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--- |
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license: mit |
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language: |
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- ja |
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tags: |
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- music |
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- audio |
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- audio-to-audio |
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- SFI |
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datasets: |
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- MUSDB18-HQ |
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metrics: |
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- SDR |
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--- |
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# Sampling-frequency-independent (SFI) Conv-TasNet trained with the MUSDB18-HQ dataset for music source separation |
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This model was proposed in [our IEEE/ACM Trans. ASLP paper](https://doi.org/10.1109/TASLP.2022.3203907) and works well with untrained sampling frequencies by using sampling-frequency-independent convolutional layers with the time domain filter design. |
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The latent analog filter is a multiphase gammatone filter. |
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It was trained by Tomohiko Nakamura using [the codebase](https://github.com/TomohikoNakamura/sfi_convtasnet)). |
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This model was trained with 32 kHz-sampled data but works well with untrained sampling frequencies (e.g., 8, 16 kHz). |
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# License |
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MIT |
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# Citation |
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Please cite the following paper. |
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``` |
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@article{KSaito2022IEEEACMTASLP, |
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author={Saito, Koichi and Nakamura, Tomohiko and Yatabe, Kohei and Saruwatari, Hiroshi}, |
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journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing}, |
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title = {Sampling-frequency-independent convolutional layer and its application to audio source separation}, |
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year=2022, |
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month=sep, |
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volume=30, |
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pages={2928--2943}, |
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doi={10.1109/TASLP.2022.3203907}, |
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} |
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``` |
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# Contents |
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- Four trained models (seed=40,42,44,47) |
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- Evaluation results (json files obtained with the museval library) |
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