A non-homogeneous beat-based harmony Markov model

作者：Roig Caries; Tardon Lorenzo J^*; Barbancho Isabel; Barbancho Ana M

来源：Knowledge-Based Systems, 2018, 142: 85-94.

DOI：10.1016/j.knosys.2017.11.027

• Automatic music composition
• Probabilistic modelling
• Machine learning
• Markov model

In this paper, a novel probabilistic model of harmonic progressions and a generation scheme based on such model are presented. On the basis of the large amount of publications that show the stochastic nature of the music and the possibility of modelling it by means of statistical processes, this paper shows how to create a non-homogeneous Markov chain to automatically generate harmonic progressions by building a temporal reference of the internal beat structure of music to guide the progressions. Thus, this new model develops on the classic transition matrix to include a beat-dependent/temporal layer to model the residency time.
The method for the automatic creation of harmonic progressions based on the model developed is presented after the model. The harmonic progressions generated by our scheme are coherent with the style of the training data employed and, thanks to the specific temporal layer designed, the musical mid-term and long-term dependencies that lead to a natural and logic cadence are taken into account. The model developed is usable for the automatic generation of harmonic patterns that can be used to enlarge the flexibility and creativity of pattern-based computational music composers.

• 出版日期2018-2-15