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The mood of Chinese Pop music: Representation and recognition
Author(s) -
Hu Xiao,
Yang YiHsuan
Publication year - 2017
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.23813
Subject(s) - categorical variable , mood , set (abstract data type) , music information retrieval , musical , representation (politics) , computer science , popular music , focus (optics) , natural language processing , psychology , speech recognition , social psychology , machine learning , visual arts , art , physics , optics , politics , political science , law , programming language
Music mood recognition (MMR) has attracted much attention in music information retrieval research, yet there are few MMR studies that focus on non‐Western music. In addition, little has been done on connecting the 2 most adopted music mood representation models: categorical and dimensional. To bridge these gaps, we constructed a new data set consisting of 818 Chinese Pop (C‐Pop) songs, 3 complete sets of mood annotations in both representations, as well as audio features corresponding to 5 distinct categories of musical characteristics. The mood space of C‐Pop songs was analyzed and compared to that of Western Pop songs. We also explored the relationship between categorical and dimensional annotations and the results revealed that one set of annotations could be reliably predicted by the other. Classification and regression experiments were conducted on the data set, providing benchmarks for future research on MMR of non‐Western music. Based on these analyses, we reflect and discuss the implications of the findings to MMR research.