A study on influence of normalization methods on music genre classification results employing kNN algorithm

Aldona Rosner, Marcin Michalak, Bożena Kostek

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-max 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.

Keywords


MIR; music information retrieval; music genre classification; normalization; pre-processing; kNN

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References


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DOI: http://dx.doi.org/10.21936/si2013_v34.n2A.45