Content Aware Music Analysis with Multi-Dimensional Similarity Measure
Wohlfahrt-Laymann, J., & Heimbürger, A. (2017). Content Aware Music Analysis with Multi-Dimensional Similarity Measure. In H. Jaakkola, B. Thalheim, Y. Kiyoki, & N. Yoshida (Eds.), Information Modelling and Knowledge Bases XXVIII (pp. 303-313). Frontiers in Artificial Intelligence and Applications, 292. IOS Press. doi:10.3233/978-1-61499-720-7-303
Julkaistu sarjassa
Frontiers in Artificial Intelligence and Applications;292Päivämäärä
2017Oppiaine
TietotekniikkaTekijänoikeudet
© IOS Press, 2017. This is a final draft version of an article whose final and definitive form has been published by IOS Press. Published in this repository with the kind permission of the publisher.
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.