Audio content description with wavelets and neural nets

Stephan Rein; Martin Reisslein; Thomas Sikora

Audio content description with wavelets and neural nets

Stephan Rein, Martin Reisslein, Thomas Sikora

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Precision audio content description is one of the key components of next generation internet multimedia search machines. We examine the usability of a combination of 39 different wavelets and three different types of neural nets for precision audio content, description. More specifically, we develop a novel wavelet dispersion measure that measures obtained ranks of wavelet coefficients. Our dispersion measure in conjunction with a probabilistic radial basis neural network trained by only three independent example sets obtains a success rate of approximately 78% in identifying unknown complex classical music movements.

Original language	English (US)
Title of host publication	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	4
State	Published - 2004
Event	Proceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing - Montreal, Que, Canada Duration: May 17 2004 → May 21 2004

Other

Other	Proceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing
Country/Territory	Canada
City	Montreal, Que
Period	5/17/04 → 5/21/04

ASJC Scopus subject areas

Electrical and Electronic Engineering
Signal Processing
Acoustics and Ultrasonics

Cite this

@inproceedings{fb2707168427409ebdea7be170a8ed59,

title = "Audio content description with wavelets and neural nets",

abstract = "Precision audio content description is one of the key components of next generation internet multimedia search machines. We examine the usability of a combination of 39 different wavelets and three different types of neural nets for precision audio content, description. More specifically, we develop a novel wavelet dispersion measure that measures obtained ranks of wavelet coefficients. Our dispersion measure in conjunction with a probabilistic radial basis neural network trained by only three independent example sets obtains a success rate of approximately 78% in identifying unknown complex classical music movements.",

author = "Stephan Rein and Martin Reisslein and Thomas Sikora",

year = "2004",

language = "English (US)",

volume = "4",

booktitle = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

note = "Proceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing ; Conference date: 17-05-2004 Through 21-05-2004",

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T1 - Audio content description with wavelets and neural nets

AU - Rein, Stephan

AU - Reisslein, Martin

AU - Sikora, Thomas

PY - 2004

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N2 - Precision audio content description is one of the key components of next generation internet multimedia search machines. We examine the usability of a combination of 39 different wavelets and three different types of neural nets for precision audio content, description. More specifically, we develop a novel wavelet dispersion measure that measures obtained ranks of wavelet coefficients. Our dispersion measure in conjunction with a probabilistic radial basis neural network trained by only three independent example sets obtains a success rate of approximately 78% in identifying unknown complex classical music movements.

AB - Precision audio content description is one of the key components of next generation internet multimedia search machines. We examine the usability of a combination of 39 different wavelets and three different types of neural nets for precision audio content, description. More specifically, we develop a novel wavelet dispersion measure that measures obtained ranks of wavelet coefficients. Our dispersion measure in conjunction with a probabilistic radial basis neural network trained by only three independent example sets obtains a success rate of approximately 78% in identifying unknown complex classical music movements.

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UR - http://www.scopus.com/inward/citedby.url?scp=4544347552&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:4544347552

VL - 4

BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

T2 - Proceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing

Y2 - 17 May 2004 through 21 May 2004

ER -

Audio content description with wavelets and neural nets

Abstract

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ASJC Scopus subject areas

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