The First Cadenza Signal Processing Challenge: Improving Music for Those With a Hearing Loss
The Cadenza project aims to improve the audio quality of music for those who have a hearing loss. This is being done through a series of signal processing challenges, to foster better and more inclusive technologies. In the first round, two common listening scenarios are considered: listening to music over headphones, and with a hearing aid in a car. The first scenario is cast as a demixing-remixing problem, where the music is decomposed into vocals, bass, drums and other components. These can then be intelligently remixed in a personalized way, to increase the audio quality for a person who has a hearing loss. In the second scenario, music is coming from car loudspeakers, and the music has to be enhanced to overcome the masking effect of the car noise. This is done by taking into account the music, the hearing ability of the listener, the hearing aid and the speed of the car. The audio quality of the submissions will be evaluated using the Hearing Aid Audio Quality Index (HAAQI) for objective assessment and by a panel of people with hearing loss for subjective evaluation.
Code (1)
Tasks
Cadenza 1 - Task 1 - HeadphoneCadenza 1 - Task 2 - In CarMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
The ICASSP SP Cadenza Challenge: Music Demixing/Remixing for Hearing Aids
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 hearin…
Remixing Music for Hearing Aids Using Ensemble of Fine-Tuned Source Separators
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, achievi…
Source Separation of Small Classical Ensembles: Challenges and Opportunities
Musical (MSS) source separation of western popular music using non-causal deep learning can be very effective. In contrast, MSS for classical music is an unsolved problem. Classical ensembles are harder to separate than …
Music Enhancement with Deep Filters: A Technical Report for The ICASSP 2024 Cadenza Challenge
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 motivatio…
Sub-band and Full-band Interactive U-Net with DPRNN for Demixing Cross-talk Stereo Music
This paper presents a detailed description of our proposed methods for the ICASSP 2024 Cadenza Challenge. Experimental results show that the proposed system can achieve better performance than official baselines.