Music Enhancement with Deep Filters: A Technical Report for The ICASSP 2024 Cadenza Challenge
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866917642370023424 |
|---|---|
| author | Shao, Keren Chen, Ke Dubnov, Shlomo |
| author_facet | Shao, Keren Chen, Ke Dubnov, Shlomo |
| contents | In this challenge, we disentangle the deep filters from the original DeepfilterNet and incorporate them into our Spec-UNet-based network to further improve a hybrid Demucs (hdemucs) based remixing pipeline. The motivation behind the use of the deep filter component lies at its potential in better handling temporal fine structures. We demonstrate an incremental improvement in both the Signal-to-Distortion Ratio (SDR) and the Hearing Aid Audio Quality Index (HAAQI) metrics when comparing the performance of hdemucs against different versions of our model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_11116 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Music Enhancement with Deep Filters: A Technical Report for The ICASSP 2024 Cadenza Challenge Shao, Keren Chen, Ke Dubnov, Shlomo Sound Artificial Intelligence Machine Learning Multimedia Audio and Speech Processing In this challenge, we disentangle the deep filters from the original DeepfilterNet and incorporate them into our Spec-UNet-based network to further improve a hybrid Demucs (hdemucs) based remixing pipeline. The motivation behind the use of the deep filter component lies at its potential in better handling temporal fine structures. We demonstrate an incremental improvement in both the Signal-to-Distortion Ratio (SDR) and the Hearing Aid Audio Quality Index (HAAQI) metrics when comparing the performance of hdemucs against different versions of our model. |
| title | Music Enhancement with Deep Filters: A Technical Report for The ICASSP 2024 Cadenza Challenge |
| topic | Sound Artificial Intelligence Machine Learning Multimedia Audio and Speech Processing |
| url | https://arxiv.org/abs/2404.11116 |