A Study on Influence of Normalization Methods on Music Genre Classification Results Employing kNN Algorithms

Aldona Rosner; Marcin Michalak; Bożena Kostek

doi:10.21936/si2013_v34.n2a.45

A Study on Influence of Normalization Methods on Music Genre Classification Results Employing kNN Algorithms

Abstract

This paper presents a comparison of different normalization methods applied to the set of feature vectors of music pieces. Test results show the inﬂuence of min-nlax and Zero-Mean normalization methods, employing different distance functions (Euclidean, Manhattan, Chebyshev, Minkowski) as a pre-processing for genre classiﬁcation, on k-Nearest Neighbor (kNN) algorithm classification results.

Citations

0

CrossRef
0

Web of Science
0

Scopus

Authors (3)

Aldona Rosner
- Instytut Informatyki, Pol. Śląska Wydział Automatyki, Elektroniki i Informatyki
Marcin Michalak
- Politechnika Śląska Instytut Informatyki
Bożena Kostek prof. dr hab. inż.

Cite as

Full text

full text is not available in portal

Keywords

Details

Category:: Articles
Type:: artykuły w czasopismach recenzowanych i innych wydawnictwach ciągłych
Published in:: Studia Informatica Pomerania no. 34, pages 411 - 423,
ISSN: 2451-0424
Language:: English
Publication year:: 2013
Bibliographic description:: Rosner A., Michalak M., Kostek B.: A Study on Influence of Normalization Methods on Music Genre Classification Results Employing kNN Algorithms// Studia Informatica. -Vol. 34., nr. 2A (111) (2013), s.411-423
DOI:: Digital Object Identifier (open in new tab) 10.21936/si2013_v34.n2a.45
Verified by:: Gdańsk University of Technology