Overview
- Focuses on two aspects of musical intelligence: music recommendation and human-agent interaction in the context of music
- Covers topics such as the design of better music playlist recommendation algorithms and algorithms for tracking user preferences over time; new approaches for modeling people's behavior in situations that involve music; and the design of agents capable of meaningful interaction with humans and other agents in settings where music plays a roll
- Addresses the question: “Can a sequential decision-making perspective guide us in the creation of better music agents, and social agents in general? And if so, how?
Part of the book series: Studies in Computational Intelligence (SCI, volume 857)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (9 chapters)
Keywords
About this book
Over recent decades, many researchers have used computational tools to perform tasks like genre identification, music summarization, music database querying, and melodic segmentation. While these are all useful algorithmic solutions, we are still a long way from constructing complete music agents able to mimic (at least partially) the complexity with which humans approach music.
One key aspect that hasn'tbeen sufficiently studied is that of sequential decision-making in musical intelligence. Addressing this gap, the book focuses on two aspects of musical intelligence: music recommendation and multi-agent interaction in the context of music. Though motivated primarily by music-related tasks, and focusing largely on people's musical preferences, the work presented in this book also establishes that insights from music-specific case studies can also be applicable in other concrete social domains, such as content recommendation.
Showing the generality of insights from musical data in other contexts provides evidence for the utility of music domains as testbeds for the development of general artificial intelligence techniques.
Ultimately, this thesis demonstrates the overall value of taking a sequential decision-making approach in settings previously unexplored from this perspective.
Authors and Affiliations
Bibliographic Information
Book Title: Sequential Decision-Making in Musical Intelligence
Authors: Elad Liebman
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-030-30519-2
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-30518-5Published: 15 October 2019
Softcover ISBN: 978-3-030-30521-5Published: 15 October 2020
eBook ISBN: 978-3-030-30519-2Published: 01 October 2019
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XXV, 206
Number of Illustrations: 11 b/w illustrations, 57 illustrations in colour
Topics: Computational Intelligence, Engineering Acoustics, Music, Artificial Intelligence