The ICASSP SP Cadenza Challenge: Music Demixing/Remixing for Hearing Aids
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866918057457221632 |
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| author | Dabike, Gerardo Roa Akeroyd, Michael A. Bannister, Scott Barker, Jon Cox, Trevor J. Fazenda, Bruno Firth, Jennifer Graetzer, Simone Greasley, Alinka Vos, Rebecca R. Whitmer, William M. |
| author_facet | Dabike, Gerardo Roa Akeroyd, Michael A. Bannister, Scott Barker, Jon Cox, Trevor J. Fazenda, Bruno Firth, Jennifer Graetzer, Simone Greasley, Alinka Vos, Rebecca R. Whitmer, William M. |
| contents | This paper reports on the design and results of the 2024 ICASSP SP Cadenza Challenge: Music Demixing/Remixing for Hearing Aids. The Cadenza project is working to enhance the audio quality of music for those with a hearing loss. The scenario for the challenge was listening to stereo reproduction over loudspeakers via hearing aids. The task was to: decompose pop/rock music into vocal, drums, bass and other (VDBO); rebalance the different tracks with specified gains and then remixing back to stereo. End-to-end approaches were also accepted. 17 systems were submitted by 11 teams. Causal systems performed poorer than non-causal approaches. 9 systems beat the baseline. A common approach was to fine-tuning pretrained demixing models. The best approach used an ensemble of models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_03480 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | The ICASSP SP Cadenza Challenge: Music Demixing/Remixing for Hearing Aids Dabike, Gerardo Roa Akeroyd, Michael A. Bannister, Scott Barker, Jon Cox, Trevor J. Fazenda, Bruno Firth, Jennifer Graetzer, Simone Greasley, Alinka Vos, Rebecca R. Whitmer, William M. Audio and Speech Processing Machine Learning Signal Processing This paper reports on the design and results of the 2024 ICASSP SP Cadenza Challenge: Music Demixing/Remixing for Hearing Aids. The Cadenza project is working to enhance the audio quality of music for those with a hearing loss. The scenario for the challenge was listening to stereo reproduction over loudspeakers via hearing aids. The task was to: decompose pop/rock music into vocal, drums, bass and other (VDBO); rebalance the different tracks with specified gains and then remixing back to stereo. End-to-end approaches were also accepted. 17 systems were submitted by 11 teams. Causal systems performed poorer than non-causal approaches. 9 systems beat the baseline. A common approach was to fine-tuning pretrained demixing models. The best approach used an ensemble of models. |
| title | The ICASSP SP Cadenza Challenge: Music Demixing/Remixing for Hearing Aids |
| topic | Audio and Speech Processing Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2310.03480 |