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). IOS Press. Frontiers in Artificial Intelligence and Applications, 292. https://doi.org/10.3233/978-1-61499-720-7-303
Date
2017Copyright
© 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.
Publisher
IOS PressParent publication ISBN
978-1-61499-719-1Is part of publication
Information Modelling and Knowledge Bases XXVIIIISSN Search the Publication Forum
0922-6389Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/26419866