Andreu Vall, Hamid Eghbal-Zadeh, Matthias Dorfer, Markus Schedl,
"Timbral and Semantic Features for Music Playlists. Accepted presentation at the Machine Learning for Music Discovery."
: Workshop, within the International Conference on Machine Learning (ICML 2016), 6-2016
Original Titel:
Timbral and Semantic Features for Music Playlists. Accepted presentation at the Machine Learning for Music Discovery.
Sprache des Titels:
Englisch
Original Buchtitel:
Workshop, within the International Conference on Machine Learning (ICML 2016)
Original Kurzfassung:
Music recommendation methods based on song-
level features (either derived from audio con-
tent or metadata), suffer from not being able
to identify clear relations between music items
and listeners, whose perception of the quality
of a received recommendation is affected by a
wider range of factors. This problem is par-
ticularly severe for the task of generating mu-
sic playlists. The analysis of song character-
istics in hand-curated playlists exhibits large
within-playlist variability, indicating that gener-
ating music playlists or creating suitable continu-
ations may be an ill-defined problem. In this pa-
per we analyze two different features, based on
either timbral or semantic descriptors of songs,
for the task of predicting whether a song is suit-
able or not for a playlist. Our empirical results on
a dataset of hand-curated playlists indicate that
features extracted from semantic descriptors are
better suited for this task.