This study focuses on the application of different computational methods to carry out a ”modal harmonic 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 for improvisation. This work proposes different computational approaches for the recognition of the chordscale type in an improvised phrase given the harmonic context. We have curated a dataset to evaluate different chordscale recognition approaches proposed in this study, where the dataset consists of around 40 minutes of improvised monophonic Jazz solo performances. The dataset is made publicly available and shared on freesound.org. To achieve the task of chord-scale type recognition, we propose one rule-based, one probabilistic and one supervised learning method. All proposed methods use Harmonic Pitch Class Profile (HPCP) features for classification. We observed an increase in the classification score when learned chordscale models are filtered with predefined scale templates indicating that incorporating prior domain knowledge to learned models is beneficial. This study has its novelty in presenting a first computational analysis on chord-scales in the context of Jazz improvisation.

This work is supported by ERC funded TECSOME project. The author E.D received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowsa-Curie grant agreement No. 765068.

Comunicació presentada a: 16th Sound & Music Computing Conference celebrada del 23 al 31 de maig de 2019 a Málaga, Espanya.