2000 character limit reached
Remixing Music for Hearing Aids Using Ensemble of Fine-Tuned Source Separators (2401.06203v2)
Published 11 Jan 2024 in eess.AS, cs.LG, cs.SD, and eess.SP
Abstract: 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.
- “Overview and results of the ICASSP SP cadenza challenge: Music demixing/remixing for hearing aids,” in 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024.
- “The hearing-aid audio quality index (HAAQI),” IEEE/ACM transactions on audio, speech, and language processing, vol. 24, no. 2, pp. 354–365, 2015.
- Alexandre Défossez, “Hybrid spectrogram and waveform source separation,” arXiv preprint arXiv:2111.03600, 2021.
- “MUSDB18-HQ - an uncompressed version of musdb18,” Dec. 2019.
- “The national acoustic laboratories’(NAL) new procedure for selecting the gain and frequency response of a hearing aid,” Ear and hearing, vol. 7, no. 4, pp. 257–265, 1986.
- “Music source separation based on a lightweight deep learning framework (DTTNET: Dual-path TFC-TDF UNet),” 2023.
- “KUIELab-MDX-Net: A two-stream neural network for music demixing,” 2021.
- Yi Luo and Jianwei Yu, “Music source separation with band-split rnn,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 31, pp. 1893–1901, 2023.
- Matthew Daly (7 papers)