Challenges to Revolutionise Hearing Device Processing
Lead Research Organisation:
University of Salford
Department Name: Sch of Science,Engineering & Environment
Abstract
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Organisations
People |
ORCID iD |
| Trevor Cox (Principal Investigator) |
Publications
Akeroyd M
(2020)
Launching the first "Clarity" Machine Learning Challenge to revolutionise hearing device processing
in The Journal of the Acoustical Society of America
Akeroyd M
(2024)
The Clarity & Cadenza Challenges
Akeroyd M
(2023)
Results of the second "clarity" enhancement challenge for hearing devices
in The Journal of the Acoustical Society of America
Akeroyd M.A.
(2022)
Predicting Speech Intelligibility for People with Hearing Loss: The Clarity Challenges
in Internoise 2022 - 51st International Congress and Exposition on Noise Control Engineering
Cox T
(2023)
Predicting Speech Intelligibility for People with a Hearing Loss: The Clarity Challenges
in INTER-NOISE and NOISE-CON Congress and Conference Proceedings
Graetzer S
(2022)
Dataset of British English speech recordings for psychoacoustics and speech processing research: The clarity speech corpus.
in Data in brief
| Description | We have run five competitions aimed at improving the signal processing in hearing aids; two main "enhancement" challenges (CEC1 and CEC2), two "prediction" challenges (CPC1 and CPC2) and a supplementary "ICASSP" enhancement challenge associated, based on live-recorded stimuli. In each enhancement challenge we supply a very large number of speech-in-noise signals, representing someone listening to a talker in a room but made difficult by there being other background sounds on at the same time, plus software for modelling what a hearing aid will do to the sounds and predicting how many words will be heard. Any research group around the world can enter; the challenge is to improve the number of words or how well they can match what real listeners do. Each competition is designed to be a harder situation than the preceding one. Each "prediction" challenge involves trying to predict the intelligibility of the materials generated by the enhancement challenges for individuals with specified hearing impairments. Some of the enhancement challenge entrants have achieved dramatic improvements in intelligibility, largely by removing the interfering sound sources using deep-learning methods. The best entrants to the last prediction challenge exploited large pre-trained speech models to improve on previous state-of-the-art. The next challenge, CEC3, is currently under development. |
| Exploitation Route | They will be useful to hearing-aid developers and also those interested in auditory theory and how hearing problems affect listening. They can allow companies developing speech technologies to make those accessible to those with hearing loss. |
| Sectors | Creative Economy Digital/Communication/Information Technologies (including Software) |
| URL | https://claritychallenge.org/ |
| Description | Organised the first open Machine Learning Challenge for enhancing the processing of hearing aids in 2021. This was part of developing a new research area and growing the number of researchers considering hearing loss and speech technology. Non-academic entrants were from: Music Tribe; Google and Tancent. Academic entrants were: Brno University of Technology; ELO-SPHERES Consortium; University of Oldenburg; University of Sheffield; University Hannover and Shenzhen University. The results were presented at the The Clarity Workshop on Machine Learning Challenges for Hearing Aids (September 2021). Organised the first open Speech Intelligibility Prediction Challenge for people with a hearing loss listening via hearing aid. Ten entrants came from a variety of universities in Taiwan, China, Japan, Germany and UK. The results were presented at the The 2nd Clarity Workshop on Machine Learning Challenges for Hearing Aids (June 2022) Organised the second open Machine Learning Challenge for enhancing the processing of hearing aids in 2022. The second challenge substantially increased the difficulty of the problem by including multiple interferers and head movement during the stimulus. This second challenge was also used as the basis for an ICASSP Signal Processing grand challenge in 2022/23. The ICASSP challenge also included a more ecologically-valid and independent evaluation set. There were 10 teams between these two challenges. The results were presented at the 2022 2nd Clarity Workshop on Machine Learning Challenges for Hearing Aids (December 2022) and at ICASSP 2023. Organised 4th ISCA Clarity Workshop on Machine Learning Challenges for Hearing Aids at InterSpeech 2023 in Dublin. |
| First Year Of Impact | 2021 |
| Sector | Digital/Communication/Information Technologies (including Software),Other |
| Impact Types | Cultural |
| Title | Dataset of British English speech recordings for psychoacoustics and speech processing research |
| Description | This paper presents the Clarity Speech Corpus, a publicly available, forty speaker British English speech dataset. The corpus was created for the purpose of running listening tests to gauge speech intelligibility and quality in the Clarity Project, which has the goal of advancing speech signal processing by hearing aids through a series of challenges. The dataset is suitable for machine learning and other uses in speech and hearing technology, acoustics and psychoacoustics. The data comprises recordings of approximately 10,000 sentences drawn from the British National Corpus (BNC) with suitable length, words and grammatical construction for speech intelligibility testing. The collection process involved the selection of a subset of BNC sentences, the recording of these produced by 40 British English speakers, and the processing of these recordings to create individual sentence recordings with associated transcripts and metadata. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| Impact | Has been used in the Clarity Machine Learning Challenges. Only just published, so too soon for other impacts, |
| URL | https://salford.figshare.com/articles/dataset/Dataset_of_British_English_speech_recordings_for_psych... |
| Title | claritychallenge/clarity_CC: clarity_CC release v1.2 |
| Description | Software to support the Clarity Enhancement and Prediction Challenges. Python tools are provided for • Mixing scenes to generate the training and development data • Running the baseline hearing aid processing using the Oldenburg/Hoertech open Master Hearing Aid (openMHA) software platform • Running signals through a python implementation of the Moore, Stone, Baer and Glasberg (MSBG) hearing loss model • Estimating intelligibility using the Modified Binaural Short-Time Objective Intelligibility measure (MBSTOI). This version includes • The first release of the code for supporting the recently opened 1st Clarity Prediction Challenge (CPC1) • Code for supporting the (now closed) 1st Clarity Enhancement Challenge (CEC1) |
| Type Of Technology | Software |
| Year Produced | 2021 |
| Impact | Used in the Clarity Machine Learning Challenges. |
| URL | https://zenodo.org/record/4593856 |
| Title | pyclarity |
| Description | pyclarity is a software suite for machine learning challenges to enhance hearing-aid signal processing and to better predict how people perceive speech-in-noise (Clarity) and speech-in-music (Cadenza). Files can be accessed via the Related Materials links below. |
| Type Of Technology | Software |
| Year Produced | 2023 |
| Open Source License? | Yes |
| URL | https://figshare.shef.ac.uk/articles/software/pyclarity/23230694 |
| Description | The 4th Clarity Workshop on Machine Learning Challenges for Hearing Aids (Clarity-2023) |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | The 4th Clarity Workshop on Machine Learning Challenges for Hearing Aids (Clarity-2023) was an in-person Workshop held in Dublin on 19th August 2023 and organised by the Clarity project team. It was supported by ISCA (International Speech Communication Association) and held as an official satellite event of the main annual ISCA conference, Interspeech. The workshop had attendees from the US, Europe and Asia and from both academia and industry. The event features invited talks from academia and industry (Meta Reality Labs, Sonova AG); and from a hearing aid user with audiological expertise providing insights regarding unsolved problems from a user perspective. The purpose of the event was to disseminate our project's findings arising from the 2nd Clarity Prediction Challenge; to foster discussion between academia and industry on the topic of the potential for machine learning in the hearing aid industry; and as an opportunity to get input from industry and academia concerning the directions for future datasets and challenges being created by the project. The input from the discussions has contributed to the design of the current round of data collection and the new design for speech intelligibility enhancement evaluation that we will be launching in 2024 as the 3rd Clarity Enhancement Challenge. |
| Year(s) Of Engagement Activity | 2023 |
| URL | https://claritychallenge.org/clarity2023-workshop/ |