5 citations · 5 across the 4 of their papers we have counts for
5 papers
Source Separation of Small Classical Ensembles: Challenges and Opportunities
Gerardo Roa-Dabike, Trevor J. Cox, Jon P. Barker +8
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. Classica…
The first Cadenza challenges: using machine learning competitions to improve music for listeners with a hearing loss
Gerardo Roa Dabike, Michael A. Akeroyd, Scott Bannister +8
It is well established that listening to music is an issue for those with hearing loss, and hearing aids are not a universal solution. How can machine learning be used to address t…
The First Cadenza Signal Processing Challenge: Improving Music for Those With a Hearing Loss
Gerardo Roa Dabike, Scott Bannister, Jennifer Firth +8
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 bet…
The ICASSP SP Cadenza Challenge: Music Demixing/Remixing for Hearing Aids
Gerardo Roa Dabike, Michael A. Akeroyd, Scott Bannister +8
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…
The Use of Voice Source Features for Sung Speech Recognition
Gerardo Roa Dabike, Jon Barker
In this paper, we ask whether vocal source features (pitch, shimmer, jitter, etc) can improve the performance of automatic sung speech recognition, arguing that conclusions previou…