Processing of musical data employing rough sets and artificial neural networks

Authors:
Bożena Kostek;Piotr Szczuko;Pawel Żwan;Piotr Dalka
Affiliations:
Multimedia Systems Department, Gdańsk University of Technology, Gdańsk, Poland;Multimedia Systems Department, Gdańsk University of Technology, Gdańsk, Poland;Multimedia Systems Department, Gdańsk University of Technology, Gdańsk, Poland;Multimedia Systems Department, Gdańsk University of Technology, Gdańsk, Poland
Venue:
Transactions on Rough Sets III
Year:
2005

Citing 7
Cited 2

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Rough-Neuro-Computing: Techniques for Computing with Words

Rough-Neuro-Computing: Techniques for Computing with Words
RSES and RSESlib - A Collection of Tools for Rough Set Computations

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A New Version of Rough Set Exploration System

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Automatic recognition of isolated monophonic musical instrument sounds using kNNC

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Automatic Singing Voice Recognition Employing Neural Networks and Rough Sets

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Abstract

This article presents experiments aiming at testing the effectiveness of the implemented low-level descriptors for automatic recognition of musical instruments and musical styles. The paper discusses first some problems in audio information analysis related to MPEG-7-based applications. A short overview of the MPEG-7 standard focused on audio information description is also given. System assumptions for automatic identification of music and musical instrument sounds are presented. A discussion on the influence of descriptor selection process on the classification accuracy is included. Experiments are carried out basing on a decision system employing Rough Sets (RS) and Artificial Neural Networks (ANNs).