1) We have added new piano models. The MVSep Piano model now comes in several variants based on the MDX23C, MelRoformer and SCNet Large neural net architectures. The model produces high-quality separation of music into piano and everything else. See the results in the table below. For comparison, the table shows metrics on the open model Demucs4HT (6 parts) and the old model "mdx23c (2023.08)". The SDR metric used is the higher the better.
| Algorithm name |
Validation type |
|||
| piano (SDR) | other (SDR) | |||
| Demucs4HT (6 stems) | 2.23 | 14.51 | ||
| mdx23c (2023.08, SDR: 4.79) | 4.79 | 17.07 | ||
| mdx23c (2024.09, SDR: 5.59) | 5.59 | 17.89 | ||
| MelRoformer (viperx, SDR: 5.67) | 5.67 | 17.95 | ||
| SCNet Large (2024.09, SDR: 5.89) | 5.89 | 18.16 | ||
| Ensemble (SCNet + Mel, SDR: 6.19) | 6.19 | 18.47 | ||
Listen to: demo, user demos.
2) We have updated our guitar models. A model based on the BSRoformer architecture by viperx has been added. The ensemble has also been updated. It is the one used by default. SDR on our test dataset increased from 7.18 to 7.51.
Listen to: demo, user demos
3) We added a new version of MelBand Roformer for vocals, which showed record results on Synth dataset. You can select it from the list called "Bas Curtiz edition (SDR vocals: 11.18, SDR instrument: 17.49)" in the "MelBand Roformer (vocals, instrumental)" section.
4) We added a new algorithm to the Experimental section: "Apollo MP3 Enhancer (by JusperLee)". This algorithm improves the sound quality of MP3 files compressed with a bitrate of 128 kbps or less. The algorithm is based on the paper "Apollo: Band-sequence Modeling for High-Quality Audio Restoration" and the model is available on huggingface. Below are the spectrograms for the audio compressed to 32 kbps (left) and restored by the new algorithm (right).
Listen to: demo, user demos.
5) We added the "Aspiration by Sucial" algorithm. This algorithm extracts whispers from the voice. The algorithm has limited use, but may be useful to someone. The model was published in our open models topic on github and is also available for download on huggingface.
Listen to: demo, user demos.
