15th workshop gRFIA
9th Music Encoding Conference (MEI 2021)
Alicante, July 19-23
13th international workshop on Machine Learning and Music
(online) September 18, 2020
- Ponce De León, P.J.; Iñesta, J.M.
"Feature-Driven Recognition of Music Styles"
Lecture Notes in Computer Science, vol. 2652, pp. 773-781
In this paper the capability of using self-organising neural maps (SOM)
as music style classifiers of musical fragments
is studied. From MIDI files, the monophonic melody track is extracted
and cut into fragments of equal length. From these sequences, melodic,
harmonic, and rhythmic numerical descriptors are computed and presented
to the SOM. Their performance is analysed in terms of separability in
different music classes from the activations of the map, obtaining different
degrees of success for classical and jazz music. This scheme has a number of
applications like indexing and selecting musical databases or the evaluation
of style-specific automatic composition systems.
author = "Ponce De León, P.J.; Iñesta, J.M.",
title = "Feature-Driven Recognition of Music Styles",
journal = "Lecture Notes in Computer Science",
pages = "773-781",
volume = "2652",
year = "2003"