  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.
