Text Separation of Single-Channel Audio Sources Using Deep Neural Networks, M.Sc. Thesis Sharif University of Technology ; Motahari, Abolfazl (Supervisor)
Abstract
The problem of separation of audio sources is one of the oldest issues raised in the field of audio processing, which has been studied for more than half a century. The main focus of recent research in this field has been on improving the sound quality resulting from the separation of sound sources with the help of deep neural networks. This is despite the fact that in most applications of audio source separation, such as the application of meeting transcription, we do not need the separated audio of people. Rather, we need a pipeline of converting overlapping speech to text, which, by receiving the audio in which several people have spoken, outputs the text spoken by the people present in...
Cataloging briefText Separation of Single-Channel Audio Sources Using Deep Neural Networks, M.Sc. Thesis Sharif University of Technology ; Motahari, Abolfazl (Supervisor)
Abstract
The problem of separation of audio sources is one of the oldest issues raised in the field of audio processing, which has been studied for more than half a century. The main focus of recent research in this field has been on improving the sound quality resulting from the separation of sound sources with the help of deep neural networks. This is despite the fact that in most applications of audio source separation, such as the application of meeting transcription, we do not need the separated audio of people. Rather, we need a pipeline of converting overlapping speech to text, which, by receiving the audio in which several people have spoken, outputs the text spoken by the people present in...
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