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|z (OCoLC)1142349165
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|b Springer Nature
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|b .W45 2020eb
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|a HCDD
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|a Weinberg, Gil,
|e author.
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|a Robotic musicianship :
|b embodied artificial creativity and mechatronic musical expression /
|c Gil Weinberg, Mason Bretan, Guy Hoffman, Scott Driscoll.
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|a Cham, Switzerland :
|b Springer,
|c [2020]
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| 300 |
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|a 1 online resource (xvii, 256 pages)
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a Automation, Collaboration, and E-Services ;
|v volume 8
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|a Intro -- Foreword -- Preface -- Contents -- 1 Introduction -- 1.1 Abstract -- 1.2 Why Robotic Musicianship -- 1.3 Sound Production and Design-Survey -- 1.3.1 Traditional Instruments -- 1.3.2 Augmented and Novel Instruments -- 1.4 Musical Intelligence -- 1.4.1 Sensing and Perception -- 1.4.2 Music Generation -- 1.5 Embodiment -- 1.6 Integrating Robotic Musicianship into New Interfaces -- 1.6.1 Musical Companion Robots -- 1.6.2 Wearable Robotic Musicians -- 1.7 Discussion -- References -- 2 Platforms-Georgia Tech's Robotic Musicians -- 2.1 Abstract -- 2.2 Haile-A Robotic Percussionist
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|a 2.2.1 Motivation -- 2.2.2 Design -- 2.3 Shimon-A Robotic Marimba Player -- 2.3.1 Striker Design -- 2.3.2 Mallet Motor Control -- 2.3.3 Slider Motor Control -- 2.3.4 Shimon's Socially Expressive Head -- 2.4 Shimi-A Music Driven Robotic Dancing Companion -- 2.4.1 Robotic Musical Companionship -- 2.4.2 Design -- 2.4.3 Software Architecture -- 2.4.4 Core Capabilities -- 2.5 The Robotic Drumming Prosthetic -- 2.5.1 Motivation -- 2.5.2 Related Work -- 2.5.3 Platform -- 2.5.4 Generative Physical Model for Stroke Generation -- 2.5.5 Conclusions -- References
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|a 3 ``Listen Like A Human''-Human-Informed Music Perception Models -- 3.1 Abstract -- 3.2 Rhythmic Analysis of Live Drumming -- 3.2.1 Onset Detection -- 3.2.2 Beat Detection -- 3.2.3 Rhythmic Stability and Similarity -- 3.2.4 User Study -- 3.3 Tonal Music Analysis Using Symbolic Rules -- 3.3.1 Implementation -- 3.3.2 Evaluation -- 3.4 Music Analysis Using Deep Neural Networks -- 3.4.1 Deep Musical Autoencoder -- 3.4.2 Music Reconstruction Through Selection -- 3.5 Real-Time Audio Analysis of Prerecorded Music -- 3.5.1 Introduction -- 3.5.2 Previous Work -- 3.5.3 System Design
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|a 3.5.4 Live Audio Analysis -- 3.5.5 Gesture Design -- 3.5.6 Network Design -- 3.5.7 User Study -- 3.5.8 Summary -- References -- 4 ``Play Like A Machine''-Generative Musical Models for Robots -- 4.1 Abstract -- 4.2 Genetic Algorithms -- 4.2.1 Related Work -- 4.2.2 Method -- 4.3 Markov Processes (``Playing with the Masters'') -- 4.3.1 Related Work -- 4.3.2 Implementation -- 4.3.3 Summary -- 4.4 Path Planning Driven Music Generation -- 4.4.1 Search and Path Planning -- 4.4.2 Musical Path Planning -- 4.4.3 Planning -- 4.4.4 Evaluation -- 4.4.5 Discussion -- 4.5 Rule Based Jazz Improvisation
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|a 4.5.1 Parametrized Representations of Higher-Level Musical Semantics -- 4.5.2 Joint Optimization -- 4.5.3 Musical Results -- 4.5.4 Discussion -- 4.6 Neural Network Based Improvisation -- 4.6.1 Introduction -- 4.6.2 Semantic Relevance -- 4.6.3 Concatenation Cost -- 4.6.4 Ranking Units -- 4.6.5 Evaluating the Model -- 4.6.6 Discussion -- 4.6.7 Subjective Evaluation -- 4.6.8 Results -- 4.6.9 An Embodied Unit Selection Process -- 4.7 Conclusion -- References -- 5 ``Be Social''-Embodied Human-Robot Musical Interactions -- 5.1 Abstract -- 5.2 Embodied Interaction with Haile -- 5.2.1 Interaction Modes
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|a 5.2.2 Leader-Follower Interaction
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|a Includes bibliographical references and index.
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|a This book discusses the principles, methodologies, and challenges of robotic musicianship through an in-depth review of the work conducted at the Georgia Tech Center for Music Technology (GTCMT), where the concept was first developed. Robotic musicianship is a relatively new research field that focuses on the design and development of intelligent music-making machines. The motivation behind the field is to develop robots that not only generate music, but also collaborate with humans by listening and responding in an expressive and creative manner. This combination of human and machine creativity has the potential to surprise and inspire us to play, listen, compose, and think about music in new ways. The book provides an in-depth view of the robotic platforms designed at the GTCMT Robotic Musicianship Group, including the improvisational robotic percussionists Haile and Shimon, the personal robotic companion Shimi, and a number of wearable robots, such as the Robotic Drumming Prosthesis, The Third Drumming Arm, and the Skywalker Piano Hand. The book discusses numerous research studies based on these platforms in the context of five main principles: Listen like a Human, Play Like a Machine, Be Social, Watch and Learn, and Wear It.
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|a Online resource; title from digital title page (viewed on March 04, 2020).
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| 650 |
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|a Artificial intelligence
|x Musical applications.
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|a Computer music.
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|a Robots.
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|a Artificial intelligence
|x Musical applications
|2 fast
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|a Computer music
|2 fast
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|a Robots
|2 fast
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|a Bretan, Mason,
|e author.
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|a Hoffman, Guy,
|e author.
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| 700 |
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|a Driscoll, Scott,
|e author.
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|i has work:
|a Robotic musicianship (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCFGdm6bfdMjbG9hFFpcXVC
|4 https://id.oclc.org/worldcat/ontology/hasWork
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|i Print version:
|a Weinberg, Gil.
|t Robotic Musicianship : Embodied Artificial Creativity and Mechatronic Musical Expression.
|d Cham : Springer, ©2020
|z 9783030389291
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| 830 |
|
0 |
|a Automation, collaboration, & e-services.
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| 856 |
4 |
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|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-030-38930-7
|y Click for online access
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| 903 |
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|a SPRING-ENGINE2020
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| 994 |
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|a 92
|b HCD
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