Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157874
Title: Deep learning speech enhancement in satellite radio communication
Authors: Low, Yuki Yu Jun
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Low, Y. Y. J. (2022). Deep learning speech enhancement in satellite radio communication. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157874
Abstract: Clarity and intelligibility are critical aspects of speech. Deep learning models for speech enhancement uses different algorithms to improve the speech quality significantly before reaching the listener. Machine learning knowledge is crucial in generating models to predict the outcome of the speech enhancement model. In this report, we study about the source of noise in an audio when air pilot controllers communicate and methods used for speech enhancement, mainly Wave-U-Net and a hybrid Recurrent Neural Network-based model. Wave-U-Net is a multi-scale neural network that provides end-to-end audio source separation, which is a modification from U-Net. Wave-U-Net repeatedly resamples feature maps to calculate and integrate features at different time scales [1]. The RNN-based model uses a hybrid of deep learning in conjunction with the basics of audio signal processing. Our experiment shows that the proposed Wave-U-Net method improves the audio quality consistently with PESQ metric – a test methodology that automatically assess speech quality when compared to the hybrid RNN-based model.
URI: https://hdl.handle.net/10356/157874
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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