Melodic Similarity and Applications Using Biologically-Inspired Techniques

Dimitrios Bountouridis,Daniel Brown,Remco Veltkamp,Frans Wiering

doi:10.3390/app7121242

Dimitrios Bountouridis, Daniel Brown + Show 2 more

Open Access

https://doi.org/10.3390/app7121242

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Journal: Applied Sciences	Publication Date: Dec 1, 2017
Citations: 5	License type: CC BY 4.0

Affiliation: Utrecht University, University of Waterloo

Abstract

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.

Highlights

IntroductionIn 2016, digital music revenues overtook physical revenues for the first time (www.ifpi.org/downloads/GMR2016.pdf), a testament to the music industry’s adaptability to the digital age
In 2016, digital music revenues overtook physical revenues for the first time, a testament to the music industry’s adaptability to the digital age
Given two sequences to be aligned, we argue that two settings and are equivalent and comparable only when they are of same flexibility, meaning they result to alignments of equal length relative to the original sequences

Summary

Introduction

In 2016, digital music revenues overtook physical revenues for the first time (www.ifpi.org/downloads/GMR2016.pdf), a testament to the music industry’s adaptability to the digital age. The proliferation of digital music services has raised the listeners’ interest in the accompaniment chords (www.chordify.com), the lyrics (www.musixmatch.com), the original versions of a cover, the sample (loop) (www.whosampled.com) that a song uses and many more scenarios that service providers cannot deal with manually. This development brings Music Information Retrieval (MIR) to the centre of attention. The field includes research about accurate and efficient computational methods, applied to various music retrieval and classification tasks such as melody retrieval, cover song detection, automatic chord extraction and music recommendation.

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