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Computational approaches to music composition and style imitation
have engaged musicians, music scholars, and computer scientists
since the early days of computing. Music generation research has
generally employed one of two strategies: knowledge-based methods
that model style through explicitly formalized rules, and data
mining methods that apply machine learning to induce statistical
models of musical style. The five chapters in this book illustrate
the range of tasks and design choices in current music generation
research applying machine learning techniques and highlighting
recurring research issues such as training data, music
representation, candidate generation, and evaluation. The
contributions focus on different aspects of modeling and generating
music, including melody, chord sequences, ornamentation, and
dynamics. Models are induced from audio data or symbolic data. This
book was originally published as a special issue of the Journal of
Mathematics and Music.
Computational approaches to music composition and style imitation
have engaged musicians, music scholars, and computer scientists
since the early days of computing. Music generation research has
generally employed one of two strategies: knowledge-based methods
that model style through explicitly formalized rules, and data
mining methods that apply machine learning to induce statistical
models of musical style. The five chapters in this book illustrate
the range of tasks and design choices in current music generation
research applying machine learning techniques and highlighting
recurring research issues such as training data, music
representation, candidate generation, and evaluation. The
contributions focus on different aspects of modeling and generating
music, including melody, chord sequences, ornamentation, and
dynamics. Models are induced from audio data or symbolic data. This
book was originally published as a special issue of the Journal of
Mathematics and Music.
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