Gabriel P. Oliveira, Gabriel R. G. Barbosa, Bruna C. Melo, Mariana O. Silva, Danilo B. Seufitelli, Mirella M. Moro
{"title":"MUHSIC: An Open Dataset with Temporal Musical Success Information","authors":"Gabriel P. Oliveira, Gabriel R. G. Barbosa, Bruna C. Melo, Mariana O. Silva, Danilo B. Seufitelli, Mirella M. Moro","doi":"10.5753/dsw.2021.17415","DOIUrl":null,"url":null,"abstract":"Music is an alive industry with an increasing volume of complex data that creates new challenges and opportunities for extracting knowledge, benefiting not only the different music segments but also the Music Information Retrieval (MIR) community. In this paper, we present MUHSIC, a novel dataset with enhanced information on musical success. We focus on artists and genres by combining chart-related data with acoustic metadata to describe the temporal evolution of musical careers. The enriched and curated data allow building success-based time series to investigate high-impact periods (hot streaks) in such careers, transforming complex data into knowledge. Overall, MUHSIC is a relevant tool in music-related tasks due to its easy use and replicability.","PeriodicalId":314975,"journal":{"name":"Anais do III Dataset Showcase Workshop (DSW 2021)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Anais do III Dataset Showcase Workshop (DSW 2021)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5753/dsw.2021.17415","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Music is an alive industry with an increasing volume of complex data that creates new challenges and opportunities for extracting knowledge, benefiting not only the different music segments but also the Music Information Retrieval (MIR) community. In this paper, we present MUHSIC, a novel dataset with enhanced information on musical success. We focus on artists and genres by combining chart-related data with acoustic metadata to describe the temporal evolution of musical careers. The enriched and curated data allow building success-based time series to investigate high-impact periods (hot streaks) in such careers, transforming complex data into knowledge. Overall, MUHSIC is a relevant tool in music-related tasks due to its easy use and replicability.
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