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Music Representation and Transformation in Software: Structure and Algorithms in Python 1st ed. 2022 [Pehme köide]

  • Formaat: Paperback / softback, 333 pages, kõrgus x laius: 254x178 mm, kaal: 677 g, 27 Illustrations, color; 177 Illustrations, black and white; XX, 333 p. 204 illus., 27 illus. in color., 1 Paperback / softback
  • Ilmumisaeg: 07-May-2022
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030974715
  • ISBN-13: 9783030974718
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Music Representation and Transformation in Software: Structure and Algorithms in Python 1st ed. 2022Suurem pilt
  • Formaat: Paperback / softback, 333 pages, kõrgus x laius: 254x178 mm, kaal: 677 g, 27 Illustrations, color; 177 Illustrations, black and white; XX, 333 p. 204 illus., 27 illus. in color., 1 Paperback / softback
  • Ilmumisaeg: 07-May-2022
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030974715
  • ISBN-13: 9783030974718
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783030974718.html
Märksõnad:
This book takes the reader on a journey through music concepts in an organized approach that develops music essentials from the concepts of tone, pitch, and time, through notes, intervals, chords, and scores while at the same time interpreting these elements as software artifacts. Close attention is paid to the organization of and relationships amongst these concepts and their representation as Python classes and objects, to learn about music from a software design viewpoint.The first part of the book focuses on software representation of the main elements found in music theory. Its objective is to provide direction to students on how to build a music software model from basic concepts and grow towards more complex concepts. Chapter by chapter, music concepts are introduced, and each is broken apart into related data properties and methods with the goal that by the end of this section, the reader will have developed a relatively complete library of music elements in software. The second part takes on the task of applying that foundation to the subject of music transformations. The focus is on localized transformations, that is, transformations isolated to a few measures. After a general introduction, the discussion includes topics of pitch assignment, key change, melodic inversion, melodic shaping, harmonic transcription, retrograde, melodic search and dilation.





This textbook is designed as a principal or supplemental source for computer science, software engineering, and programming courses. It can also be used as a main textbook for advanced computer music courses or electronic music courses. Computer music software professionals interested in learning how to model the complexities of music theory artifacts, or music students who want to learn advanced programming techniques in their domain will also find the book helpful.
Preface v
Introduction v
The Origin of this Book vi
Prerequisites and Approach vii
What this Book is Not About viii
Intended Audience ix
Regarding the Musical Examples x
About the Software x
Acknowledgements xii
PART 1 REPRESENTATION
1(172)
1 Software Modeling And Object-Oriented Design
3(8)
1.1 Object-Oriented Design by Example
3(3)
1.2 A Concise Introduction to Python Classes
6(2)
1.3 What Can Go Wrong in Object-Oriented Design
8(1)
1.4 The Meaning of Music Representation
9(2)
2 The Chromatic Scale And The Diatonic Foundation
11(14)
2.1 The Pythagorean Scale
11(2)
2.2 Just Intonation and Equal-Tempered Chromatic Scales
13(4)
2.2.1 The Chromatic Scale Representation
15(2)
2.3 Tones, Pitches, and the Diatonic Foundation
17(6)
2.3.1 Diatonic Tone Representation
19(2)
2.3.2 Diatonic ToneCache
21(1)
2.3.3 Diatonic Pitch Representation
22(1)
2.4 Exercises
23(2)
3 Intervals
25(16)
3.1 What is a Music Interval?
25(4)
3.1.1 Compound Intervals
28(1)
3.1.2 Interval Inversion
29(1)
3.2 Interval Analytics
29(5)
3.2.1 Negative Intervals
30(1)
3.2.2 Interval Reduction
31(1)
3.2.3 Inversions
32(1)
3.2.4 Interval Addition
32(1)
3.2.5 Afterword on Interval Operators
33(1)
3.3 Interval Representation and Implementation
34(4)
3.3.1 Negation, Inversion, and Reduction
35(1)
3.3.2 Interval Addition and Operator Overloading
35(2)
3.3.3 Exceptions
37(1)
3.3.4 Computing Start and End Pitches
38(1)
3.4 Examples
38(1)
3.5 Exercises
39(2)
4 Modality, Tonality, And Scales
41(14)
4.1 Modality and Tonality
41(5)
4.1.1 A Traditional Approach to Modality
42(1)
4.1.2 An Intervallic Approach to Modality
43(1)
4.1.3 Counterexamples
44(1)
4.1.4 Modes, Derived Modalities and Modal Index
45(1)
4.2 Modality and Tonality Representation
46(7)
4.2.1 Defining and Creating Modality
47(1)
4.2.2 System Modalities
48(1)
4.2.3 Modality Factory
49(1)
4.2.4 Modal Index, Incremental Intervals, and Tonal Scale
50(1)
4.2.5 Tonality Representation
50(2)
4.2.6 Pitch Scales
52(1)
4.3 Exercises
53(2)
5 Time In Music
55(16)
5.1 Three Notions of Time in Music, an Introduction
55(1)
5.2 Whole Note Time: Measuring Time in Fractions
56(1)
5.3 Time Signature and Tempo
57(2)
5.3.1 Time Signature
58(1)
5.3.2 Tempo
58(1)
5.4 Whole Time/Actual Time Conversions
59(2)
5.5 Whole Time/Beat Time Conversion
61(2)
5.6 Representational Considerations
63(6)
5.6.1 Whole Note Time Representation
64(1)
5.6.2 Position and Duration
64(1)
5.6.3 Beat Position
65(1)
5.6.4 Time Signature and Tempo classes
66(1)
5.6.5 The Time Conversion Algorithms
67(2)
5.7 Exercises
69(2)
6 The Note And Note Aggregates
71(18)
6.1 Notes and Their Aggregative Partners
71(3)
6.1.1 The Note
71(2)
6.1.2 Beams
73(1)
6.1.3 Tuplets
73(1)
6.2 Representation
74(9)
6.2.1 The Note Inheritance Hierarchy
74(7)
6.2.2 Semantic Clarifications
81(2)
6.3 Algorithms
83(5)
6.3.1 Accessing Notes
83(1)
6.3.2 Adding a Note
84(3)
6.3.3 Reversing a Melody
87(1)
6.4 Exercises
88(1)
7 Chords
89(14)
7.1 A Brief Introduction to Chords
89(3)
7.1.1 Tertian Chord Types
90(1)
7.1.2 Secundal Chord Types
90(1)
7.1.3 Quartal Chord Types
91(1)
7.1.4 Secondary Chord Types
91(1)
7.2 Chord Template and Chord Representation
92(3)
7.2.1 Template and Chord Class Design
93(1)
7.2.2 Chord Construction
93(2)
7.3 Chord Categories: Syntax and Examples
95(5)
7.3.1 Tertian Chords
96(1)
7.3.2 Secundal Chords
97(1)
7.3.3 Quartal Chords
98(1)
7.3.4 Secondary Chords
99(1)
7.4 Exercises
100(3)
8 Instruments
103(12)
8.1 A Simple Genealogical Model for Instruments
103(2)
8.2 A Software Model for an Instrument Catalog
105(1)
8.3 The Instrument Catalog Class Model
105(7)
8.3.1 Articulations
107(1)
8.3.2 Accessing Instrument Data
107(2)
8.3.3 The Instrument Catalog as Persistent Data
109(2)
8.3.4 Instrument Catalog Initialization
111(1)
8.4 Exercises
112(3)
9 Score Representation
115(20)
9.1 What is a Musical Score?
116(1)
9.2 Score as a Class Model
117(7)
9.2.1 Voices and Lines
118(3)
9.2.2 Event Sequences for Tempo, Time Signature, and Dynamics
121(2)
9.2.3 What Happened to Key Signature?
123(1)
9.2.4 Putting It All Together
123(1)
9.3 Time-Based Note Search
124(6)
9.3.1 Red-Black Trees
125(1)
9.3.2 Notes and Intervals
125(1)
9.3.3 Interval Search Trees
126(1)
9.3.4 Finding all Notes Sounding in an Interval
127(2)
9.3.5 Finding All Notes Starting in an Interval
129(1)
9.3.6 Adding Note Search to Score
129(1)
9.4 Score Management
130(3)
9.4.1 Interval Tree Update using the Observer Pattern
130(3)
9.5 Final Remarks
133(1)
9.6 Exercises
133(2)
10 Rendering Score To Midi
135(12)
10.1 An Introduction to MIDI
135(5)
10.1.1 Tracks, Channels, and Messages
136(2)
10.1.2 Time Representation in MIDI
138(1)
10.1.3 MIDI Messages
138(2)
10.2 Mapping a Score to a MIDI File
140(1)
10.3 Time Conversions
141(1)
10.4 Score to MIDI Conversion Algorithms
142(2)
10.5 Converting a Line to a MIDI File
144(1)
10.6 Exercises
145(2)
11 Variable Dynamics And Tempo
147(18)
11.1 What are Variable Dynamics and Tempo?
147(2)
11.1.1 An Introduction to Variable Dynamics and Tempo
147(2)
11.2 Functions and Graphs
149(7)
11.2.1 The Piecewise Linear Function
150(2)
11.2.2 The Stepwise Function
152(1)
11.2.3 The Constant Function
153(1)
11.2.4 Generic Univariate Function
153(2)
11.2.5 Domain Reset with Affine Transformations
155(1)
11.3 Using Functions for Variable Tempo
156(4)
11.3.1 Tempo Functions and the Tempo Event Sequence
157(2)
11.3.2 Tempo Functions, MIDI Generation and Time Conversion
159(1)
11.4 Using Variable Dynamics Functions
160(3)
11.4.1 Details on Variable Dynamics Functions
160(1)
11.4.2 Computing MIDI Velocity Events
161(2)
11.5 Exercises
163(2)
12 Further Representations
165(8)
12.1 Harmonic Context
165(2)
12.1.1 Harmonic Context and Harmonic Context Track Representations
166(1)
12.2 A Syntax for Constructing Music Structures
167(4)
12.2.1 The Music Parser
168(1)
12.2.2 A Music Syntax
168(3)
12.2.3 Invoking the Parser in Code
171(1)
12.3 Lite Score
171(1)
12.4 Exercises
172(1)
PART 2 MUSIC TRANSFORMATIONS
173(136)
13 An Introduction To Music Transformations
175(4)
13.1 What is a Music Transformation?
175(1)
13.2 Outline on Transformations
176(1)
13.3 A Word on Machine Learning
177(1)
13.4 Moving Forward
178(1)
14 A Constraint Engine For Pitch Assignment
179(12)
14.1 Constraint Programming
179(1)
14.2 Music Constraints and Solution Spaces
180(2)
14.3 Music Constraint Programming Elements
182(5)
14.3.1 PMaps
182(2)
14.3.2 Constraints
184(2)
14.3.3 Types of Constraints
186(1)
14.3.4 Invoking the Pitch Constraint Solver
187(1)
14.4 Examples
187(3)
14.5 Final Thoughts
190(1)
15 Shift: A Transform For Key Change
191(16)
15.1 An Overview of the Shift Transform
191(2)
15.2 Structural Design of the Shift Transform
193(1)
15.3 Tonal and Pitch Functions
194(4)
15.3.1 Tonal Functions
194(1)
15.3.2 Cross Tonality Shift Tonal Function
195(2)
15.3.3 General Pitch Function
197(1)
15.3.4 Cross Tonality Shift Pitch Function
197(1)
15.4 The Shift Transform
198(4)
15.4.1 Problem Context and Interface
198(1)
15.4.2 The Main Loop
199(1)
15.4.3 Clarifying Shifting with Mode Change
200(1)
15.4.4 Clarifying Shift with Secondary Chords
201(1)
15.5 Examples
202(4)
15.5.1 Simple Key Shift using Intervals
202(1)
15.5.2 Shift with Modality Change
203(1)
15.5.3 Modality and Modal Index Change
204(1)
15.5.4 Modulating Sequence
205(1)
15.6 Final Thoughts
206(1)
16 Reflection: A Transform For Melodic Inversion
207(16)
16.1 An Overview of Melodic Reflection
208(1)
16.2 Permutations
209(2)
16.2.1 Definition and Permutation Composition
209(1)
16.2.2 Using Permutations
210(1)
16.3 Class Structures for the Reflection Transform
211(1)
16.4 Scalar Reflection
212(5)
16.4.1 Scalar Reflection Details
212(1)
16.4.2 T Diatonic Reflection
213(1)
16.4.3 Examples of Scalar Reflection
214(3)
16.5 Chromatic Reflection
217(4)
16.5.1 Chromatic Reflection Details
217(1)
16.5.2 T Chromatic Reflection
218(1)
16.5.3 Examples of Chromatic Reflection
219(2)
16.6 Final Thoughts
221(2)
17 Reshape: A Transform For Melodic Shaping
223(11)
17.1 Introduction
223(2)
17.2 A Model for Shaping Functions
225(3)
17.2.1 Shaping Functions
225(1)
17.2.2 The Pitch Range Interpreter
226(2)
17.3 A Representation for Shaping Functions and Pitch Interpreters
228(6)
17.3.1 The Pitch Range Interpreters, Description and Examples
229(2)
17.3.2 Shaping Classes, Description and Examples
231(3)
17 A Melodic Forms and Constraints
234(13)
17.5 The Reshape Transform
238(1)
17.5.1 The TReshape Transform API
239(1)
17.6 Examples
239(6)
17.7 Final Thoughts
245(2)
18 Harmonic Transcription: Preserving Melody Over Harmonic Change
247(14)
18.1 Introduction
247(2)
18.2 Melodic Analysis for Similarity
249(2)
18.2.1 Harmonic Role Analysis
249(1)
18.2.2 Melodic Contour Analysis
249(1)
18.2.3 Software Design
250(1)
18.3 Further Constraining the Search Space
251(2)
18.3.1 The Melodic Window
252(1)
18.3.2 Melodic Variance (Tunnel)
252(1)
18.4 The Harmonic Transcription Transform
253(2)
18.5 Examples
255(4)
18.6 Final Thoughts
259(2)
19 Retrograde: Reversing A Melody
261(8)
19.1 Introduction
261(2)
19.2 The Retrograde Transform
263(1)
19.2.1 The Retrograde Transform Design
263(1)
19.2.2 The Retrograde Transform API
264(1)
19.3 Examples
264(3)
19.4 Final Thoughts
267(2)
20 Melodic Search And Pattern Substitution
269(18)
20.1 Melodic Search
269(7)
20.1.1 Melodic Search Options
271(1)
20.1.2 Melodic Search Technical Details
272(3)
20.1.3 Melodic Search API
275(1)
20.1.4 Melodic Search Examples
275(1)
20.2 The Melodic Pattern Substitution Transform
276(5)
20.2.1 The Foundations of Melodic Pattern Substitution
276(2)
20.2.2 An Approach to Melodic Pattern Substitution
278(1)
20.2.3 Harmonic Context Expression Rules
278(2)
20.2.4 The Melodic Pattern Substitution API
280(1)
20.3 Examples
281(3)
20.4 Final Thoughts
284(3)
21 Dilation: Musical Expansion And Contraction
287(8)
21.1 A Discussion on Dilation
287(4)
21.1.1 Dilation Dynamics
288(3)
21.1.2 The Dilation Transform API
291(1)
21.2 Examples
291(2)
21.3 Final Thoughts
293(2)
22 The Step Shift Transform And Sequences
295(10)
22.1 An Introduction to Step Shift
295(1)
22.2 Step Shift Dynamics
296(4)
22.2.1 Pitch Remap
296(2)
22.2.2 Simple Chord Remap
298(1)
22.2.3 Secondary Chords
298(1)
22.2.4 The Step Shift API
299(1)
22.3 Examples
300(3)
22.4 Final Thoughts
303(2)
23 Final Thoughts
305(4)
23.1 The Quest for a Broader Musical Synthesis
305(1)
23.2 The Quest for Real-time Synthesis
306(1)
23.3 Automated Score Analysis
307(1)
23.4 Forward
307(2)
APPENDICES
309(22)
A Modality Varieties
309(2)
A.1 Diatonic Modalities
309(1)
A.2 Pentatonic Modalities
310(1)
A.3 Octatonic Modality
310(1)
A.4 Whole Tone Modality
310(1)
A.5 Blues Modality
310(1)
B Orderedmap
311(2)
C The Observer Pattern
313(2)
D Chords
315(4)
D.1 Tertian Chords
315(1)
D.2 Secundal Chords
316(1)
D.3 Quartal Chords
316(3)
E A Brief Introduction To XML
319(2)
E.1 Tags, Elements, and Attributes
319(1)
E.2 Data Structuring
319(1)
E.3 Reading an XML File with Python
320(1)
F Binary Search Trees
321(2)
G Processing Constraints: The Design Of A Pitch Constraint Engine
323(8)
G.1 Two Straight Forward Solutions
323(1)
G.1.1 Depth-First Search
323(2)
G.1.2 Breadth-First Search and Chains
325(2)
G.2 Improvements on the Constraint Solution Algorithm
327(4)
Bibliography 331
Donald P. Pazel is a computer scientist and software engineer who has worked on a wide diversity of software projects. He was a member of the computer music research team in the Mathematics Department at the IBM TJ Watson Research Center in New York. That work focused on MIDI-based composition software and real-time music modification. Various prototypes were developed during his time there, e.g., MusicSketcher, a prototype application that demonstrated a wide range of music transformational capability. He is also a musician with long-time experience in composition and piano performance. In this book, he develops music theory software based on his music experiences and knowledge of object-oriented and algorithmic programming in Python.