Pitch Detection Using Deep Learning, M.Sc. Thesis Sharif University of Technology ; Marvasti, Farrokh (Supervisor) ; Ghaemmaghami, Shahrokh (Co-Supervisor)
Abstract
Pitch frequency is one of the most important attributes of speech, which has been found to be quite challenging in noisy conditions. In this paper, we propose a pitch detection method based on separation of the low pitch from high pitch signals, depending on the pitch frequency below or over 200Hz, respectively, using a deep convolutional neural network. The pitch frequency is initially estimated, employing a conventional pitch detection method. From this initial estimation and using a deep convolutional neural network which determines the signals type (high-pitch or low-pitch), the pitch candidates are derived. To choose the true pitch values, we use three features in addition to soft...
Cataloging briefPitch Detection Using Deep Learning, M.Sc. Thesis Sharif University of Technology ; Marvasti, Farrokh (Supervisor) ; Ghaemmaghami, Shahrokh (Co-Supervisor)
Abstract
Pitch frequency is one of the most important attributes of speech, which has been found to be quite challenging in noisy conditions. In this paper, we propose a pitch detection method based on separation of the low pitch from high pitch signals, depending on the pitch frequency below or over 200Hz, respectively, using a deep convolutional neural network. The pitch frequency is initially estimated, employing a conventional pitch detection method. From this initial estimation and using a deep convolutional neural network which determines the signals type (high-pitch or low-pitch), the pitch candidates are derived. To choose the true pitch values, we use three features in addition to soft...
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