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We present a deep neural network based singing voice synthesizer, inspired by the Deep Convolutions Generative Adversarial Networks (DCGAN) architecture and optimized using the Wasserstein-GAN algorithm.
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M. Blaauw and J. Bonada, “A neural parametric singing synthesizer,” in INTERSPEECH , 2017
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S. Ma, D. Mcduff, and Y. Song, “A generative adversarial network for style modeling in a text-to-speech system,” in International Conference on Learning Representations , 2019
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T. Kaneko, S. Takaki, H. Kameoka, and J. Yamagishi, “Generative adversarial network-based postfilter for stft spectrograms,” in Proceedings of Interspeech , 2017
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P. Chandna, M. Miron, J. Janer, and E. Gómez, “Monoaural audio source separation using deep convolutional neural networks,” in International Conference on Latent Variable Analysis and Signal Separation . Springer, 2017, pp. 258–266
2017
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J. Shen, R. Pang, R. J. Weiss, M. Schuster, N. Jaitly, Z. Yang, Z. Chen, Y. Zhang, Y. Wang, R. Skerrv-Ryan et al. , “Natural tts synthesis by conditioning wavenet on mel spectrogram predictions,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2018, pp. 4779–4783
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Y. Zhao, S. Takaki, H.-T. Luong, J. Yamagishi, D. Saito, and N. Minematsu, “Wasserstein gan and waveform loss-based acoustic model training for multi-speaker text-to-speech synthesis systems using a wavenet vocoder,” IEEE Access , vol. 6, pp. 60 478–60 488, 2018
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2019
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M. Blaauw, J. Bonada, and R. Daido, “Data efficient voice cloning for neural singing synthesis,” in 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2019
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S. Lee, J. Ha, and G. Kim, “Harmonizing maximum likelihood with GANs for multimodal conditional generation,” in International Conference on Learning Representations , 2019
2019
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