Hybrid Deep Learning Model for Singing Voice Separation

  • Rusul Amer Information and Communication Eng. Dept., Al Khwarizmi Eng. College, University of Baghdad, Iraq
  • Ahmed Al Tmeme Information and Communication Eng. Dept., Al Khwarizmi Eng. College, University of Baghdad, Iraq
Keywords: Monaural Source Separation, Hybrid Deep Learning, Time Frequency Masking, Convolution Neural Network, Dense Neural Network, Recurrent Neural Network

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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Published
2021-12-21
How to Cite
[1]
Amer, R. and Al Tmeme, A. 2021. Hybrid Deep Learning Model for Singing Voice Separation. MENDEL. 27, 2 (Dec. 2021), 44-50. DOI:https://doi.org/10.13164/mendel.2021.2.044.
Section
Articles