| Open-unmix-a reference implementation for music source separation FR Stöter, S Uhlich, A Liutkus, Y Mitsufuji The Journal of Open Source Software, 2019 | 342 | 2019 |
| Improving music source separation based on deep neural networks through data augmentation and network blending S Uhlich, M Porcu, F Giron, M Enenkl, T Kemp, N Takahashi, Y Mitsufuji ICASSP, 261-265, 2017 | 288 | 2017 |
| MMDenseLSTM: An efficient combination of convolutional and recurrent neural networks for audio source separation N Takahashi, N Goswami, Y Mitsufuji IWAENC, 2018 | 220 | 2018 |
| Multi-scale multi-band densenets for audio source separation N Takahashi, Y Mitsufuji WASPAA, 21-25, 2017 | 202 | 2017 |
| Deep neural network based instrument extraction from music. S Uhlich, F Giron, Y Mitsufuji ICASSP, 2135-2139, 2015 | 160 | 2015 |
| Consistency trajectory models: Learning probability flow ode trajectory of diffusion D Kim, CH Lai, WH Liao, N Murata, Y Takida, T Uesaka, Y He, Y Mitsufuji, ... ICLR, 2024 | 135 | 2024 |
| ACCDOA: Activity-Coupled Cartesian Direction of Arrival Representation for Sound Event Localization And Detection K Shimada, Y Koyama, N Takahashi, S Takahashi, Y Mitsufuji ICASSP, 915-919, 2021 | 113 | 2021 |
| Music demixing challenge 2021 Y Mitsufuji, G Fabbro, S Uhlich, FR Stöter, A Défossez, M Kim, W Choi, ... Frontiers in Signal Processing, 18, 2022 | 110* | 2022 |
| Recursive speech separation for unknown number of speakers N Takahashi, S Parthasaarathy, N Goswami, Y Mitsufuji INTERSPEECH, 1348-1352, 2019 | 104 | 2019 |
| D3Net: Densely connected multidilated densenet for music source separation N Takahashi, Y Mitsufuji arXiv preprint arXiv:2010.01733, 2020 | 95 | 2020 |
| Multi-ACCDOA: Localizing and Detecting Overlapping Sounds from the Same Class with Auxiliary Duplicating Permutation Invariant Training K Shimada, Y Koyama, S Takahashi, N Takahashi, E Tsunoo, Y Mitsufuji ICASSP, 316-320, 2022 | 92 | 2022 |
| STARSS22: A dataset of spatial recordings of real scenes with spatiotemporal annotations of sound events A Politis, K Shimada, P Sudarsanam, S Adavanne, D Krause, Y Koyama, ... DCASE Workshop, 2022 | 89 | 2022 |
| PhaseNet: Discretized Phase Modeling with Deep Neural Networks for Audio Source Separation. N Takahashi, P Agrawal, N Goswami, Y Mitsufuji INTERSPEECH, 2713-2717, 2018 | 86 | 2018 |
| Densely connected multi-dilated convolutional networks for dense prediction tasks N Takahashi, Y Mitsufuji CVPR, 993-1002, 2021 | 85 | 2021 |
| Signal processing apparatus and signal processing method, encoder and encoding method, decoder and decoding method, and program Y Yamamoto, T Chinen, H Honma, Y Mitsufuji US Patent 9,406,312, 2016 | 73 | 2016 |
| SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization Y Takida, T Shibuya, WH Liao, CH Lai, J Ohmura, T Uesaka, N Murata, ... ICML, 20987-21012, 2022 | 67 | 2022 |
| All for One and One for All: Improving Music Separation by Bridging Networks R Sawata, S Uhlich, S Takahashi, Y Mitsufuji ICASSP, 51-55, 2021 | 62 | 2021 |
| Frequency band extending device and method, encoding device and method, decoding device and method, and program Y Yamamoto, T Chinen, H Honma, Y Mitsufuji US Patent 9,208,795, 2015 | 47 | 2015 |
| GibbsDDRM: A partially collapsed gibbs sampler for solving blind inverse problems with denoising diffusion restoration N Murata, K Saito, CH Lai, Y Takida, T Uesaka, Y Mitsufuji, S Ermon ICML, 2023 | 46 | 2023 |
| FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation CH Lai, Y Takida, N Murata, T Uesaka, Y Mitsufuji, S Ermon ICML, 18365-18398, 2023 | 43* | 2023 |
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