Style Imitation and Chord Invention in Polyphonic Music with Exponential Families

By Gaëtan Hadjeres, Jason Sakellariou, François Pachet. 2016

18 citations recorded by the source corpus.

Modeling polyphonic music is a particularly challenging task because of the intricate interplay between melody and harmony. A good model should satisfy three requirements: statistical accuracy (capturing faithfully the statistics of correlations at various ranges, horizontally and vertically), flexibility (coping with arbitrary user constraints), and generalization capacity (inventing new material, while staying in the style of the training corpus). Models proposed so far fail on at least one of these requirements. We propose a statistical model of polyphonic music, based on the maximum entropy principle. This model is able to learn and reproduce pairwise statistics between neighboring note events in a given corpus. The model is also able to invent new chords and to harmonize unknown melodies. We evaluate the invention capacity of the model by assessing the amount of cited, re-discovered, and invented chords on a corpus of Bach chorales. We discuss how the model enables the user to specify and enforce user-defined constraints, which makes it useful for style-based, interactive music generation.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Generative Models, Signal Processing

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Citations in the corpus

Music Transformer: Generating Music with Long-Term Structure (2018, methodology): “B ACH C HORALES J.S. Bach chorales is a canonical dataset used for evaluating generative models for music 3 (e.g., Allan & Williams, 2005; Boulanger-Lewandowski et al., 2012; Liang, 2016; Hadjeres et al., 2016; Huang et al., 2017).”
DeepBach: a Steerable Model for Bach Chorales Generation (2016, methodology): “A method that addresses the rigidity of sequential generation in music was ﬁrst proposed in (Sakellariou et al., 2015; Sakellariou et al., 2016) for monophonic music and later generalized to polyphony in (Hadjeres et al., 2016).”
An Improved Relative Self-Attention Mechanism for Transformer with Application to Music Generation (2018, background): “Bach Chorales is a canonical dataset used for evaluating generative models for music [1, 3, 7, 11, 17].”
Counterpoint by Convolution (2019, methodology): “While the literature features many variants of this dataset [1, 4, 14, 27], we report results on that used by [4].”