Unsupervised Cross-Domain Singing Voice Conversion

Adam Polyak, Lior Wolf, Yossi Adi, Yaniv Taigman

We present a wav-to-wav generative model for the task of singing voice conversion from any identity. Our method utilizes both an acoustic model, trained for the task of automatic speech recognition, together with melody extracted features to drive a waveform-based generator. The proposed generative architecture is invariant to the speaker’s identity and can be trained to generate target singers from unlabeled training data, using either speech or singing sources. The model is optimized in an end-to-end fashion without any manual supervision, such as lyrics, musical notes or parallel samples. The proposed approach is fully-convolutional and can generate audio in realtime. Experiments show that our method significantly outperforms the baseline methods while generating convincingly better audio samples than alternative attempts.

 DOI: 10.21437/Interspeech.2020-1862

Cite as: Polyak, A., Wolf, L., Adi, Y., Taigman, Y. (2020) Unsupervised Cross-Domain Singing Voice Conversion. Proc. Interspeech 2020, 801-805, DOI: 10.21437/Interspeech.2020-1862.

  author={Adam Polyak and Lior Wolf and Yossi Adi and Yaniv Taigman},
  title={{Unsupervised Cross-Domain Singing Voice Conversion}},
  booktitle={Proc. Interspeech 2020},