The second Cadenza machine learning challenge (CAD2): Improving music for people with hearing loss

The Cadenza machine learning challenges are improving the processing of music in hearing aids and consumer devices for those with hearing loss. There are two tasks in the current round (CAD2), which is organized within the IEEE SPS program. The tasks are motivated by the problem people with hearing loss can have when trying to hear out lyrics and instruments. Task 1 is to improve lyric intelligibility for pop/rock music without compromising audio quality. The objective audio quality is evaluated using HAAQI, the Hearing Aid Audio Quality Index, and intelligibility by a system based on the Whisper ASR (automatic-speech recognition). Subjective audio quality and intelligibility are also evaluated via a panel of listeners with hearing loss. Task 2 is to rebalance instruments within small classical ensembles (duets to quintets of strings and woodwinds) to enable personalized remixing. The objective metric is HAAQI. Both tasks start with stereo recordings. We will present the challenge design, a summary of the approaches taken by challenge entrants, and an overview of the objective and perceptual evaluation of the systems entered. We will also discuss how CAD2 will inform the third Cadenza challenge, launching later in 2025.