Objective Evaluation of Tonal Fitness for Chord Progressions Using the Tonal Interval Space

Abstract

Chord progressions are core elements of Western tonal harmony regulated by multiple theoretical and perceptual principles. Ideally, objective measures to evaluate chord progressions should reflect their tonal fitness. In this work, we propose an objective measure of the fitness of a chord progression within the Western tonal context computed in the Tonal Interval Space, where distances capture tonal music principles. The measure considers four parameters, namely tonal pitch distance, consonance, hierarchical tension and voice leading between the chords in the progression. We performed a listening test to perceptually assess the proposed tonal fitness measure across different chord progressions, and compared the results with existing related models. The perceptual rating results show that our objective measure improves the estimation of a chord progression's tonal fitness in comparison with existing models.

A Computational Model of Tonal Tension Profile of Chord Progressions in the Tonal Interval Space

“…This article proposes a computational model of tonal tension profile of a chord progression that extends the previous work by Navarro-Cáceres et al [35]. This tension profile can be conceptualized as a continuous curve that captures changes in tonal tension over the temporal sequence of a chord progression.…”

“…Furthermore, Lerdahl emphasized the importance of the long-term hierarchical context representation of functional dependencies across all preceding or succeeding chords in a progression [ 22 ]. In an earlier publication [ 35 ], a tension model which notably includes a tree representation to capture long-term functional dependencies in chord progressions based on Rohrmeier’s [ 27 ] generative syntax model has been proposed [ 35 ].…”

“…However, the statistical nature of the models is more appropriate to capture linear changes that happen over time, whereas long-term dependencies such as phrase structures require modeling the intrinsic harmonic hierarchy. In a preliminary investigation, Navarro et al [ 35 ] presented a computational model of tonal tension associated with each chord in a progression. It adapts Lerdahl’s tonal tension model [ 1 ] into a fully automated framework.…”

“…This tension profile can be conceptualized as a continuous curve that captures changes in tonal tension over the temporal sequence of a chord progression. The tonal tension profile accounts for dependencies across immediately neighboring chords and the overall tonal hierarchies of the progression and is computed by connecting all the instantaneous tonal tension values given by the measure detailed in our previous work [ 35 ]. The original contributions of our work are four-fold.…”

Assistive Model to Generate Chord Progressions Using Genetic Programming with Artificial Immune Properties

“…That means we need to find the best values for weights k j in Equation ( 5) by applying cross-validation. A previous work, [43] has demonstrated the independence of the hierarchical properties of the chord progressions, independently of the key. Additionally, we used cross-validation to calculate the weights k i that best fit the scores resulting from a listening test with a total of 20 chord progressions.…”