Probabilistic Modular Bass Voice Leading in Melodic Harmonisation

Probabilistic methodologies provide successful tools for automated music composition, such as melodic harmonisation, since they capture statistical rules of the music idioms they are trained with. Proposed methodologies focus either on specific aspects of harmony (e.g., generating abstract chord symbols) or incorporate the determination of many harmonic characteristics in a single probabilistic generative scheme. This paper addresses the problem of assigning voice leading focussing on the bass voice, i.e., the realisation of the actual bass pitches of an abstract chord sequence, under the scope of a modular melodic harmonisation system where different aspects of the generative process are arranged by different modules. The proposed technique defines the motion of the bass voice according to several statistical aspects: melody voice contour, previous bass line motion, bass-to-melody distances and statistics regarding inversions and note doublings in chords. The aforementioned aspects of voicing are modular, i.e., each criterion is defined by independent statistical learning tools. Experimental results on diverse music idioms indicate that the proposed methodology captures efficiently the voice layout characteristics of each idiom, whilst additional analyses on separate statistically trained modules reveal distinctive aspects of each idiom. The proposed system is designed to be flexible and adaptable (for instance, for the generation of novel blended melodic harmonisations).