Hybrid Deep Learning Model for Singing Voice Separation

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Date
2021-12-21
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Advisor
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Mark
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Institute of Automation and Computer Science, Brno University of Technology
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Abstract
Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achieves (4.81) dB GNSDR gain, (7.28) dB GSIR gain, and (3.39) dB GSAR gain in comparison to current approaches
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Citation
Mendel. 2021 vol. 27, č. 2, s. 44-50. ISSN 1803-3814
https://mendel-journal.org/index.php/mendel/article/view/139
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Peer-reviewed
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en
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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license
http://creativecommons.org/licenses/by-nc-sa/4.0
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