# The ICASSP SP Cadenza Challenge: Music Demixing/Remixing for Hearing Aids

arXiv: https://arxiv.org/abs/2310.03480 · HTML: https://mlsanity.com/paper/2310.03480/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 5 Oct 2023.

## Builds on (most cited first, 2 of 2)

- [Hybrid Spectrogram and Waveform Source Separation](https://mlsanity.com/paper/2111.03600/) (2021)
- [Moisesdb: A dataset for source separation beyond 4-stems](https://mlsanity.com/paper/2307.15913/) (2023)

## Built on by (most cited first, 3 of 3)

- [Remixing Music for Hearing Aids Using Ensemble of Fine-Tuned Source Separators](https://mlsanity.com/paper/2401.06203/) (2024)
- [Music Enhancement with Deep Filters: A Technical Report for The ICASSP 2024 Cadenza Challenge](https://mlsanity.com/paper/2404.11116/) (2024)
- [The first Cadenza challenges: using machine learning competitions to improve music for listeners with a hearing loss](https://mlsanity.com/paper/2409.05095/) (2024)

## Abstract (arXiv)

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.