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.
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;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on
Conference_Location :
Toulouse
Print_ISBN :
1-4244-0469-X
DOI :
10.1109/ICASSP.2006.1661396
