1 and MSE losses. Results show that the Dilated Wave-U-Net architecture outperforms other state-of-the-art methods in terms of intelligibility and quality metrics on both datasets and that MSE loss is the most performing.