Environment and Listener Optimised Speech Processing for Hearing Enhancement in Real Situations (ELO-SPHERES)

Lead Research Organisation: University College London
Department Name: Speech Hearing and Phonetic Science

Abstract

Although modern hearing aids offer the potential to exploit advanced signal processing techniques, the experience and capabilities of hearing impaired listeners are still unsatisfactory in many everyday listening situations. In part this is because hearing aids reduce or remove subtle differences in the signals received at the two ears. The normally-hearing auditory system uses such differences to determine the location of sound sources in the environment, separate wanted from unwanted sounds and allow attention to be focused on a particular talker in a noisy, multi-talker environment.

True binaural hearing aids - in which the sound processing that takes place in the left and right ears is coordinated rather than independent - are just becoming available. However practitioners' knowledge of how best to match their potential to the requirements of impaired listeners and listening situations is still very limited. One significant problem is that typical existing listening tests do not reflect the complexity of real-world listening situations, in which there may be many sound sources which may move around and listeners move their heads and also use visual information. A second important issue is that HI listeners vary widely in their underlying spatial hearing abilities.

This project aims to understand better the problems of hearing impaired listeners in noisy, multiple-talker conversations, particularly with regard to (i) their abilities to attend to and recognise speech coming from different directions while listening through binaural aids, (ii) their use of audio-visual cues. We will develop new techniques for coordinated processing of the signals arriving at the different ears that will allow identification of the locations and characteristics of different sound sources in complex environments and tailor the information presented to match the individual listener's pattern of hearing loss. We will build virtual reality simulations of complex listening environments and develop audio-visual tests to assess the abilities of listeners. We will investigate how the abilities of hearing-impaired listeners vary with their degree of impairment and the complexity of the environment.

This research project is a timely and focussed addition to knowledge and techniques to realise the potential of binaural hearing aids. Its outcomes will provide solutions to some key problems faced by hearing aid users in noisy, multiple-talker situations.

Planned Impact

The key components of this project will have significant impact on the understanding of hearing impairment and on the development of more effective binaural hearing aids. Work on understanding the spatial characteristics of a listening environment will have value in modelling the influence room acoustics has on hearing and in building better VR simulations of those environments. Work on multi-modal hearing assessment using VR will lead to a better understanding of the scientific characterisation of hearing impairment and its consequences in difficult listening situations. Work on optimising spatial audio processing techniques to render auditory scenes matched to the listener will lead to scientific advances in modelling attention and speech intelligibility and to technological advances in spatial signal processing able to cope with moving sources and listeners. The outcomes of the project will encourage new research into spatial hearing, the clinical assessment of hearing impairment, the fitting of binaural hearing aids and the training of HI listeners to make best use of their aids. The VR simulation of hearing impairment will have significant impact in the public awareness of hearing disability.
 
Description A number of commercial binaural hearing aids have been evaluated against each other and against simple loudness compensation using a group of hearing-impaired listeners in four different listening scenarios . Results show that no single aid is best for all listeners and no single aid is best in all listening situations for a given listener. In some situations, commercial aids perform worse than the simple compensation strategy. Opportunities exist to improve the way in which hearing aids are fitted to listeners, and how hearing aids adapt to the listening situation.
Exploitation Route The speech intelligibility testing platforms and speech datasets generated in the project have been made available for use by others.
Sectors Digital/Communication/Information Technologies (including Software),Healthcare

 
Title HearVR - Virtual Reality Video database for audiovisual speech intelligibility assessment 
Description British English matrix sentences were recorded in our anechoic chamber against a green screen, using a 360° camera and a high-quality condenser microphone. These sentences contain 5 slots with 10 choices in each slot. Different sets of 200 sentences were recorded by 5 male and 5 female talkers sitting at a table. The individual talkers have been cropped from the videos and the green screen and table replaced by a transparent background to allow compositing of speakers in new 360° scenes. The individual matrix sentences have been extracted as 11s videos together with level-normalised monophonic audio. We have developed scripts that build multi-talker videos from these elements in a format that can be played on VR Headsets such as the Oculus 2. We have also developed a Unity application for the headsets, which will play the 360° composite videos and create spatialised sound sources for the talkers together with background noise delivered from multiple virtual loudspeakers. 
Type Of Material Database/Collection of data 
Year Produced 2023 
Provided To Others? Yes  
Impact This database facilitates experiments into audiovisual listening behaviour with multiple speakers in realistic environments involving spatialised audio. 
URL https://speechandhearing.net/hearvr/
 
Title Materials used in "Measuring audio-visual speech intelligibility under dynamic listening conditions using virtual reality" 
Description The materials in this record are the audio and video files, together with various configuration files, used by the "SEAT" software in the study described in Moore, Green, Brookes & Naylor (2022) "Measuring audio-visual speech intelligibility under dynamic listening conditions using virtual reality" They are shared in this form so that the experiment may be reproduced. For any other use please contact the authors to obtain the original database(s) from which these materials are derived. The materials were created to be compatible with v0.3 of SEAT, which is available from GitHub. Note that the materials must be placed at C:\seat_experiments\cafe_AV. 
Type Of Material Database/Collection of data 
Year Produced 2022 
Provided To Others? Yes  
Impact These data were used in the publication: "Moore, Green, Brookes & Naylor (2022) "Measuring audio-visual speech intelligibility under dynamic listening conditions using virtual reality"" 
URL https://zenodo.org/record/6889160
 
Title eBrIRD - ELOSPHERES binaural room impulse response database 
Description The ELOSPHERES binaural room impulse response database (eBrIRD) is a resource for generating audio for binaural hearing-aid (HA) experiments. It allows testing the performance of new and existing audio processing algorithms under ideal as well as real-life auditory scenes in different environments: an anechoic chamber, a restaurant, a kitchen and a car cabin. The database consists of a collection of binaural room impulse responses (BRIRs) measured with six microphones. Two microphones represent the listener's eardrums. Four microphones are located at the front and back of two behind-the-ear hearing aids placed over the listener's pinnae. The database allows simulations of head movement in the transverse plane. [Updated July 2021 to include Car cabin BRIR]. 
Type Of Material Database/Collection of data 
Year Produced 2021 
Provided To Others? Yes  
Impact Materials to use for construction of listening tests exploiting spatial audio. 
URL https://www.phon.ucl.ac.uk/resource/ebrird/
 
Description Clarity Challenges for Machine Learning for Hearing Devices 
Organisation University of Nottingham
Department School of Medicine
Country United Kingdom 
Sector Academic/University 
PI Contribution The ELO-SPHERES project team are taking part in the Clarity Enhancement and Prediction Challenges.
Collaborator Contribution The Clarity challenges are a series of machine learning challenges to enhance hearing-aid signal processing and to better predict how people perceive speech-in-noise. The ELO-SPHERES project has contributed an enhancement system to the first enhancement challenge and is taking part in future challenges.
Impact Moore, A, Sina Hafezi, Rebecca Vos, Mike Brookes, Patrick A. Naylor, Mark Huckvale, Stuart Rosen... Gaston Hilkhuysen. (2021). A binaural MVDR beamformer for the 2021 Clarity Enhancement Challenge: ELO-SPHERES consortium system description.
Start Year 2021
 
Description Clarity Challenges for Machine Learning for Hearing Devices 
Organisation University of Nottingham
Department School of Medicine
Country United Kingdom 
Sector Academic/University 
PI Contribution The ELO-SPHERES project team are taking part in the Clarity Enhancement and Prediction Challenges.
Collaborator Contribution The Clarity challenges are a series of machine learning challenges to enhance hearing-aid signal processing and to better predict how people perceive speech-in-noise. The ELO-SPHERES project has contributed an enhancement system to the first enhancement challenge and is taking part in future challenges.
Impact Moore, A, Sina Hafezi, Rebecca Vos, Mike Brookes, Patrick A. Naylor, Mark Huckvale, Stuart Rosen... Gaston Hilkhuysen. (2021). A binaural MVDR beamformer for the 2021 Clarity Enhancement Challenge: ELO-SPHERES consortium system description.
Start Year 2021
 
Description Clarity Challenges for Machine Learning for Hearing Devices 
Organisation University of Salford
Country United Kingdom 
Sector Academic/University 
PI Contribution The ELO-SPHERES project team are taking part in the Clarity Enhancement and Prediction Challenges.
Collaborator Contribution The Clarity challenges are a series of machine learning challenges to enhance hearing-aid signal processing and to better predict how people perceive speech-in-noise. The ELO-SPHERES project has contributed an enhancement system to the first enhancement challenge and is taking part in future challenges.
Impact Moore, A, Sina Hafezi, Rebecca Vos, Mike Brookes, Patrick A. Naylor, Mark Huckvale, Stuart Rosen... Gaston Hilkhuysen. (2021). A binaural MVDR beamformer for the 2021 Clarity Enhancement Challenge: ELO-SPHERES consortium system description.
Start Year 2021
 
Description Clarity Challenges for Machine Learning for Hearing Devices 
Organisation University of Salford
Country United Kingdom 
Sector Academic/University 
PI Contribution The ELO-SPHERES project team are taking part in the Clarity Enhancement and Prediction Challenges.
Collaborator Contribution The Clarity challenges are a series of machine learning challenges to enhance hearing-aid signal processing and to better predict how people perceive speech-in-noise. The ELO-SPHERES project has contributed an enhancement system to the first enhancement challenge and is taking part in future challenges.
Impact Moore, A, Sina Hafezi, Rebecca Vos, Mike Brookes, Patrick A. Naylor, Mark Huckvale, Stuart Rosen... Gaston Hilkhuysen. (2021). A binaural MVDR beamformer for the 2021 Clarity Enhancement Challenge: ELO-SPHERES consortium system description.
Start Year 2021
 
Description Clarity Challenges for Machine Learning for Hearing Devices 
Organisation University of Sheffield
Department Department of Computer Science
Country United Kingdom 
Sector Academic/University 
PI Contribution The ELO-SPHERES project team are taking part in the Clarity Enhancement and Prediction Challenges.
Collaborator Contribution The Clarity challenges are a series of machine learning challenges to enhance hearing-aid signal processing and to better predict how people perceive speech-in-noise. The ELO-SPHERES project has contributed an enhancement system to the first enhancement challenge and is taking part in future challenges.
Impact Moore, A, Sina Hafezi, Rebecca Vos, Mike Brookes, Patrick A. Naylor, Mark Huckvale, Stuart Rosen... Gaston Hilkhuysen. (2021). A binaural MVDR beamformer for the 2021 Clarity Enhancement Challenge: ELO-SPHERES consortium system description.
Start Year 2021
 
Description Clarity Challenges for Machine Learning for Hearing Devices 
Organisation University of Sheffield
Department Department of Computer Science
Country United Kingdom 
Sector Academic/University 
PI Contribution The ELO-SPHERES project team are taking part in the Clarity Enhancement and Prediction Challenges.
Collaborator Contribution The Clarity challenges are a series of machine learning challenges to enhance hearing-aid signal processing and to better predict how people perceive speech-in-noise. The ELO-SPHERES project has contributed an enhancement system to the first enhancement challenge and is taking part in future challenges.
Impact Moore, A, Sina Hafezi, Rebecca Vos, Mike Brookes, Patrick A. Naylor, Mark Huckvale, Stuart Rosen... Gaston Hilkhuysen. (2021). A binaural MVDR beamformer for the 2021 Clarity Enhancement Challenge: ELO-SPHERES consortium system description.
Start Year 2021
 
Description Thomas Simm Littler Lecture to the British Society of Audiology (BSA) 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Professional Practitioners
Results and Impact In October 2020, Stuart Rosen gave the Thomas Simm Littler Lectureship to the British Society of Audiology (BSA) This is a biennial prize awarded 'in recognition of a sustained academic contribution to hearing science and audiology. This is the BSA's most prestigious award and consists of a certificate and honorarium ...'.

Title: 'How do background sounds interfere with speech?'
Year(s) Of Engagement Activity 2020