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Trevor J. Cox

5 accepted papers

2024

The 2nd Clarity Prediction Challenge: A Machine Learning Challenge for Hearing Aid Intelligibility Prediction

ICASSP 2024accepted

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 approaches for estimating the intelligibility of…

Cited by 0SourceScholar
2023

Overview of the 2023 ICASSP SP Clarity Challenge: Speech Enhancement for Hearing Aids

ICASSP 2023accepted

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 head rotation by the listener. The challenge extended…

Cited by 0SourceScholar
2023

The 2nd Clarity Enhancement Challenge for Hearing Aid Speech Intelligibility Enhancement: Overview and Outcomes

ICASSP 2023accepted

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 speaker in a noisy, domestic environment. The challeng…

Cited by 0SourceScholar
2019

Background Adaptation for Improved Listening Experience in Broadcasting

ICASSP 2019accepted

The intelligibility of speech in noise can be improved by modifying the speech. But with object-based audio, there is the possibility of altering the background sound while leaving the speech unaltered. This may prove a less intrusive approach, affording good speech intelligibility without overly co…

Cited by 0SourceScholar
2019

Generalisation in Environmental Sound Classification: The 'Making Sense of Sounds' Data Set and Challenge

ICASSP 2019accepted

Humans are able to identify a large number of environmental sounds and categorise them according to high-level semantic categories, e.g. urban sounds or music. They are also capable of generalising from past experience to new sounds when applying these categories. In this paper we report on the crea…

Cited by 14SourceScholar