By Topic

On the Discrimination of Speech/Music Using a Time Series Regularity

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Swe, E.M.M. ; Univ. of Comput. Studies, Yangon ; Pwint, M. ; Sattar, F.

A new method to discriminate between speech and music related to the automatic transcription of broadcast news is presented. In the proposed method, a time series regularity, sample entropy (SampEn), is mainly used as an efficient feature to discriminate speech and music of broadcast audio stream. SampEn is a variant of the approximate entropy (ApEn) that measures the regularity of time series. Depending on the regularity of time series, a segment of a given audio stream is classified into speech or music. The first step of the method is calculation of SampEn sequence over windows. The second step is classification of this segment with a rule-based classification scheme over sample entropy sequence. Experimental results show the effectiveness of the proposed method for broadcast news shows with different music styles.

Published in:

Multimedia, 2008. ISM 2008. Tenth IEEE International Symposium on

Date of Conference:

15-17 Dec. 2008