Update README.md

README.md

@@ -1,3 +1,42 @@
 ---
 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)