Remixing Music for Hearing Aids Using Ensemble of Fine-Tuned Source Separators
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866911768701304832 |
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| author | Daly, Matthew |
| author_facet | Daly, Matthew |
| contents | This paper introduces our system submission for the Cadenza ICASSP 2024 Grand Challenge, which presents the problem of remixing and enhancing music for hearing aid users. Our system placed first in the challenge, achieving the best average Hearing-Aid Audio Quality Index (HAAQI) score on the evaluation data set. We describe the system, which uses an ensemble of deep learning music source separators that are fine tuned on the challenge data. We demonstrate the effectiveness of our system through the challenge results and analyze the importance of different system aspects through ablation studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_06203 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Remixing Music for Hearing Aids Using Ensemble of Fine-Tuned Source Separators Daly, Matthew Audio and Speech Processing Machine Learning Sound Signal Processing This paper introduces our system submission for the Cadenza ICASSP 2024 Grand Challenge, which presents the problem of remixing and enhancing music for hearing aid users. Our system placed first in the challenge, achieving the best average Hearing-Aid Audio Quality Index (HAAQI) score on the evaluation data set. We describe the system, which uses an ensemble of deep learning music source separators that are fine tuned on the challenge data. We demonstrate the effectiveness of our system through the challenge results and analyze the importance of different system aspects through ablation studies. |
| title | Remixing Music for Hearing Aids Using Ensemble of Fine-Tuned Source Separators |
| topic | Audio and Speech Processing Machine Learning Sound Signal Processing |
| url | https://arxiv.org/abs/2401.06203 |