Automatic Chord-Scale Type Detection Using Chroma Features

TitleAutomatic Chord-Scale Type Detection Using Chroma Features
Publication TypeMaster Thesis
Year of Publication2018
AuthorsDemirel, E.
AbstractThere has been great effort in designing data-driven applications which exploit computational methods to solve musically related problems in the research field of Music Information Retrieval. However, the current state of music modeling needs to be expanded in consideration with musical domain knowledge, as well as the human perception and cognition of music. In this thesis work, we study and evaluate different computational methods to carry out a "modal analysis" for Jazz improvisation performances by modeling the concept of "chord-scales". The Chord-Scale Theory is a theoretical concept that explains the relationship between the harmonic context of a musical piece and possible scale types to be used in improvisation. This work proposes different computational approaches to detect (or recognize) the chord-scale type present in the target Jazz solo, given the harmonic context. The experiments are conducted on two different datasets which are created within the course of this work. One of the datasets is made publicly available. To achieve the task of chord-scale type detection, we exploit a rule-based and a supervised learning method. The rule-based approach is developed in order to reveal possibilities for computational modeling of chord-scales. In the supervised learning algorithm, Support Vector Machines are chosen as classifiers. The classification of audio data is performed using chroma features. Furthermore, we conduct a case study on user (student) performance using "MusicCritic", which is a novel framework for automatic student performance assessment. This work has its value for conducting one of the first research on the numeric representations of chord-scales in improvised solo performances and pointing out several possible directions for exploring some of the core elements behind Jazz improvisation.
Keywordschord-scale detection, chroma features, Computational Musicology, Jazz improvisation, Machine learning, music information retrieval, supervised learning
Final publicationhttps://doi.org/10.5281/zenodo.1434122
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