# Music Enhancement with Deep Filters: A Technical Report for The ICASSP 2024 Cadenza Challenge

arXiv: https://arxiv.org/abs/2404.11116 · HTML: https://mlsanity.com/paper/2404.11116/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 17 Apr 2024.

## Builds on (most cited first, 5 of 5)

- [Hybrid Spectrogram and Waveform Source Separation](https://mlsanity.com/paper/2111.03600/) (2021)
- [DeepFilterNet: A Low Complexity Speech Enhancement Framework for Full-Band Audio based on Deep Filtering](https://mlsanity.com/paper/2110.05588/) (2021)
- [High Fidelity Speech Enhancement with Band-split RNN](https://mlsanity.com/paper/2212.00406/) (2022)
- [Deep Multi-Frame Filtering for Hearing Aids](https://mlsanity.com/paper/2305.08225/) (2023)
- [The ICASSP SP Cadenza Challenge: Music Demixing/Remixing for Hearing Aids](https://mlsanity.com/paper/2310.03480/) (2023)

## Built on by (most cited first, 1 of 1)

- [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)

In this challenge, we disentangle the deep filters from the original DeepfilterNet and incorporate them into our Spec-UNet-based network to further improve a hybrid Demucs (hdemucs) based remixing pipeline. The motivation behind the use of the deep filter component lies at its potential in better handling temporal fine structures. We demonstrate an incremental improvement in both the Signal-to-Distortion Ratio (SDR) and the Hearing Aid Audio Quality Index (HAAQI) metrics when comparing the performance of hdemucs against different versions of our model.