Comparing Probabilistic Models for Melodic Sequences

Athina Spiliopoulou; Amos Storkey

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arXiv · 1109.6804

Comparing Probabilistic Models for Melodic Sequences

Athina Spiliopoulou·Amos Storkey

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Abstract

Modelling the real world complexity of music is a challenge for machine learning. We address the task of modeling melodic sequences from the same music genre. We perform a comparative analysis of two probabilistic models; a Dirichlet Variable Length Markov Model (Dirichlet-VMM) and a Time Convolutional Restricted Boltzmann Machine (TC-RBM). We show that the TC-RBM learns descriptive music features, such as underlying chords and typical melody transitions and dynamics. We assess the models for future prediction and compare their performance to a VMM, which is the current state of the art in melody generation. We show that both models perform significantly better than the VMM, with the Dirichlet-VMM marginally outperforming the TC-RBM. Finally, we evaluate the short order statistics of the models, using the Kullback-Leibler divergence between test sequences and model samples, and show that our proposed methods match the statistics of the music genre significantly better than the VMM.

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Athina Spiliopoulou, Amos Storkey. 2011-09-30. Comparing Probabilistic Models for Melodic Sequences. https://arxiv.org/abs/1109.6804

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