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Identifier 000438710
Title On the use of neural networks and dilation for speech enhancement in a Generative Adversarial Network environment
Alternative Title Περί της χρήσης νευρωνικών δικτύων για βελτίωση σήματος φωνής και της διαστολής σε ένα περιβάλλον Παραγωγικού Αντιπαραθετικού Δικτύου
Author Μπακαγιάννης, Λεωνίδας
Thesis advisor Στυλιανού, Ιωάννης
Reviewer Τσακαλίδης, Παναγιώτης
Πανταζής, Γιάννης
Abstract Speech Enhancement(SE) is a Speech Processing field which aims to improve speech quality of noisy signals in an attempt to increase their intelligibility and,as a consequence, to reduce the amount of effort that someone has to make in order to listen to them. Several algorithms have been proposed for speech enhancement throughout the 20th century. Most of them mainly took advantage of the spectral characteristics of the noisy signal. But with the use of Neural Networks (NNs) escalating over the recent years, there have been several neural-network-based systems that are used to enhance a signal and remove noise. A relatively recent class of machine learning frameworks based on Neural Networks are Generative Adversarial Networks(GANs) which use two separate neural networks,the Generator and the Discriminator, that compete with each other in order to achieve the system’s goals. These two networks play a minimax zero-sum game, where the Generator tries to produce samples that seem real to the Discriminator with the ultimate goal of the generator being the production of samples that the discriminator cannot distinguish whether they erupt from the generator or from the real distribution. In this thesis, a study of the main neural-network-based systems for speech enhancement is presented alongside a study on how a neural network concept, dilation, can be used to boost speech enhancement performance. Specifically, a comparative evaluation of the architectures of three Speech Enhancement system (SE-WaveNet, SEGAN, SE-FFTNet) is presented as well their comparative evaluation based on objective (PESQ,STOI,CSIG,CBAK,CVAL,SSNR) and subjective (Mean Opinion Score) metrics. Additionally, the experiments regarding the application of dilation in a Generative Adversial Network environment in an effort to reduce the number of parameters required for a Speech Enhancement Generative Adversarial Network is presented.
Language English
Issue date 2021-03-26
Collection   School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
  Type of Work--Post-graduate theses
Permanent Link https://elocus.lib.uoc.gr//dlib/4/a/7/metadata-dlib-1617103260-934216-7565.tkl Bookmark and Share
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