Abstract
In this paper, we present a practical three-step approach for singing voice detection based on a gated recurrent unit (GRU) recurrent neural network (RNN) and the proposed method achieves comparable results to state-of-the-art method. We combine four classic features—namely Mel-frequency Cepstral Coefficients (MFCC), Mel-filter Bank, Linear Predictive Cepstral Coefficients (LPCC), and Chroma. Then, the mixed signal is first preprocessed by singing voice separation (SVS) with the Deep U-Net Convolutional Networks. Long short-term memory (LSTM) and GRU are both proposed to solve the gradient vanish problem in RNN. In our experiments, we set the block duration as 120 ms and 720 ms respectively, and we get comparable or better results than results from state-of-the-art methods, while results on Jamendo are not as good as those from RWC-Pop.
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Acknowledgements
This research was supported by NSFC 61671156. We thank our colleagues from Fudan University, who provided insight and expertise that greatly assisted the research, although they may not agree with all the interpretations of this paper.
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Chen, Z., Zhang, X., Deng, J., Li, J., Jiang, Y., Li, W. (2019). A Practical Singing Voice Detection System Based on GRU-RNN. In: Li, W., Li, S., Shao, X., Li, Z. (eds) Proceedings of the 6th Conference on Sound and Music Technology (CSMT). Lecture Notes in Electrical Engineering, vol 568. Springer, Singapore. https://doi.org/10.1007/978-981-13-8707-4_2
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