DocumentCode
2332941
Title
A Markov-Chain Monte-Carlo Approach to Musical Audio Segmentation
Author
Rhodes, Christophe ; Casey, Michael ; Abdallah, Samer ; Sandler, Mark
Author_Institution
Goldsmiths Coll., London Univ.
Volume
5
fYear
2006
fDate
14-19 May 2006
Abstract
This paper describes a method for automatically segmenting and labelling sections in recordings of musical audio. We incorporate the user´s expectations for segment duration as an explicit prior probability distribution in a Bayesian framework, and demonstrate experimentally that this method can produce accurate labelled segmentations for popular music
Keywords
Bayes methods; Markov processes; Monte Carlo methods; audio signal processing; music; statistical distributions; Bayesian framework; Markov-chain Monte-Carlo approach; musical audio segmentation; prior probability distribution; Audio recording; Bayesian methods; Data mining; Educational institutions; Hidden Markov models; Histograms; Labeling; Music; Noise reduction; Probability distribution;
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on
Conference_Location
Toulouse
ISSN
1520-6149
Print_ISBN
1-4244-0469-X
Type
conf
DOI
10.1109/ICASSP.2006.1661396
Filename
1661396
Link To Document