Machine MusicianshipMusicians begin formal training by acquiring a body of musical concepts commonly known as musicianship. These concepts underlie the musical skills of listening, performance, and composition. Like humans, computer music programs can benefit from a systematic foundation of musical knowledge. This book explores the technology of implementing musical processes such as segmentation, pattern processing, and interactive improvisation in computer programs. It shows how the resulting applications can be used to accomplish tasks ranging from the solution of simple musical problems to the live performance of interactive compositions and the design of musically responsive installations and Web sites. Machine Musicianship is both a programming tutorial and an exploration of the foundational concepts of musical analysis, performance, and composition. The theoretical foundations are derived from the fields of music theory, computer music, music cognition, and artificial intelligence. The book will be of interest to practitioners of those fields, as well as to performers and composers.The concepts are programmed using C++ and Max. The accompanying CD-ROM includes working versions of the examples, as well as source code and a hypertext document showing how the code leads to the program's musical functionality. |
Contents
Machine Musicianship | 1 |
11 The Motivation for Machine Musicianship | 3 |
12 Algorithmic Composition | 6 |
13 Algorithmic Analysis | 8 |
14 Structure of the Text | 12 |
15 Machine Musicianship Library | 14 |
Symbolic Processes | 17 |
21 Chord Theory | 18 |
62 Knowledge Representation | 241 |
63 Learning | 246 |
64 Expressive Performance | 264 |
Interactive Improvisation | 277 |
71 Sequences in Improvisation | 279 |
72 Influence on Improvisation Processes | 287 |
73 Transformation in Improvisation | 297 |
74 Player Paradigm Systems | 301 |
22 Context Sensitivity | 46 |
23 Key Induction | 60 |
24 C++ and Object Orientation | 77 |
Subsymbolic Processes | 93 |
32 Time Structures | 110 |
33 Beat Tracking | 122 |
34 Max Externals | 139 |
Segments and Patterns | 145 |
42 Pattern Processing | 168 |
43 Auditory Models | 191 |
Compositional Techniques | 201 |
51 Generation Techniques | 203 |
52 Score Following and Algorithmic Signal Processing | 212 |
53 Standard MIDI Files | 232 |
Algorithmic Expression and Music Cognition | 235 |
75 Ensemble Improvisation | 308 |
Interactive Multimedia | 317 |
81 Intimate Immensity | 319 |
82 A Flock of Words | 325 |
83 In Transit | 334 |
84 Multimedia Improvisation | 343 |
Installations | 355 |
92 Animated Improvisation | 362 |
93 Multimodal Environments | 372 |
Directions | 377 |
102 Research Directions | 378 |
381 | |
393 | |
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Common terms and phrases
algorithm algorithmic composition analysis application array artificial intelligence audio bang beat tracking calculated CD-ROM chord Chromosome composer composition Computer Music Conference const context Dannenberg developed duration elements event example function genetic algorithm harmonic human identify implemented improvisation inlet input integer interactive music International Computer Music intervals IRCAM jazz key induction learning listener Machine Musicianship major major seventh major triad match melodic metric MIDI messages milliseconds minor modulo operation music cognition musicians neural network nodes object onset output parameters Parncutt patch pattern perception performance Philippe Manoury piano pitch class played player PPQN preference rules produce pulse quantization real-time representation rhythm rhythmic Roger Dannenberg root salience scale score segments SeqSelect sequence shown in figure signal sound structure supervised learning techniques template tempo theory tion tonal tonic training set triad values velocity void weight zero