Statistical Models for Music Prediction and Generation

Dear Colleagues,

Music generation was one of the earliest applications of computing and machine learning, and much of the early work in music informatics was concerned with computational style emulation. Nearly 70 years ago, Shannon (“Prediction and Entropy of Printed English”, 1951) set out the topics of n-grams, long-range statistics, and entropy of a language, and this work led directly to pioneering research in music generation. In parallel music theoretical advances, Meyer (“Meaning in Music and Information Theory”, 1957) showed that aspects of information theory are highly relevant for music analysis. In recent decades, music prediction and generation by sampling from statistical models has been revisited (Conklin, “Music Generation from Statistical Models”, 2003), and continued advances in learning methods and algorithms have opened a new expanding era of music generation research. This Special Issue welcomes papers on the latest advances in music generation based on statistical modeling of music corpora.

Prof. Dr. Darrell Conklin
Guest Editor

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• Algorithms for sampling from predictive models of music
• Language models and deep learning
• Novel model architectures for music prediction
• Learning from small or heterogeneous corpora
• Template transformation methods
• Generation of pop tunes, polyphony, folk tunes, etc.
• Latent representations and embedding
• Multilayer textures: joint models of harmony, rhythm, and melody
• Information flow, cognitive expectation and surprise
• Semiotic structure and coherence: musical repetition, segmentation, and structuring