Prof Trevor Cox T.J.Cox@salford.ac.uk
Professor
Prof Trevor Cox T.J.Cox@salford.ac.uk
Professor
The 2nd Clarity Prediction Challenge: A Machine Learning Challenge for Hearing Aid Intelligibility Prediction (2024)
Presentation / Conference Contribution
This paper reports on the design and outcomes of the 2nd Clarity Prediction Challenge (CPC2) for predicting the intelligibility of hearing aid processed signals heard by individuals with a hearing impairment. The challenge was designed to promote new... Read More about The 2nd Clarity Prediction Challenge: A Machine Learning Challenge for Hearing Aid Intelligibility Prediction.
The ICASSP SP Cadenza Challenge: Music Demixing/Remixing for Hearing Aids (2024)
Presentation / Conference Contribution
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 hearing loss. The scenario for the c... Read More about The ICASSP SP Cadenza Challenge: Music Demixing/Remixing for Hearing Aids.
The 2nd Clarity Enhancement Challenge for Hearing Aid Speech Intelligibility Enhancement: Overview and Outcomes (2023)
Presentation / Conference Contribution
This paper reports on the design and outcomes of the 2nd Clarity Enhancement Challenge (CEC2), a challenge for stimulating novel approaches to hearing-aid speech intelligibility enhancement. The challenge was for a listener attending to a target spea... Read More about The 2nd Clarity Enhancement Challenge for Hearing Aid Speech Intelligibility Enhancement: Overview and Outcomes.
Overview of the 2023 ICASSP SP Clarity Challenge: Speech Enhancement for Hearing Aids (2023)
Presentation / Conference Contribution
This paper reports on the design and outcomes of the ICASSP SP Clarity Challenge: Speech Enhancement for Hearing Aids. The scenario was a listener attending to a target speaker in a noisy, domestic environment. There were multiple interferers and hea... Read More about Overview of the 2023 ICASSP SP Clarity Challenge: Speech Enhancement for Hearing Aids.
The 1st Clarity Prediction Challenge: A machine learning challenge for hearing aid intelligibility prediction (2022)
Presentation / Conference Contribution
This paper reports on the design and outcomes of the 1st Clarity Prediction Challenge (CPC1) for predicting the intelligibility of hearing aid processed signals heard by individuals with a hearing impairment. The challenge was designed to promote the... Read More about The 1st Clarity Prediction Challenge: A machine learning challenge for hearing aid intelligibility prediction.
Clarity-2021 challenges : machine learning challenges for advancing hearing aid processing (2021)
Presentation / Conference Contribution
In recent years, rapid advances in speech technology have been made possible by machine learning challenges such as CHiME, REVERB, Blizzard, and Hurricane. In the Clarity project, the machine learning approach is applied to the problem of hearing aid... Read More about Clarity-2021 challenges : machine learning challenges for advancing hearing aid processing.
Machine learning challenges to revolutionise hearing device processing
Presentation / Conference
Graetzer, S., Cox, T., Barker, J., Akeroyd, M., Culling, J., & Naylor, G. Machine learning challenges to revolutionise hearing device processing. Poster presented at Speech in Noise (SPiN) 2020, Toulouse, FranceIn this project, we will run a series of machine learning challenges to revolutionise speech processing for hearing devices. Over five years, there will be three paired challenges. Each pair will consist of a challenge focussed on hearing-device proc... Read More about Machine learning challenges to revolutionise hearing device processing.
Clarity : machine learning challenges to revolutionise hearing device processing
Presentation / Conference Contribution
In the Clarity project, we will run a series of machine learning challenges to revolutionise speech processing for hearing devices. Over five years, there will be three paired challenges. Each pair will consist of a competition focussed on hearing-de... Read More about Clarity : machine learning challenges to revolutionise hearing device processing.
About USIR
Administrator e-mail: library-research@salford.ac.uk
This application uses the following open-source libraries:
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Apache License Version 2.0 (http://www.apache.org/licenses/)
SIL OFL 1.1 (http://scripts.sil.org/OFL)
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CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
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