Content Aware Music Analysis with Multi-Dimensional Similarity Measure

Music players and cloud solution for music recommendation and automatic playlist creation are becoming increasingly more popular, as they intent to overcome the issue of the difficulty for users to find fitting music, based on context, mood and impression. Much research on the topic has been conducted, which has recommended different approaches to overcome this problem. This paper suggests a system which uses a multi-dimensional vector space, based on the music’s key elements, as well as the mood expressed through them and the song lyrics, which allows for difference and similarity finding to automatically generate a contextually meaningful playlist.

[2]  Kiyoaki Shirai,et al.  Machine Learning Approaches for Mood Classification of Songs toward Music Search Engine , 2009, 2009 International Conference on Knowledge and Systems Engineering.

[3]  Xing Chen,et al.  Contextual and Differential Computing for the Multi-Dimensional World Map with Context-Specific Spatial-Temporal and Semantic Axes , 2013, EJC.

[4]  Lillian Lee,et al.  Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..

[5]  J. Russell.  A circumplex model of affect. , 1980 .

[6]  R. Thayer.  The biopsychology of mood and arousal , 1989 .

[7]  Lie Lu,et al.  Automatic mood detection and tracking of music audio signals , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[8]  Xiao Hu,et al.  Music and Mood: Where Theory and Reality Meet , 2010 .

[9]  J. Stephen Downie,et al.  When Lyrics Outperform Audio for Music Mood Classification: A Feature Analysis , 2010, ISMIR.

[10]  Sibylle Moser.  Media modes of poetic reception: Reading lyrics versus listening to songs , 2007 .

[11]  Jun Hu,et al.  Music Playlist Recommendation Based on User Heartbeat and Music Preference , 2009, 2009 International Conference on Computer Technology and Development.

[12]  Bamshad Mobasher,et al.  The Role of Emotions in Context-aware Recommendation , 2013, Decisions@RecSys.

[13]  Lior Rokach,et al.  Best Usage Context Prediction for Music Tracks , 2010 .

[14]  Man-Kwan Shan,et al.  A personalized music filtering system based on melody style classification , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[15]  Dan Yang,et al.  Disambiguating Music Emotion Using Software Agents , 2004, ISMIR.

[16]  Setsuo Ohsuga,et al.  Information Modelling and Knowledge Bases , 1990 .

[17]  Xing Chen,et al.  A Semantic Associative Computation Method for Automatic Decorative-Multimedia Creation with 'Kansei' Information , 2009, APCCM.

[18]  K. Hevner.  Experimental studies of the elements of expression in music , 1936 .

[19]  Simon Frith,et al.  Performing Rites: On the Value of Popular Music , 1996 .

[20]  Björn W. Schuller,et al.  Determination of Nonprototypical Valence and Arousal in Popular Music: Features and Performances , 2010, EURASIP J. Audio Speech Music. Process..

[21]  Amy Beth Warriner,et al.  Norms of valence, arousal, and dominance for 13,915 English lemmas , 2013, Behavior Research Methods.

[22]  Janyce Wiebe,et al.  Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis , 2005, HLT.

[23]  Daniel J Levitin,et al.  The structure of musical preferences: a five-factor model. , 2011, Journal of personality and social psychology.

[24]  I. Peretz,et al.  Exposure effects on music preference and recognition , 1998, Memory & cognition.