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Robotic Musicianship Embodied Artificial Creativity and Mechatronic Musical Expression

:	Gil Weinberg, Mason Bretan, Guy Hoffman, Scott Driscoll
:	Robotic Musicianship Embodied Artificial Creativity and Mechatronic Musical Expression
:	Springer-Verlag
:	9783030389307
:	1
:	CHF 142.50
:

:	Maschinenbau, Fertigungstechnik
:	English

:	270
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

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.

	Foreword	7
	Preface	9
	Contents	12
	1 Introduction	17
	1.1 Abstract	17
	1.2 Why Robotic Musicianship	17
	1.3 Sound Production and Design—Survey	19
	1.3.1 Traditional Instruments	20
	1.3.2 Augmented and Novel Instruments	24
	1.4 Musical Intelligence	25
	1.4.1 Sensing and Perception	26
	1.4.2 Music Generation	30
	1.5 Embodiment	32
	1.6 Integrating Robotic Musicianship into New Interfaces	34
	1.6.1 Musical Companion Robots	34
	1.6.2 Wearable Robotic Musicians	35
	1.7 Discussion	36
	References	37
	2 Platforms—Georgia Tech's Robotic Musicians	41
	2.1 Abstract	41
	2.2 Haile—A Robotic Percussionist	42
	2.2.1 Motivation	42
	2.2.2 Design	42
	2.3 Shimon—A Robotic Marimba Player	47
	2.3.1 Striker Design	47
	2.3.2 Mallet Motor Control	50
	2.3.3 Slider Motor Control	53
	2.3.4 Shimon's Socially Expressive Head	56
	2.4 Shimi—A Music Driven Robotic Dancing Companion	59
	2.4.1 Robotic Musical Companionship	60
	2.4.2 Design	61
	2.4.3 Software Architecture	62
	2.4.4 Core Capabilities	63
	2.5 The Robotic Drumming Prosthetic	66
	2.5.1 Motivation	67
	2.5.2 Related Work	68
	2.5.3 Platform	69
	2.5.4 Generative Physical Model for Stroke Generation	70
	2.5.5 Conclusions	75
	References	75
	3 ``Listen Like A Human''—Human-Informed Music Perception Models	78
	3.1 Abstract	78
	3.2 Rhythmic Analysis of Live Drumming	79
	3.2.1 Onset Detection	79
	3.2.2 Beat Detection	79
	3.2.3 Rhythmic Stability and Similarity	80
	3.2.4 User Study	83
	3.3 Tonal Music Analysis Using Symbolic Rules	84
	3.3.1 Implementation	85
	3.3.2 Evaluation	88
	3.4 Music Analysis Using Deep Neural Networks	91
	3.4.1 Deep Musical Autoencoder	91
	3.4.2 Music Reconstruction Through Selection	94
	3.5 Real-Time Audio Analysis of Prerecorded Music	95
	3.5.1 Introduction	95
	3.5.2 Previous Work	97
	3.5.3 System Design	97
	3.5.4 Live Audio Analysis	98
	3.5.5 Gesture Design	101
	3.5.6 Network Design	105
	3.5.7 User Study	107
	3.5.8 Summary	108
	References	109
	4 ``Play Like A Machine''—Generative Musical Models for Robots	110
	4.1 Abstract	110
	4.2 Genetic Algorithms	111
	4.2.1 Related Work	111
	4.2.2 Method	111
	4.3 Markov Processes (``Playing with the Masters'')	115
	4.3.1 Related Work	115
	4.3.2 Implementation	115
	4.3.3 Summary	119
	4.4 Path Planning Driven Music Generation	120
	4.4.1 Search and Path Planning	120
	4.4.2 Musical Path Planning	121
	4.4.3 Planning	123
	4.4.4 Evaluation	128
	4.4.5 Discussion	130
	4.5 Rule Based Jazz Improvisation	130
	4.5.1 Parametrized Representations of Higher-Level Musical Semantics	131
	4.5.2 Joint Optimization	138
	4.5.3 Musical Results	140
	4.5.4 Discussion	142
	4.6 Neural Network Based Improvisation	143
	4.6.1 Introduction	144
	4.6.2 Semantic Relevance	146
	4.6.3 Concatenation Cost	147
	4.6.4 Ranking Units	148
	4.6.5 Evaluating the Model	149
	4.6.6 Discussion	149
	4.6.7 Subjective Evaluation	150
	4.6.8 Results	150
	4.6.9 An Embodied Unit Selection Process	152
	4.7 Conclusion	154
	References	155
	5 ``Be Social''—Embodied Human-Robot Musical Interactions	158
	5.1 Abstract	158
	5.2 Embodied Interaction with Haile	158
	5.2.1 Interaction Modes	159
	5.2.2 Leader-Follower Interaction	161
	5.2.3 Evaluation	162
	5.2.4 Data Analysis	165
	5.2.5 Results	168