Recognition of musical instruments

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Recognition of musical instruments

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Title: Recognition of musical instruments
Author: Harya, Wicaksana; Septian, Hartono; Foo, Say Wei
Copyright year: 2006
Abstract: In this paper an automated method to recognize the musical instruments playing the musical signals is presented. Various features of the musical instruments and musical signals are investigated. The features can broadly be grouped into three categories: temporal, spectral, and cepstral features. A composite neural network structure is proposed as the classifier. The performance of the composite neural network using a set of carefully chosen features is compared with that of the traditional neural network. Experimental results show that the accuracy achieved using composite structure (94%) is significantly higher than that using the traditional structure (88%) when more than four musical instruments are to be distinguished.
Type: Conference Paper
Conference name: IEEE Asia Pacific Conference on Circuits and Systems (2006 : Singapore)
School: School of Electrical and Electronic Engineering
Rights: ©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. http://www.ieee.org/portal/site.
Version: Published version

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