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…
Lip-Reading Driven Deep Learning Approach for Speech Enhancement
Ahsan Adeel, Mandar Gogate, Amir Hussain +1
This paper proposes a novel lip-reading driven deep learning framework for speech enhancement. The proposed approach leverages the complementary strengths of both deep learning and…