[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128398-en":3,"doc-seo-128398-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128398,8796095027276,"Valentina","https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=",8,"Research & Report","The first Cadenza challenges - Using machine learning competitions to improve music for listeners with a hearing loss","Listening to music is challenging for people with hearing loss, and hearing aids do not fully solve the problem. This paper applies an open challenge methodology to use machine learning to enhance audio quality for listeners with hearing loss. Two competitions are described: CAD1 with a stand-alone format, and ICASSP24 with more difficult conditions involving loudspeaker reproduction and specified gains. Tasks cover demixing/remixing pop/rock and amplification to correct elevated hearing thresholds. Evaluation uses HAAQI, showing several systems surpassed the baselines and established open benchmarks for future research.","The first Cadenza challenges: using machine learning competitions to improve music for listeners with a hearing loss  \nX  \nXX(X):1–12  \n©The Author(s) 2016  \nReprints and permission: [sagepub.co.uk/journalsPermissions.nav](sagepub.co.uk/journalsPermissions.nav)[ ](sagepub.co.uk/journalsPermissions.nav)DOI: 10.1177/ToBeAssigned [www.sagepub.com/](www.sagepub.com/)  \nSAGE  \narXiv :2409 .05095v 1 [ cs . SD] 8 Sep 2024  \nGerardo Roa Dabike1 , Michael A. Akeroyd3 , Scott Bannister2 , Jon P. Barker4 , Trevor J. Cox1 , Bruno Fazenda1 , Jennifer Firth3 , Simone Graetzer1 , Alinka Greasley2 , Rebecca R. Vos1 and William M. Whitmer3  \nAbstract  \nIt is well established that listening to music is an issue for those with hearing loss, and hearing aids are not a universal solution. How can machine learning be used to address this? This paper details the first application of the open challenge methodology to use machine learning to improve audio quality of music for those with hearing loss. The first challenge was a stand-alone competition (CAD1) and had 9 entrants. The second was an 2024 ICASSP grand challenge (ICASSP24) and attracted 17 entrants. The challenge tasks concerned demixing and remixing pop/rock music to allow a personalised rebalancing of the instruments in the mix, along with amplification to correct for raised hearing thresholds. The software baselines provided for entrants to build upon used two state-of-the-art demix algorithms: Hybrid Demucs and Open-Unmix. Evaluation of systems was done using the objective metric HAAQI, the HearingAid Audio Quality Index. No entrants improved on the best baseline in CAD1 because there was insufficient room for improvement. Consequently, for ICASSP24 the scenario was made more difficult by using loudspeaker reproduction and specified gains to be applied before remixing. This also made the scenario more useful for listening through hearing aids. 9 entrants scored better than the the best ICASSP24 baseline.  \nMost entrants used a refined version of Hybrid Demucs and NAL-R amplification. The highest scoring system combined the outputs of several demixing algorithms in an ensemble approach. These challenges are now open benchmarks for future research with the software and data being freely available.  \nIntroduction  \nMost, if not all human cultures have music (Blacking 1995) . Music brings people together, shapes society and offers significant benefits to health and well-being (MacDonald et al. 2013) . Hearing loss can detract from the listening experience, however. The World Health Organisation estimates that by 2050 2.5 billion people will have some form of hearing loss, with at least 700 million requiring treatment (World Health Organization 2021) . Hearing loss can lead toa range of challenges with music, including the inaudibility  \nof quieter passages, poor or anomalous pitch perception, and difficulty in identifying and distinguishing lyrics and instruments (Hake et al. 2023 ; Moore 2016 ; Siedenburg et al. 2020) . Therefore, it is essential to develop improved methods for processing music on hearing aids and consumer devices, enabling those with hearing loss to continue enjoying and benefiting from music.  \nThe most common intervention for mild to moderately severe hearing loss is hearing aids. Many of these devices have music programs but efficacy is mixed (Greasley et al. 2020 ; Madsen and Moore 2014 ; Looi et al. 2019 ; Vaisberg et al. 2019) . For example, Greasley et al. (2020) found that 68% of users report difficulties when listening to music through their hearing aids. The issue is complicated because hearing aids are typically frequency-dependent, nonlinear amplifiers to compensate for an individual’s elevated thresholds, which must also allow for the rapid growth in loudness with low-intensity sound (loudness recruitment) and the potential discomfort from overcompensating louder sounds. These wide-dynamic range compression systems (WDRC) should make incoming sound audible and ","cbCaifZrUy7SheLS","https://ap.wps.com/l/cbCaifZrUy7SheLS","pdf",1131209,1,12,"English","en",105,"# Abstract\n# Introduction\n## Background: music and hearing loss\n## Limitations of hearing aids for music\n## Opportunities for signal processing and ML approaches","[{\"question\":\"What problem do the “first Cadenza challenges” address?\",\"answer\":\"They address how to use machine learning to improve music audio quality for listeners with hearing loss, since hearing aids are not a universal solution.\"},{\"question\":\"What are the main tasks in the competitions?\",\"answer\":\"The challenges focus on demixing and remixing pop/rock music for personalized instrument rebalancing, plus amplification to correct raised hearing thresholds.\"},{\"question\":\"How were systems evaluated?\",\"answer\":\"Systems were evaluated using the objective metric HAAQI (HearingAid Audio Quality Index).\"}]","The first Cadenza challenges - Using machine learning competitions to improve music for listeners with a hearing loss | PDF",1785947294,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"the-first-cadenza-challenges-using-machine-learning-competitions-to-improve-music-for-listeners-with-a-hearing-loss","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/the-first-cadenza-challenges-using-machine-learning-competitions-to-improve-music-for-listeners-with-a-hearing-loss/128398/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem do the “first Cadenza challenges” address?","Question",{"text":76,"@type":77},"They address how to use machine learning to improve music audio quality for listeners with hearing loss, since hearing aids are not a universal solution.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What are the main tasks in the competitions?",{"text":81,"@type":77},"The challenges focus on demixing and remixing pop/rock music for personalized instrument rebalancing, plus amplification to correct raised hearing thresholds.",{"name":83,"@type":74,"acceptedAnswer":84},"How were systems evaluated?",{"text":85,"@type":77},"Systems were evaluated using the objective metric HAAQI (HearingAid Audio Quality Index).","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":122},"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]