A spiking neural network with an explicit mode and key theory subsystem learns pitch-class connection patterns resembling the Krumhansl-Schmuckler key profiles and generates four-part music conditioned on the requested mode and key.
A Survey on Artificial Intelligence for Music Generation: Agents, Domains and Perspectives
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abstract
Music is one of the Gardner's intelligences in his theory of multiple intelligences. How humans perceive and understand music is still being studied and is crucial to develop artificial intelligence models that imitate such processes. Music generation with Artificial Intelligence is an emerging field that is gaining much attention in the recent years. In this paper, we describe how humans compose music and how new AI systems could imitate such process by comparing past and recent advances in the field with music composition techniques. To understand how AI models and algorithms generate music and the potential applications that might appear in the future, we explore, analyze and describe the agents that take part of the music generation process: the datasets, models, interfaces, the users and the generated music. We mention possible applications that might benefit from this field and we also propose new trends and future research directions that could be explored in the future.
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Mode-conditioned music learning and composition: a spiking neural network inspired by neuroscience and psychology
A spiking neural network with an explicit mode and key theory subsystem learns pitch-class connection patterns resembling the Krumhansl-Schmuckler key profiles and generates four-part music conditioned on the requested mode and key.