[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117811-en":3,"doc-seo-117811-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},117811,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Accurately predicting hit songs using neurophysiology and machine learning","Identifying hit songs remains a difficult “Hit Song Science” problem due to low predictive accuracy and the challenge of curating music from vast incoming catalogs. The study measures neurophysiologic responses to streaming-service–selected songs labeled as hits or flops, then compares statistical methods. A linear model using two neural measures reaches 69% accuracy, while ensemble machine learning on synthetic data improves classification to 97%. Using only the first minute of neural response still classifies hits at 82%, indicating rapid brain identification and higher reliability for market outcome prediction.","TYPE Original Research PUBLISHED 20 June 2023  \nDOI 10. 3389/frai.2023.1154663  \nOPEN ACCESS  \nEDITED BY  \nRicky J. Sethi,  \nFitchburg State University, United States  \nREVIEWED BY  \nCharles Courchaine,  \nNational University, United States  \nF. Amilcar Cardoso,  \nUniversity of Coimbra, Portugal  \n*CORRESPONDENCE  \nPaul J. Zak  \n [paul@neuroeconomicstudies.org](paul@neuroeconomicstudies.org)  \nRECEIVED 03 February 2023  \nACCEPTED 09 May 2023  \nPUBLISHED 20 June 2023  \nCITATION  \nMerritt SH, Ga􀀀uri K and Zak PJ (2023)  \nAccurately predicting hit songs using neurophysiology and machine learning.  \nFront. Artif. Intell. 6:1154663 .  \ndoi: 10.3389/frai.2023.1154663  \nCOPYRIGHT  \n© 2023 Merritt, Ga􀀀uri and Zak. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nAccurately predicting hit songs using neurophysiology and machine learning  \nSean H. Merritt1 , Kevin Ga􀀀uri1 and Paul J. Zak1,2*  \n1 Center for Neuroeconomics Studies, Claremont Graduate University, Claremont, CA, United States,  \n2 Immersion Neuroscience, Henderson, NV, United States  \nIdentifying hit songs is notoriously di􀀈cult. Traditionally, song elements have been measured from large databases to identify the lyrical aspects of hits. We took adi􀀀erent methodological approach, measuring neurophysiologic responses to a set of songs provided by a streaming music service that identified hits and flops. We compared several statistical approaches to examine the predictive accuracy of each technique. A linear statistical model using two neural measures identified hits with 69% accuracy. Then, we created a synthetic set data and applied ensemble machine learning to capture inherent non-linearities in neural data. This model classified hit songs with 97% accuracy. Applying machine learning to the neural response to 1st min of songs accurately classified hits 82% of the time showing that the brain rapidly identifies hit music. Our results demonstrate that applying machine learning to neural data can substantially increase classification accuracy for di􀀈cult to predict market outcomes.  \nKEYWORDS  \nprediction, immersion, music, neurophysiology, classification  \nIntroduction  \nEvery day, 24,000 new songs are released worldwide (Pandora, 2018) . That’s 168,000 new songs every week. People are drowning in choices. The surfeit of choices makes it di􀀔cult for streaming services and radio stations to identify songs to add to playlists. Music distribution channels use both human listeners and arti􀀂cial intelligence models to identify new music that is likely to become a hit. Unfortunately, the accuracy of predictions has generally been low (Prey, 2018) . This has been called the “Hit Song Science” problem (McFee et al., 2012) . The inability to predict hits means that artists are often underpaid for their work and music labels misallocate production and marketing resources when seeking to build audiences for new music (Byun, 2016) . The inability to curate desirable music also causes audiences move between platforms searching for music they enjoy (Prey, 2018) .  \nPeople want new music, but generally prefer songs similar to those they already know (Ward et al., 2014; Askin and Mauskapf, 2017) . Music streaming services have invested in technologies to identify and introduce new music customized to subscribers’ existing playlists. Spotify does this with “Discover Weekly,” a playlist of 30 new songs subscribers receive every Monday morning. Pandora classi􀀂es new music using 450 attributes in its Music Genome Project and introduces new music using a service called “Personalized Soundtracks” (Carbone, 2021","cbCaiklRjU6tynrb","https://ap.wps.com/l/cbCaiklRjU6tynrb","pdf",563852,1,10,"English","en",105,"# Introduction\n# Background","[{\"question\":\"How does the study identify hit songs?\",\"answer\":\"It measures neurophysiologic responses to a set of songs provided by a streaming music service that labels tracks as hits and flops, then applies predictive models to classify them.\"},{\"question\":\"What prediction accuracy do the models achieve?\",\"answer\":\"A linear statistical model using two neural measures identifies hits with 69% accuracy, while ensemble machine learning on synthetic data reaches 97%. Using only the first minute of neural response yields 82% accuracy.\"},{\"question\":\"Why is using neural data important for predicting market outcomes?\",\"answer\":\"The results show that machine learning applied to neural data can substantially improve classification accuracy for hard-to-predict entertainment popularity, implying that the brain rapidly encodes information relevant to hit identification.\"}]","Accurately predicting hit songs using neurophysiology and machine learning | PDF",1785679691,25,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"accurately-predicting-hit-songs-using-neurophysiology-and-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@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/accurately-predicting-hit-songs-using-neurophysiology-and-machine-learning/117811/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the study identify hit songs?","Question",{"text":75,"@type":76},"It measures neurophysiologic responses to a set of songs provided by a streaming music service that labels tracks as hits and flops, then applies predictive models to classify them.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What prediction accuracy do the models achieve?",{"text":80,"@type":76},"A linear statistical model using two neural measures identifies hits with 69% accuracy, while ensemble machine learning on synthetic data reaches 97%. Using only the first minute of neural response yields 82% accuracy.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is using neural data important for predicting market outcomes?",{"text":84,"@type":76},"The results show that machine learning applied to neural data can substantially improve classification accuracy for hard-to-predict entertainment popularity, implying that the brain rapidly encodes information relevant to hit identification.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"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":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]