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Melodic Similarity and Applications Using Biologically-Inspired Techniques

Department of Information and Computing Sciences, Utrecht University, 3584 CC Utrecht, The Netherlands
David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Author to whom correspondence should be addressed.
Academic Editor: Meinard Müller
Appl. Sci. 2017, 7(12), 1242;
Received: 30 September 2017 / Revised: 23 November 2017 / Accepted: 27 November 2017 / Published: 1 December 2017
(This article belongs to the Special Issue Sound and Music Computing)
Music similarity is a complex concept that manifests itself in areas such as Music Information Retrieval (MIR), musicological analysis and music cognition. Modelling the similarity of two music items is key for a number of music-related applications, such as cover song detection and query-by-humming. Typically, similarity models are based on intuition, heuristics or small-scale cognitive experiments; thus, applicability to broader contexts cannot be guaranteed. We argue that data-driven tools and analysis methods, applied to songs known to be related, can potentially provide us with information regarding the fine-grained nature of music similarity. Interestingly, music and biological sequences share a number of parallel concepts; from the natural sequence-representation, to their mechanisms of generating variations, i.e., oral transmission and evolution respectively. As such, there is a great potential for applying scientific methods and tools from bioinformatics to music. Stripped-down from biological heuristics, certain bioinformatics approaches can be generalized to any type of sequence. Consequently, reliable and unbiased data-driven solutions to problems such as biological sequence similarity and conservation analysis can be applied to music similarity and stability analysis. Our paper relies on such an approach to tackle a number of tasks and more notably to model global melodic similarity. View Full-Text
Keywords: melodic similarity; alignment; stability; variation; bioinformatics melodic similarity; alignment; stability; variation; bioinformatics
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MDPI and ACS Style

Bountouridis, D.; Brown, D.G.; Wiering, F.; Veltkamp, R.C. Melodic Similarity and Applications Using Biologically-Inspired Techniques. Appl. Sci. 2017, 7, 1242.

AMA Style

Bountouridis D, Brown DG, Wiering F, Veltkamp RC. Melodic Similarity and Applications Using Biologically-Inspired Techniques. Applied Sciences. 2017; 7(12):1242.

Chicago/Turabian Style

Bountouridis, Dimitrios, Daniel G. Brown, Frans Wiering, and Remco C. Veltkamp. 2017. "Melodic Similarity and Applications Using Biologically-Inspired Techniques" Applied Sciences 7, no. 12: 1242.

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