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
© 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.
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Wohlfahrt-Laymann, Jan; Heimbürger, Anneli (Tampere University of Technology, 2016)
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