Title :
Musical genres classification using Markov models
Author :
Iloga, Sylvain ; Romain, Olivier ; Bendaouia, Lotfi ; Tchuente, Maurice
Author_Institution :
Dept. of Comput. Sci., Univ. of Maroua, Maroua, Cameroon
Abstract :
Some audio files´ formats contain metadata such as musical genre. This facilitates the usability of musical devices. However there are still many audio files´ formats in which no metadata about the sound can be found. Our goal is to provide to users the same comfort in these conditions by computing the genres automatically. Various techniques have been proposed to determine a song´s genre among N known genres. In this paper, we propose a new way of using Markov models as classifiers to perform genres classification. Experiments on 10 genres including 4 cameroonian genres showed an accuracy of 69.4%.
Keywords :
Markov processes; classification; meta data; music; Markov models; audio files formats; meta data; musical genres classification; Accuracy; Computational modeling; Hidden Markov models; Markov processes; Music; Training; Vectors;
Conference_Titel :
Audio, Language and Image Processing (ICALIP), 2014 International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4799-3902-2
DOI :
10.1109/ICALIP.2014.7009885