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Convolution-based classification of audio and symbolic representations of music

Velarde, G., Cancino Chacon, C., Meredith, D. , Weyde, T. ORCID: 0000-0001-8028-9905 & Grachten, M. (2018). Convolution-based classification of audio and symbolic representations of music. Journal of New Music Research, 47(3), pp. 191-205. doi: 10.1080/09298215.2018.1458885


We present a novel convolution-based method for classification of audio and symbolic representations of music, which we apply to classification of music by style. Pieces of music are first sampled to pitch–time representations (spectrograms or piano-rolls) and then convolved with a Gaussian filter, before being classified by a support vector machine or by k-nearest neighbours in an ensemble of classifiers. On the well-studied task of discriminating between string quartet movements by Haydn and Mozart, we obtain accuracies that equal the state of the art on two data-sets. However, in multi-class composer identification, methods specialised for classifying symbolic representations of music are more effective. We also performed experiments on symbolic representations, synthetic audio and two different recordings of The Well-Tempered Clavier by J. S. Bach to study the method’s capacity to distinguish preludes from fugues. Our experimental results show that our approach performs similarly on symbolic representations, synthetic audio and audio recordings, setting our method apart from most previous studies that have been designed for use with either audio or symbolic data, but not both.

Publication Type: Article
Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis in 'Journal of New Music research' on 06 may 2018, available online:
Publisher Keywords: Classification algorithms, composer classification, genre classification, convolution, filtering, audio music classification, symbolic music classification
Departments: School of Science & Technology > Computer Science
SWORD Depositor:
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