Audio content analysis for understanding structures of scene in video

Authors:
Chan-Mi Kang;Joong-Hwan Baek
Affiliations:
Multimedia Retrieval Lab. in School of Electronics and Communication Engineering, Hankuk Aviation University;School of Electronics and Communication Engineering, Hankuk Aviation University
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Year:
2006

Citing 4
Cited 0

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Scene boundary detection by audiovisual contents analysis

AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
A speech/music discriminator based on RMS and zero-crossings

IEEE Transactions on Multimedia

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Abstract

In this paper, we propose a system to categorize audio in 7 classes. For classification features, we use the mean and variance of RMS, ZCR, fundamental frequency and frequency peak which are extracted from every frame of 25ms length. In addition to the audio content classification, we also perform speaker identification with the voice sequences extracted automatically using our proposed method. The accuracy of our proposed scheme reaches 93.8% in categorizing audio signal and 80% in the speaker identification process.