Musical track popularity mining dataset: Extension & experimentation

• Published in: Neurocomputing (Amsterdam) 2018-03, Vol.280, p.76-85
• Main Authors: Karydis, Ioannis, Gkiokas, Aggelos, Katsouros, Vassilis, Iliadis, Lazaros
• Format: Article
• Language: English
• Subjects: Dataset; Hit song science; Music Information Research; Popularity trend features; Popularity trend similarity; Track popularity
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2017.09.100

Music Information Research (MIR) requires access to real musical content in order to test the efficiency and effectiveness of its methods as well as to compare developed methodologies on common data. Existing datasets do not address the research direction of musical track popularity that has recently received considerate attention. Moreover, sources of musical popularity do not provide easily manageable data and no standardised dataset exists for musical popularity research. To address these issues the Track Popularity Dataset (TPD) was created in a previous work. TPD provided (a) different sources of popularity definition ranging from 2004 to 2014, (b) mapping between different track/ author/ album identification spaces allowing use of different popularity sources, (c) information on the remaining, non popular, tracks of an album with a popular track, (d) contextual similarity between tracks and (e) ready for MIR use extracted features for both popular and non-popular audio tracks. This paper extends the TPD by (a) adding more readily computed features, (b) proposing feature & similarity definitions on popularity trends, (c) formulating common data mining scenarios on tracks’ popularity and (d) presenting respective promising results.