東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Sequential decision-making in musica...

Liebman, Elad.

FindBook

Google Book

Amazon

博客來

Sequential decision-making in musical intelligence

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Sequential decision-making in musical intelligence/ by Elad Liebman.
作者:	Liebman, Elad.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	xxv, 206 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Music. -
電子資源:	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. - Cham :Springer International Publishing :2020. - xxv, 206 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.8571860-949X ;. - Studies in computational intelligence ;v.857..

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:

516178
Music.

LC Class. No.: ML74

Dewey Class. No.: 780.28563

Sequential decision-making in musical intelligence
LDR:03251nmm a2200337 a 4500 001 2213947
003 DE-He213
005 20200224160042.0
006 m d
007 cr nn 008maaau
008 201118s2020 sz s 0 eng d
020 $a 9783030305192 $q (electronic bk.)
020 $a 9783030305185 $q (paper)
024 7 $a 10.1007/978-3-030-30519-2 $2 doi
035 $a 978-3-030-30519-2
040 $a GP $c GP
041 0 $a eng
050 4 $a ML74
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 780.28563 $2 23
090 $a ML74 $b .L716 2020
100 1 $a Liebman, Elad. $3 3443979
245 1 0 $a Sequential decision-making in musical intelligence $h [electronic resource] / $c by Elad Liebman.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xxv, 206 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v 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 2 4 $a Music. $3 516178
650 2 4 $a Artificial Intelligence. $3 769149
650 0 $a Artificial intelligence $x Musical applications. $3 628486
650 0 $a Computational intelligence. $3 595739
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Engineering Acoustics. $3 1533220
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Studies in computational intelligence ; $v v.857. $3 3443980
856 4 0 $u https://doi.org/10.1007/978-3-030-30519-2
950 $a Intelligent Technologies and Robotics (Springer-42732)