Discovering the Neuroanatomical Correlates of Music with Machine Learning In Eduardo Reck Miranda (Eds.). Handbook of Artificial Intelligence for Music. Part 1. Springer

Abstract

Music is ubiquitous in our lives yet unique to humans. The interaction between music and the brain is complex, engaging a variety of neural circuits underlying sensory perception, learning and memory, action, social communication, and creative activities. Over the past decades, a growing body of literature has revealed the neural and computational underpinnings of music processing including not only sensory perception (e.g., pitch, rhythm, and timbre), but also1local/non-local structural processing (e.g., melody and harmony). These findings have also influenced Artificial Intelligence and Machine Learning systems, enabling computers to possess human-like learning and composing abilities. Despite the plenty of evidence, more study is required for complete account of music knowledge and creative mechanisms in human brain. This chapter reviews the neural correlates of unsupervised learning with regard to the computational and neuroanatomical architectures of music processing. Further, we offer a novel theoretical perspective on the brain’s unsupervised learning machinery that considers computational and neurobiological constraints, highlighting the connections between neuroscience and machine learning.

Artificial Intelligence Creates Art? An Experimental Investigation of Value and Creativity Perceptions

“…Although able to generate creative solutions, the mode of work of AI is intrinsically distinct from that of humans. Daikoku (2021) states that algorithms are not normally optimized for creativity but for efficiency, representing an important distinction between human creatives and AI. AI relies on data conformity, whereas human creativity often seeks to move away from general existing rules and thereby to novel solutions.…”