| 书目名称 | Sequential Decision-Making in Musical Intelligence |
| 编辑 | Elad Liebman |
| 视频video | |
| 概述 | 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 algori |
| 丛书名称 | Studies in Computational Intelligence |
| 图书封面 |  |
| 描述 | 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‘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 primari |
| 出版日期 | Book 2020 |
| 关键词 | Computational Intelligence; Musical Intelligence; Music Recommendation; Playlist Recommendation Algorit |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-30519-2 |
| isbn_softcover | 978-3-030-30521-5 |
| isbn_ebook | 978-3-030-30519-2Series ISSN 1860-949X Series E-ISSN 1860-9503 |
| issn_series | 1860-949X |
| copyright | Springer Nature Switzerland AG 2020 |
发表于 2025-3-21 19:13:05
