Note: all the model repositories are currently being reworked into one unified repository to make it easier to maintain. More details will be updated soon.
K. N. Watcharasupat et al., “A Generalized Bandsplit Neural Network for Cinematic Audio Source separation,” IEEE Open Journal of Signal Processing, vol. 5, pp. 73–81, 2023, doi: 10.1109/OJSP.2023.3339428.
Bandit is a cinematic audio source separation model adapted from Bandsplit RNN.
K. N. Watcharasupat and A. Lerch, “A Stem-Agnostic Single-Decoder System for Music Source Separation Beyond Four Stems,” to appear in the Proceedings of the 25th International Society for Music Information Retrieval Conference, San Francisco, CA, USA, Nov. 2024.
Banquet is a query-based music source separation model adapted from Bandit + PaSST
K. N. Watcharasupat, C.-W. Wu, and I. Orife, “Facing the Music: Tackling Singing Voice Separation in Cinematic Audio Source Separation,” to appear in the Late-Breaking Demo Session of the 25th International Society for Music Information Retrieval Conference, San Francisco, CA, USA, Nov. 2024.
- [arXiv](https://arxiv.org/abs/2406.18747
K. N. Watcharasupat, C.-W. Wu, and I. Orife, “Remastering Divide and Remaster: A Cinematic Audio Source Separation Dataset with Multilingual Support,” in Proceedings of the 5th IEEE International Symposium on the Internet of Sounds, Erlangen, Germany: IEEE, Sep. 2024.
A multilingual rework of the Divide and Remaster v2 dataset.
K. N. Watcharasupat and A. Lerch, “Quantifying Spatial Audio Quality Impairment,” in Proceedings of the 2024 International Conference on Acoustics, Speech, and Signal Processing, Seoul, Korea, Republic of: IEEE, Apr. 2024, pp. 746–750. doi: 10.1109/ICASSP48485.2024.10447947.