Music Genre Recognition Using Gabor Filters and LPQ Texture Descriptors View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2013

AUTHORS

Yandre Costa , Luiz Oliveira , Alessandro Koerich , Fabien Gouyon

ABSTRACT

This paper presents a novel approach for automatic music genre recognition in the visual domain that uses two texture descriptors. For this, the audio signal is converted into spectrograms and then textural features are extracted from this visual representation. Gabor filters and LPQ texture descriptors were used to capture the spectrogram content. In order to evaluate the performance of local feature extraction, some different zoning mechanisms were taken into account. The experiments were performed on the Latin Music Database. At the end, we have shown that the SVM classifier trained with LPQ is able to achieve a recognition rate above 80%. This rate is among the best results ever presented in the literature. More... »

PAGES

67-74

References to SciGraph publications

Book

TITLE

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

ISBN

978-3-642-41826-6
978-3-642-41827-3

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-41827-3_9

DOI

http://dx.doi.org/10.1007/978-3-642-41827-3_9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1017719759


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