mlsanityData to 14 Oct 2026

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

arXiv 2310.03480 ↗First posted 5 Oct 2023eess.AS cs.LG eess.SPcited by 3

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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.

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

2 of 14 references.

References · 2

  1. 2021Hybrid Spectrogram and Waveform Source Separationcited by 91
  2. 2023Moisesdb: A dataset for source separation beyond 4-stemscited by 39

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Cited by year

Citing papers per year: 2024: 3.

3 papers.

  1. 2024Remixing Music for Hearing Aids Using Ensemble of Fine-Tuned Source Separatorscited by 1
  2. 2024Music Enhancement with Deep Filters: A Technical Report for The ICASSP 2024 Cadenza Challengecited by 1
  3. 2024The first Cadenza challenges: using machine learning competitions to improve music for listeners with a hearing losscited by 1