Environment and Listener Optimised Speech Processing for Hearing Enhancement in Real Situations (ELO-SPHERES)
Lead Research Organisation:
Imperial College London
Department Name: Electrical and Electronic Engineering
Abstract
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Publications
D'Olne E
(2021)
Model-based Beamforming for Wearable Microphone Arrays
D'Olne E.
(2022)
Speech Enhancement in Distributed Microphone Arrays Using Polynomial Eigenvalue Decomposition
in European Signal Processing Conference
Green T
(2022)
Speech recognition with a hearing-aid processing scheme combining beamforming with mask-informed speech enhancement.
in Trends in hearing
Grinstein E
(2023)
Dual input neural networks for positional sound source localization
in EURASIP Journal on Audio, Speech, and Music Processing
Guiraud P
(2022)
Using a Single-Channel Reference with the Mbstoi Binaural Intelligibility Metric
in SSRN Electronic Journal
Guiraud P
(2023)
Using a single-channel reference with the MBSTOI binaural intelligibility metric
in Speech Communication
Hafezi S
(2021)
Narrowband multi-source direction-of-arrival estimation in the spherical harmonic domain.
in The Journal of the Acoustical Society of America
| Description | The intelligibility of binaural speech can be estimated using a machine learning model with an accuracy comparable to, and in some cases better than, the classical metric. |
| Exploitation Route | Dissemination of our results has been done and, together with sharing our software, will enable others to estimate measures of binaural intelligibility better than before. |
| Sectors | Communities and Social Services/Policy,Digital/Communication/Information Technologies (including Software),Electronics,Healthcare |
| Title | Moore, Alastair H; Green, Tim 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 |
| Description | Materials of data and methodology |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| Impact | Research paper |
| URL | https://zenodo.org/record/6889160#.Y_za6C-l3GA |
| 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 |