Abstract
In Western tonal music, tension in chord progressions plays an important role in defining the path that a musical composition should follow. The creation of chord progressions that reflects such tension profiles can be challenging for novice composers, as it depends on many subjective factors, and also is regulated by multiple theoretical principles. This work presents ChordAIS-Gen, a tool to assist the users to generate chord progressions that comply with a concrete tension profile. We propose an objective measure capable of capturing the tension profile of a chord progression according to different tonal music parameters, namely, consonance, hierarchical tension, voice leading and perceptual distance. This measure is optimized into a Genetic Program algorithm mixed with an Artificial Immune System called Opt-aiNet. Opt-aiNet is capable of finding multiple optima in parallel, resulting in multiple candidate solutions for the next chord in a sequence. To validate the objective function, we performed a listening test to evaluate the perceptual quality of the candidate solutions proposed by our system. Most listeners rated the chord progressions proposed by ChordAIS-Gen as better candidates than the progressions discarded. Thus, we propose to use the objective values as a proxy for the perceptual evaluation of chord progressions and compare the performance of ChordAIS-Gen with chord progressions generators.
Highlights
In Western tonal music, tension is the anticipation a musical composition creates in a listener’s mind for relaxation or release
We aim to validate the objective function M we designed as a good measure that captures the tonal tension profile
Due to the specific features of the algorithm, it was programmed entirely without any external library designed for genetic programming
Summary
In Western tonal music, tension is the anticipation a musical composition creates in a listener’s mind for relaxation or release. In music composition, creating chord progressions which reflects a tonal tension profile commonly requires years of musical training To make this process easier, technological solutions have been proposed to automatically model and analyze chord progressions, following different paradigms including statistical learning [6,7,8,9], rules [10,11,12,13], grammars [14,15], and biological principles [16,17,18,19]. ChordAIS measures the consonance and relatedness to the previous chord, key and harmonic function to create a chord progression With this comparison we aim for the inclusion of new musical properties in the measure improves the results.
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