Outputs (2)

The First Cadenza Challenges: Using Machine Learning Competitions to Improve Music for Listeners With a Hearing Loss (2025)
Journal Article
Roa-Dabike, G., Akeroyd, M. A., Bannister, S., Barker, J. P., Cox, T. J., Fazenda, B., Firth, J., Graetzer, S., Greasley, A., Vos, R. R., & Whitmer, W. M. (2025). The First Cadenza Challenges: Using Machine Learning Competitions to Improve Music for Listeners With a Hearing Loss. IEEE Open Journal of Signal Processing, 6, 722-734. https://doi.org/10.1109/OJSP.2025.3578299

Listening to music can be an issue for those with a hearing impairment, and hearing aids are not a universal solution. This paper details the first use of an open challenge methodology to improve the audio quality of music for those with hearing loss... Read More about The First Cadenza Challenges: Using Machine Learning Competitions to Improve Music for Listeners With a Hearing Loss.

Muddy, muddled, or muffled? Understanding the perception of audio quality in music by hearing aid users (2024)
Journal Article
Bannister, S., Greasley, A. E., Cox, T. J., Akeroyd, M. A., Barker, J., Fazenda, B., Firth, J., Graetzer, S. N., Roa Dabike, G., Vos, R. R., & Whitmer, W. M. (2024). Muddy, muddled, or muffled? Understanding the perception of audio quality in music by hearing aid users. Frontiers in Psychology, 15, Article 1310176. https://doi.org/10.3389/fpsyg.2024.1310176

Introduction: Previous work on audio quality evaluation has demonstrated a developing convergence of the key perceptual attributes underlying judgments of quality, such as timbral, spatial and technical attributes. However, across existing research t... Read More about Muddy, muddled, or muffled? Understanding the perception of audio quality in music by hearing aid users.