Title :
Semantic segmentation and summarization of music: methods based on tonality and recurrent structure
Author_Institution :
GE Global Res. Center, NY
fDate :
3/1/2006 12:00:00 AM
Abstract :
This paper describes a study on automatic music segmentation and summarization from audio signals. The paper inquires scientifically into the nature of human perception of music and offers a practical solution to difficult problems of machine intelligence for automated multimedia content analysis and information retrieval. Specifically, three problems are addressed: segmentation based on tonality analysis, segmentation based on recurrent structural analysis, and summarization. Experimental results are evaluated quantitatively, demonstrating the promise of the proposed methods
Keywords :
audio signal processing; computational linguistics; multimedia computing; music; audio signals; automated multimedia content analysis; automatic music segmentation; automatic music summarization; chromagram; dynamic time warping; human perception; information retrieval; machine intelligence; music matching; recurrent structural analysis; repetition detection; section beginning strategy; section transition strategy; self similarity analysis; thumbnailing; tonality analysis; Buildings; Computational modeling; Content based retrieval; Frequency; Hidden Markov models; Humans; Information analysis; Machine intelligence; Multiple signal classification; Music information retrieval;
Journal_Title :
Signal Processing Magazine, IEEE
DOI :
10.1109/MSP.2006.1598088