國立虎尾科技大學 |

Sequential Decision-Making in Musical Intelligence

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Sequential Decision-Making in Musical Intelligence/ by Elad Liebman.
Author:	Liebman, Elad.
Description:	XXV, 206 p. 68 illus., 57 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computational intelligence. -
Online resource:	https://doi.org/10.1007/978-3-030-30519-2
ISBN:	9783030305192

Sequential Decision-Making in Musical Intelligence
Liebman, Elad.

Sequential Decision-Making in Musical Intelligence[electronic resource] /by Elad Liebman. - 1st ed. 2020. - XXV, 206 p. 68 illus., 57 illus. in color.online resource. - Studies in Computational Intelligence,8571860-949X ;. - Studies in Computational Intelligence,564.

Introduction -- Background -- Playlist Recommendation -- Algorithms for Tracking Changes In Preference Distributions -- Modeling the Impact of Music on Human Decision-Making -- Impact of Music on Person-Agent Interaction -- Multiagent Collaboration Learning: A Music Generation Test Case -- Related Work and a Taxonomy of Musical Intelligence Tasks -- Conclusion and Future Work.

Over the past 60 years, artificial intelligence has grown from an academic field of research to a ubiquitous array of tools used in everyday technology. Despite its many recent successes, certain meaningful facets of computational intelligence have yet to be thoroughly explored, such as a wide array of complex mental tasks that humans carry out easily, yet are difficult for computers to mimic. A prime example of a domain in which human intelligence thrives, but machine understanding is still fairly limited, is music. 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't been 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.

ISBN: 9783030305192

Standard No.: 10.1007/978-3-030-30519-2doiSubjects--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Sequential Decision-Making in Musical Intelligence
LDR:03648nam a22004095i 4500 001 1023776
003 DE-He213
005 20200704062331.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030305192 $9 978-3-030-30519-2
024 7 $a 10.1007/978-3-030-30519-2 $2 doi
035 $a 978-3-030-30519-2
050 4 $a Q342
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
100 1 $a Liebman, Elad. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1319790
245 1 0 $a Sequential Decision-Making in Musical Intelligence $h [electronic resource] / $c by Elad Liebman.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a XXV, 206 p. 68 illus., 57 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Studies in Computational Intelligence, $x 1860-949X ; $v 857
505 0 $a Introduction -- Background -- Playlist Recommendation -- Algorithms for Tracking Changes In Preference Distributions -- Modeling the Impact of Music on Human Decision-Making -- Impact of Music on Person-Agent Interaction -- Multiagent Collaboration Learning: A Music Generation Test Case -- Related Work and a Taxonomy of Musical Intelligence Tasks -- Conclusion and Future Work.
520 $a Over the past 60 years, artificial intelligence has grown from an academic field of research to a ubiquitous array of tools used in everyday technology. Despite its many recent successes, certain meaningful facets of computational intelligence have yet to be thoroughly explored, such as a wide array of complex mental tasks that humans carry out easily, yet are difficult for computers to mimic. A prime example of a domain in which human intelligence thrives, but machine understanding is still fairly limited, is music. 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't been 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.
650 0 $a Computational intelligence. $3 568984
650 0 $a Acoustical engineering. $3 563185
650 0 $a Music. $3 649088
650 0 $a Artificial intelligence. $3 559380
650 1 4 $a Computational Intelligence. $3 768837
650 2 4 $a Engineering Acoustics. $3 785331
650 2 4 $a Artificial Intelligence. $3 646849
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030305185
776 0 8 $i Printed edition: $z 9783030305208
776 0 8 $i Printed edition: $z 9783030305215
830 0 $a Studies in Computational Intelligence, $x 1860-949X ; $v 564 $3 1253640
856 4 0 $u https://doi.org/10.1007/978-3-030-30519-2
912 $a ZDB-2-INR
912 $a ZDB-2-SXIT
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)
950 $a Intelligent Technologies and Robotics (R0) (SpringerNature-43728)