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E-raamat: Spectral Music Design: A Computational Approach

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(Professor of Music, Maynooth University)
  • Formaat: 528 pages
  • Ilmumisaeg: 23-Jul-2021
  • Kirjastus: Oxford University Press Inc
  • Keel: eng
  • ISBN-13: 9780197524046
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Spectral Music Design: A Computational ApproachSuurem pilt
  • Formaat: 528 pages
  • Ilmumisaeg: 23-Jul-2021
  • Kirjastus: Oxford University Press Inc
  • Keel: eng
  • ISBN-13: 9780197524046
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Processing audio in the spectral domain has become a practical proposition for a variety of applications in computer music, composition, and sound design, making it an area of significant interest for musicians, programmers, sound designers, and researchers. While spectral processing has been
explored already from a variety of perspectives, previous approaches tended to be piecemeal: some dealt with signal processing details, others with a high-level music technology discussion of techniques, some more compositionally focused, and others at music/audio programming concerns. As author
Victor Lazzarini argues, the existing literature has made a good footprint in the area but has failed to integrate these various approaches within spectral audio. In Spectral Sound Design: A Computational Approach, Lazzarini provides an antidote. Spectral Sound Design: A Computational Approach gives
authors a set of practical tools to implement processing techniques and algorithms in a balanced way, covering application aspects as well the fundamental theory that underpins them, within the context of contemporary and electronic music practice. The book employs a mix of Python for prototyping
and Csound for deployment and music programming. The tight integration of these three languages as well as the wide scope offered by the combination (going from embedded to supercomputing, and including web-based and mobile applications) makes it the go-to resource to deal with the practical aspects
of the subject.

Arvustused

Victor Lazzarini has condensed thirty years of experience of into a dazzlingly lucid resource that will inform and delight all those studying and making spectral music with computers. The book covers both the fundamental and creative aspects of spectral music design, leading through detailed accounts of computer synthesis and digital signal processing to illuminating discussions of how composers and musicians have developed their approaches. This is an indispensable book that provides an excellent technical and artistic foundation for any digital musician. * Andrew Hugill, School of Informatics, University of Leicester * Spectral Music Design: A Computational Approach will prove a vast and deep source of insight, inspiration, enlightenment, and understanding for the innovative composer, producer, sound designer, and developer. Drawing on a lifetime of experience in the classroom in the studio, on the stage, and in the lab, the 'Leonardo da Vinci' of DSP - Victor Lazzarini - illustrates, demonstrates, explains and shares his masterpieces of code and music in this incredibly inspiring book. For years to come, Spectral Music Design will be a go-to resource for teachers and students of computer music, acoustics, digital signal processing, and computer science, as well as cutting-edge electronic music producers, innovative sound designers, and audio app developers. * Dr. Richard Boulanger, Professor of Electronic Production and Design - Berklee College of Music *

Foreword v
Preface vii
Acknowledgments xi
PART I BACKGROUND
1(72)
1 What is the Spectrum?
3(18)
1.1 Functions and Signals
3(5)
1.1.1 Functions of Time
4(2)
1.1.2 Functions of Frequency
6(2)
1.2 Fundamental Concepts of Spectrum
8(6)
1.2.1 Periodicity and Pitch
8(2)
1.2.2 Distributed Spectra
10(1)
1.2.3 Dynamic Spectra
11(1)
1.2.4 The Uncertainty Principle
12(2)
1.3 Psychoacoustic Aspects
14(4)
1.3.1 The Cochlear Mechanism
15(1)
1.3.2 Critical Bandwidth
16(1)
1.3.3 Loudness Perception
17(1)
1.4 A Provisional Definition
18(3)
2 A History of the Spectrum
21(29)
2.1 Principles of Pitch and Scale
21(4)
2.1.1 The Pythagorean Scale
22(1)
2.1.2 Just Intonation
23(1)
2.1.3 Musical Instruments
24(1)
2.2 Classical Physics
25(5)
2.2.1 Frequency and Pitch
25(1)
2.2.2 Harmonics
26(1)
2.2.3 Strings and the Wave Equation
26(2)
2.2.4 Chladni Figures
28(1)
2.2.5 Fourier's Theorem
28(2)
2.2.6 Partials and Hearing
30(1)
2.3 Helmholtzian Theory
30(7)
2.3.1 Musical Tones and Noise
31(1)
2.3.2 Resonators and Other Analytical Instruments
32(2)
2.3.3 Theory of Spectral Hearing
34(1)
2.3.4 Musical Timbre
35(1)
2.3.5 Rayleigh's Theory of Sound
36(1)
2.4 Twentieth Century
37(13)
2.4.1 Electronic Instruments and Signal Processing
37(3)
2.4.2 Electronic Music
40(1)
2.4.3 Computer Music
41(9)
3 Fundamental Aspects of Audio and Music Signals
50(23)
3.1 The Nature of Audio Signals
50(3)
3.1.1 Real Signals
51(1)
3.1.2 Instantaneous Frequency and Phase
52(1)
3.2 Manipulating Analogue Audio Signals
53(8)
3.2.1 Non-Linear Distortion
55(2)
3.2.2 Noise and Signal Level
57(1)
3.2.3 Modulation
58(2)
3.2.4 DC Offset
60(1)
3.3 Discrete Signals
61(12)
3.3.1 Sampling
63(3)
3.3.2 The Discrete-Time Baseband
66(4)
3.3.3 Digital Audio
70(3)
PART II TECHNIQUES
73(310)
4 Continuous and Discrete Spectra
75(31)
4.1 The Fourier Series
75(4)
4.1.1 Even and Odd Functions
76(1)
4.1.2 Interpreting the Fourier Formula
76(2)
4.1.3 The Fourier Series of a Square Wave
78(1)
4.1.4 Complex Representation
78(1)
4.2 The Fourier Transform
79(6)
4.2.1 The Inverse Fourier Transform
80(1)
4.2.2 Amplitude and Phase Spectra
80(1)
4.2.3 The Spectra of Real Signals
80(3)
4.2.4 The Spectra of Fundamental Signals
83(2)
4.3 Convolution
85(4)
4.3.1 Discrete Convolution
86(3)
4.4 Sampling in Time and Frequency
89(7)
4.4.1 Finite-Time Signals
91(3)
4.4.2 Hard-Sync Waveforms
94(2)
4.5 Classic Waveforms
96(6)
4.5.1 The Sawtooth
98(1)
4.5.2 Triangle Wave
98(2)
4.5.3 Pulses
100(1)
4.5.4 Additive Synthesis
101(1)
4.6 The Fourier Spectrum
102(4)
5 Discrete Time, Discrete Frequency
106(50)
5.1 The Discrete Fourier Transform
106(12)
5.1.1 Programming the DFT
107(3)
5.1.2 Interpreting the DFT
110(3)
5.1.3 Analysis Windows
113(5)
5.2 The Fast Fourier Transform
118(12)
5.2.1 Radix-2 FFT
118(4)
5.2.2 ReaTto-Complex and Complex-to-Real Transforms
122(5)
5.2.3 Other Radices
127(3)
5.3 Discrete-Time Convolution
130(18)
5.3.1 Direct Convolution
131(5)
5.3.2 Fast Convolution
136(3)
5.3.3 Partitioned Convolution
139(3)
5.3.4 Multiple Partitions
142(3)
5.3.5 Spectral Design Applications
145(3)
5.4 Time-Varying Convolution
148(4)
5.4.1 Implementation
149(1)
5.4.2 Spectral Design Applications
149(3)
5.5 The Discrete Spectrum
152(4)
6 Time-Frequency Processing
156(48)
6.1 Sub-band Signals
156(2)
6.1.1 Designing a Bandpass Filter
156(1)
6.1.2 The Phase Vocoder
157(1)
6.2 The Short-Time Fourier Transform
158(6)
6.2.1 Analysis Frame Rate
160(1)
6.2.2 Phase Alignment
161(1)
6.2.3 Resynthesis
162(2)
6.3 Spectral Analysis-Synthesis
164(8)
6.3.1 Phase Difference Method
166(1)
6.3.2 Instantaneous Frequencies
167(2)
6.3.3 One-Sample Hopsize
169(1)
6.3.4 Sliding Transform
170(1)
6.3.5 Phase Integration
171(1)
6.4 Streaming Spectral Processing
172(10)
6.4.1 The Spectral Analysis--Synthesis Class
172(7)
6.4.2 Spectral Signals in Csound
179(3)
6.5 Spectral Manipulation
182(13)
6.5.1 Filters
182(1)
6.5.2 Blurring
183(3)
6.5.3 Tracing
186(1)
6.5.4 Stenciling
186(1)
6.5.5 Mixing and Demixing
187(1)
6.5.6 Frequency Scaling and Shifting
187(2)
6.5.7 Spectral Envelope
189(4)
6.5.8 Morphing
193(1)
6.5.9 Spectral Delays
193(2)
6.6 Timescale Modifications
195(3)
6.6.1 Phase Locking
196(1)
6.6.2 Pitch and Timescale
197(1)
6.6.3 Csound Opcodes
198(1)
6.7 The Hilbert Transform
198(2)
6.8 The Dynamic Spectrum
200(4)
7 The Spectra of Filters
204(65)
7.1 Filters and Delays
205(4)
7.1.1 Pure Delays
205(1)
7.1.2 Inverse Comb Filter
206(3)
7.2 The Z-Transform
209(2)
7.2.1 Complex Polynomials
210(1)
7.2.2 Zeros
210(1)
7.2.3 The Z-Transform and the DFT
211(1)
7.3 Zeros on the Complex Plane
211(7)
7.3.1 First-Order Filters
213(1)
7.3.2 Second-Order Filters
214(2)
7.3.3 Minimum Phase
216(1)
7.3.4 Linear Phase
217(1)
7.4 Filter Design
218(6)
7.4.1 Time-Domain Method
218(1)
7.4.2 Frequency-Domain Method
219(2)
7.4.3 Design Example
221(3)
7.5 Feedback
224(7)
7.5.1 Poles
225(1)
7.5.2 Resonators
226(3)
7.5.3 Stability
229(1)
7.5.4 Phase Response
230(1)
7.6 Recursive Filter Design
231(22)
7.6.1 Parallel and Series Connections
231(5)
7.6.2 Modeling Physical Systems
236(1)
7.6.3 String Resonators
237(5)
7.6.4 Allpass Filters
242(6)
7.6.5 The Channel Vocoder
248(5)
7.7 Time-Varying Filters
253(9)
7.7.1 Allpass Phasers
254(3)
7.7.2 Audio-Rate Coefficient Modulation
257(2)
7.7.3 Delay Time Modulation
259(3)
7.8 A Generalized Concept of Spectrum
262(7)
8 Non-Linear Synthesis of Spectra
269(51)
8.1 Closed-Form Synthesis Formulae
270(10)
8.1.1 Generalized Summation Methods
273(7)
8.2 Frequency and Phase Modulation Synthesis
280(12)
8.2.1 Phase Modulation
281(1)
8.2.2 Signal Bandwidth and Aliasing
282(2)
8.2.3 Carrier to Modulator Ratio
284(1)
8.2.4 Implementation
285(1)
8.2.5 Frequency Modulation
286(1)
8.2.6 Splitting Sidebands
287(1)
8.2.7 Feedback
288(1)
8.2.8 Complex PM
289(2)
8.2.9 Exponential FM
291(1)
8.3 Phase Distortion Synthesis
292(4)
8.3.1 Vector Phase Shaping
293(3)
8.4 Modified Frequency Modulation Synthesis
296(5)
8.4.1 Phase-synchronous ModFM
296(3)
8.4.2 Extended ModFM
299(2)
8.5 Polynomial Waveshaping
301(10)
8.5.1 Dynamic Spectra
302(2)
8.5.2 Normalization
304(1)
8.5.3 Implementation
304(3)
8.5.4 Chebyshev Polynomials
307(2)
8.5.5 Quadrature Waveshaping
309(2)
8.6 Other Distortion Functions
311(2)
8.7 Adaptive Modulation Methods
313(2)
8.7.1 Adaptive Frequency Modulation
314(1)
8.8 The Non-Linear Spectrum
315(5)
9 Noise
320(63)
9.1 Random Processes and Noise Signals
321(7)
9.1.1 Centroid and Bandwidth
321(1)
9.1.2 Probability Distribution and Density
322(1)
9.1.3 Power Spectrum Density
323(2)
9.1.4 Fractional Noise
325(1)
9.1.5 Spectral Moments
325(3)
9.2 Computing Noise
328(6)
9.2.1 Random Number Generators
329(2)
9.2.2 Sample and Hold
331(1)
9.2.3 Heterodyning
332(1)
9.2.4 Filtered Noise
332(1)
9.2.5 Wavetables
333(1)
9.3 Grain
334(8)
9.3.1 Asynchronous Granular Synthesis
334(3)
9.3.2 Wavelets
337(4)
9.3.3 Matching Pursuit
341(1)
9.4 The Spectral Envelope Revisited
342(12)
9.4.1 Linear Prediction
342(1)
9.4.2 Computing Prediction Coefficients
343(3)
9.4.3 Synthesis
346(1)
9.4.4 Spectral Representations
347(5)
9.4.5 Streaming Linear Prediction
352(2)
9.5 Spectral Models
354(24)
9.5.1 Partial Tracking
355(2)
9.5.2 Peak Identification
357(1)
9.5.3 Peak Interpolation
358(1)
9.5.4 Track Formation
359(4)
9.5.5 Frequency and Phase
363(2)
9.5.6 Synthesis
365(5)
9.5.7 Residual Extraction
370(3)
9.5.8 Modeling the Residual
373(2)
9.5.9 Transients
375(1)
9.5.10 Streaming Partial Track Processing
375(2)
9.5.11 ATS
377(1)
9.6 The Non-Deterministic Spectrum
378(5)
PART III DESIGN
383(92)
10 Spectral Design in Music
385(29)
10.1 The Emergence of Spectral Color as a Structuring Device
386(5)
10.1.1 Chords and Spectra
387(1)
10.1.2 Instrumentation and Spectra
388(3)
10.2 Audio Technology
391(3)
10.2.1 Recording and Broadcasting as Carriers of Spectral Information
391(1)
10.2.2 Changes in Instrumental Sound
392(1)
10.2.3 The Mechano-Acoustic and the Electro-Acoustic
393(1)
10.3 Electronic Music
394(4)
10.3.1 The Feedback on Instrumental Writing
395(1)
10.3.2 Electric Jazz, Rock, and Pop
396(1)
10.3.3 Spectromorphology
397(1)
10.3.4 Spectral Hearing
398(1)
10.4 Computer Music
398(12)
10.4.1 Risset's Catalog
399(2)
10.4.2 Case Studies
401(9)
10.5 The Musical Spectrum
410(4)
11 Computer Sound Design
414(35)
11.1 Additive Synthesis
414(7)
11.1.1 Recursion
418(3)
11.2 Non-Linear Distortion
421(8)
11.2.1 Operator FM
421(4)
11.2.2 Synthesis of Resonance
425(4)
11.3 Source-Modifier Techniques
429(6)
11.3.1 String Machines
430(3)
11.3.2 The Vocoder
433(2)
11.4 Granular Processing
435(2)
11.5 Analysis-Synthesis
437(8)
11.5.1 Spectral Envelopes
438(2)
11.5.2 Morphing
440(1)
11.5.3 Timescaling
441(2)
11.5.4 Spectral Delays
443(2)
11.6 Design Methods
445(4)
12 Composing the Spectrum
449(26)
12.1 Spectral Music-Making
449(5)
12.1.1 Metaphors
450(1)
12.1.2 Terminology
451(1)
12.1.3 Realtime Systems and Performance
451(1)
12.1.4 Physical and Virtual Space
452(1)
12.1.5 Approaches to Composing the Spectrum
453(1)
12.2 The Composition of Mouvements
454(19)
12.2.1 The Generative Principle
454(2)
12.2.2 Variations
456(6)
12.2.3 Other Variants
462(5)
12.2.4 Macrostructure
467(4)
12.2.5 Discussion
471(2)
12.3 Conclusion: the Spectral Playground
473(2)
References 475(8)
Index 483
Victor Lazzarini completed his doctorate at the University of Nottingham, UK, where he received the Heyman scholarship for research progress and the Hallward composition prize for a large-scale work, Magnificat. His interests include musical signal processing and sound synthesis; computer music languages; electroacoustic and instrumental composition. He joined the Music Department at Maynooth University in 1998 and was Dean of the Faculty of Arts from 2014 to 2019.
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