Subject: [AUDITORY] Pre-announcing the First Clarity Enhancement Challenge for Hearing Aid Signal Processing From: Jon Barker <j.p.barker@xxxxxxxx> Date: Thu, 26 Nov 2020 09:52:17 +0000--0000000000007804c005b4ff807c Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: quoted-printable Pre-announcing the First Clarity Enhancement Challenge for Hearing Aid Signal Processing -- Launching January 2021 -- *Background* We are organising a series of machine learning challenges to advance hearing aid speech signal processing. Even if you=E2=80=99ve not worked on = hearing aids before, we=E2=80=99ll provide you with the tools to enable you to appl= y your machine learning and speech processing algorithms to help those with a hearing loss. Although age-related hearing loss affects 40% of 55 to 74 year-olds, the majority of adults who would benefit from hearing aids don=E2=80=99t use th= em. A key reason is simply that hearing aids don=E2=80=99t provide enough benefit= . In particular, speech in noise is still a critical problem, even for the most sophisticated devices. The purpose of the =E2=80=9CClarity=E2=80=9D challen= ges is to catalyse new work to radically improve the speech intelligibility provided by hearing aids. The series of challenges will consider increasingly complex listening scenarios. The first round, launching in January 2021, will focus on speech in indoor environments in the presence of a single interferer. It will begin with a challenge involving improving hearing aid processing. Future challenges on how to model speech-in-noise perception will be launched at a later date. *The task* You will be provided with simulated scenes, each including a target speaker and interfering noise. For each scene, there will be signals that simulate those captured by a behind-the-ear hearing aid with 3-channels at each ear and those captured at the eardrum without a hearing aid present. The target speech will be a short sentence and the interfering noise will be either speech or domestic appliance noise. The task will be to deliver a hearing aid signal processing algorithm that can improve the intelligibility of the target speaker for a specified hearing-impaired listener. Initially, entries will be evaluated using an objective speech intelligibility measure. Subsequently, up to twenty of the most promising systems will be evaluated by a panel of listeners. We will provide a baseline system so that teams can choose to focus on individual components or to develop their own complete pipelines. *What will be provided* - Evaluation of the best entries by a panel of hearing-impaired listeners. - Speech + interferer scenes for training and evaluation. - An entirely new database of 10,000 spoken sentences - Listener characterisations including audiograms and speech-in-noise testing. - Software including tools for generating training data, a baseline hearing aid algorithm, a baseline model of hearing impairment, and a binaural objective intelligibility measure. *Important Dates* - January 2021 - Challenge launch and release of software and data - April 2021 - Evaluation data released - May 2021 - Submission deadline - June-August 2021 - Listening test evaluation period - September 2021 - Results announced at a Clarity Challenge Workshop in conjunction with Interspeech 2021 Challenge and workshop participants will be invited to contribute to a journal Special Issue on the topic of Machine Learning for Hearing Aid Processing that will be announced next year. *For further information*If you are interested in participating and wish to receive further information, please sign up to the Clarity Forum at http://claritychallenge.org/sign-up-to-the-challenges If you have questions, contact us directly at contact@xxxxxxxx *Organisers*Prof. Jon P. Barker, Department of Computer Science, University of Sheffield Prof. Michael A. Akeroyd, Hearing Sciences, School of Medicine, University of Nottingham Prof. Trevor J. Cox, Acoustics Research Centre, University of Salford Prof. John F. Culling, School of Psychology, Cardiff University Prof. Graham Naylor, Hearing Sciences, School of Medicine, University of Nottingham Dr Simone Graetzer, Acoustics Research Centre, University of Salford Dr Rhoddy Viveros Mu=C3=B1oz, School of Psychology, Cardiff University Eszter Porter, Hearing Sciences, School of Medicine, University of Nottingham Funded by the Engineering and Physical Sciences Research Council (EPSRC), U= K Supported by RNID (formerly Action on Hearing Loss), Hearing Industry Research Consortium, Amazon TTS Research, Honda Research Institute Europe --=20 Professor Jon Barker, Department of Computer Science, University of Sheffield +44 (0) 114 222 1824 --0000000000007804c005b4ff807c Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable <div dir=3D"ltr">Pre-announcing the First Clarity Enhancement Challenge for= Hearing Aid Signal Processing<br>-- Launching January 2021 --<div><br><b>B= ackground<br></b><br>We are organising a series of machine learning challen= ges to advance hearing aid speech signal processing. Even if you=E2=80=99ve= not worked on hearing aids before, we=E2=80=99ll provide you with the tool= s to enable you to apply your machine learning and speech processing algori= thms to help those with a hearing loss.<br><br>Although age-related hearing= loss affects 40% of 55 to 74 year-olds, the majority of adults who would b= enefit from hearing aids don=E2=80=99t use them. A key reason is simply tha= t hearing aids don=E2=80=99t provide enough benefit. In particular, speech = in noise is still a critical problem, even for the most sophisticated devic= es. The purpose of the =E2=80=9CClarity=E2=80=9D challenges is to catalyse = new work to radically improve the speech intelligibility provided by hearin= g aids.<br><br>The series of challenges will consider increasingly complex = listening scenarios. The first round, launching in January 2021, will focus= on speech in indoor environments in the presence of a single interferer. I= t will begin with a challenge involving improving hearing aid processing. F= uture challenges on how to model speech-in-noise perception will be launche= d at a later date.<br><br><b>The task<br></b><br>You will be provided with = simulated scenes, each including a target speaker and interfering noise. Fo= r each scene, there will be signals that simulate those captured by a behin= d-the-ear hearing aid with 3-channels at each ear and those captured at the= eardrum without a hearing aid present.=C2=A0 The target speech will be a s= hort sentence and the interfering noise will be either speech or domestic a= ppliance noise.<br><br>The task will be to deliver a hearing aid signal pro= cessing algorithm that can improve the intelligibility of the target speake= r for a specified hearing-impaired listener. Initially, entries will be eva= luated using an objective speech intelligibility measure. Subsequently, up = to twenty of the most promising systems will be evaluated by a panel of lis= teners.<br><br>We will provide a baseline system so that teams can choose t= o focus on individual components or to develop their own complete pipelines= .<br><br><b>What will be provided<br></b><br>- Evaluation of the best entri= es by a panel of hearing-impaired listeners.<br>- Speech + interferer scene= s for training and evaluation.<br>- An entirely new database of 10,000 spok= en sentences<br>- Listener characterisations including audiograms and speec= h-in-noise testing.<br>- Software including tools for generating training d= ata, a baseline hearing aid algorithm, a baseline model of hearing impairme= nt, and a binaural objective intelligibility measure.<br><br><b>Important D= ates<br></b><br>- January 2021 - Challenge launch and release of software a= nd data<br>- April 2021 - =C2=A0Evaluation data released<br>- May 2021 - Su= bmission deadline<br>- June-August 2021 =C2=A0- Listening test evaluation p= eriod<br>- September 2021 - Results announced at a Clarity Challenge Worksh= op in conjunction with Interspeech 2021<br><br>Challenge and workshop parti= cipants will be invited to contribute to a journal Special Issue on the top= ic of Machine Learning for Hearing Aid Processing that will be announced ne= xt year.<br><br><b>For further information<br></b>If you are interested in = participating and wish to receive further information, please sign up to th= e Clarity Forum at=C2=A0<a href=3D"http://claritychallenge.org/sign-up-to-t= he-challenges" target=3D"_blank">http://claritychallenge.org/sign-up-to-the= -challenges</a><br>If you have questions, contact us directly at=C2=A0<a hr= ef=3D"mailto:contact@xxxxxxxx" target=3D"_blank">contact@xxxxxxxx= ychallenge.org</a><br><br><b>Organisers<br></b>Prof. Jon P. Barker, Departm= ent of Computer Science, University of Sheffield<br>Prof. Michael A. Akeroy= d, Hearing Sciences, School of Medicine, University of Nottingham<br>Prof. = Trevor J. Cox, Acoustics Research Centre, University of Salford<br>Prof. Jo= hn F. Culling, School of Psychology, Cardiff University<br>Prof. Graham Nay= lor, Hearing Sciences, School of Medicine, University of Nottingham<br>Dr S= imone Graetzer, Acoustics Research Centre, University of Salford<br>Dr Rhod= dy Viveros Mu=C3=B1oz, School of Psychology, Cardiff University<br>Eszter P= orter, Hearing Sciences, School of Medicine, University of Nottingham<br><b= r>Funded by the Engineering and Physical Sciences Research Council (EPSRC),= UK<br><br>Supported by RNID (formerly Action on Hearing Loss), Hearing Ind= ustry Research Consortium, Amazon TTS Research, Honda Research Institute Eu= rope<br clear=3D"all"><div><br></div>-- <br><div dir=3D"ltr" class=3D"gmail= _signature" data-smartmail=3D"gmail_signature"><div dir=3D"ltr"><div><div d= ir=3D"ltr">Professor Jon Barker,<div><div>Department of Computer Science,</= div><div>University of Sheffield</div><div>+44 (0) 114 222 1824</div><div><= br></div></div></div></div></div></div></div></div> --0000000000007804c005b4ff807c--