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