| Citation: | ZHANG Yang, NIU Zhixian, NIU Baoning, et al. Monaural singing voice separation based on high-resolution network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(8): 1555-1563. doi: 10.13700/j.bh.1001-5965.2019.0491(in Chinese)
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Monaural singing voice separation separates singing voice and accompaniment from a song, which can be used for applications such as melody extraction, lyrics recognition, karaoke, etc. To resolve the limited accuracy of predicted spectrogram, this paper proposes a monaural singing voice separation algorithm based on high-resolution neural network, which has the advantages of parallel structure and sufficient features interaction for improving the performance of the model. Firstly, the high-resolution network suitable for singing voice separation is designed and constructed. Then, the spectrogram of the origin song is input to the network in order to get the predicted spectrograms of accompaniment and singing voice. Finally, the time-domain signals are reconstructed by combining the song phases with the separated spectrograms. Experiments conducted on the MIR-1K dataset show that SNR, SIR and SAR indicators of the proposed algorithm are better than those of the state-of-the-art algorithm, and the proposed algorithm improves the quality of the separated accompaniment and singing voice.
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