Abstract
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.
Acknowledgements
The authors would like to thank Peter van Kranenburg and Ruben Hillewaere for sharing with us the Haydn and Mozart string quartet data-sets; and the anonymous reviewers for their insightful comments.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
Gissel Velarde, Aalborg University, Department of Architecture, Design and Media Technology, Rendsburggade 14, Building: 6-314, 9000 Aalborg, Denmark. Email: [email protected].
1 Results published by MIREX 2016 (http://music-ir.org/mirex/wiki/2016:MIREX2016_Results).
2 For the horn sound we use the SYNTHTYPE function of the Matlab MIDI Toolbox (Eerola & Toiviainen, Citation2003). The horn sound was used as it was the best choice of the two available sounds in the toolbox that we used for rendering (the alternative was Shepard tones). For the string sound we used fluidsynth (www.fluidsynth.org) with FluidR3 GM sound font.
3 Toolbox accessed from http://www.cs.tut.fi/sgn/arg/CQT/ on 28 August 2015.
5 http://www.musedata.org/encodings/bach/bg/keybd/. Accessed on 23 February 2015.