Automatic Music Summarization via Similarity Analysis

Analysis:81-85 (2002)
  Copy   BIBTEX

Abstract

We present methods for automatically producing summary excerpts or thumbnails of music. To find the most representative excerpt, we maximize the average segment similarity to the entire work. After windowbased audio parameterization, a quantitative similarity measure is calculated between every pair of windows, and the results are embedded in a 2D similarity matrix. Summing the similarity matrix over the support of a segment results in a measure of how similar that segment is to the whole. This measure is maximized to find the segment that best represents the entire work. We discuss variations on the method, and present experimental results for orchestral music, popular songs, and jazz. These results demonstrate that the method finds significantly representative excerpts, using very few assumptions about the source audio

Other Versions

No versions found

Links

PhilArchive



    Upload a copy of this work     Papers currently archived: 101,369

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

Similar books and articles

言葉の意味の類似性判別に関するシソーラスと概念ベースの性能評価.石川 勉 川島 貴広 - 2005 - Transactions of the Japanese Society for Artificial Intelligence 20:326-336.

Analytics

Added to PP
2013-11-21

Downloads
21 (#1,012,268)

6 months
5 (#1,059,814)

Historical graph of downloads
How can I increase my downloads?

Author's Profile

Matthew Locke-Cooper
University of Sussex

Citations of this work

No citations found.

Add more citations

References found in this work

No references found.

Add more references