| Description |
1 online resource (xi, 488 pages) : illustrations (some color) |
| Contents |
pt. I BACKGROUND -- 1. What is the Spectrum? -- 1.1. Functions and Signals -- 1.1.1. Functions of Time -- 1.1.2. Functions of Frequency -- 1.2. Fundamental Concepts of Spectrum -- 1.2.1. Periodicity and Pitch -- 1.2.2. Distributed Spectra -- 1.2.3. Dynamic Spectra -- 1.2.4. The Uncertainty Principle -- 1.3. Psychoacoustic Aspects -- 1.3.1. The Cochlear Mechanism -- 1.3.2. Critical Bandwidth -- 1.3.3. Loudness Perception -- 1.4. A Provisional Definition -- 2. A History of the Spectrum -- 2.1. Principles of Pitch and Scale -- 2.1.1. The Pythagorean Scale -- 2.1.2. Just Intonation -- 2.1.3. Musical Instruments -- 2.2. Classical Physics -- 2.2.1. Frequency and Pitch -- 2.2.2. Harmonics -- 2.2.3. Strings and the Wave Equation -- 2.2.4. Chladni Figures -- 2.2.5. Fourier's Theorem -- 2.2.6. Partials and Hearing -- 2.3. Helmholtzian Theory -- 2.3.1. Musical Tones and Noise -- 2.3.2. Resonators and Other Analytical Instruments -- 2.3.3. Theory of Spectral Hearing -- 2.3.4. Musical Timbre -- 2.3.5. Rayleigh's Theory of Sound -- 2.4. Twentieth Century -- 2.4.1. Electronic Instruments and Signal Processing -- 2.4.2. Electronic Music -- 2.4.3. Computer Music -- 3. Fundamental Aspects of Audio and Music Signals -- 3.1. The Nature of Audio Signals -- 3.1.1. Real Signals -- 3.1.2. Instantaneous Frequency and Phase -- 3.2. Manipulating Analogue Audio Signals -- 3.2.1. Non-Linear Distortion -- 3.2.2. Noise and Signal Level -- 3.2.3. Modulation -- 3.2.4. DC Offset -- 3.3. Discrete Signals -- 3.3.1. Sampling -- 3.3.2. The Discrete-Time Baseband -- 3.3.3. Digital Audio -- pt. II TECHNIQUES -- 4. Continuous and Discrete Spectra -- 4.1. The Fourier Series -- 4.1.1. Even and Odd Functions -- 4.1.2. Interpreting the Fourier Formula -- 4.1.3. The Fourier Series of a Square Wave -- 4.1.4. Complex Representation -- 4.2. The Fourier Transform -- 4.2.1. The Inverse Fourier Transform -- 4.2.2. Amplitude and Phase Spectra -- 4.2.3. The Spectra of Real Signals -- 4.2.4. The Spectra of Fundamental Signals -- 4.3. Convolution -- 4.3.1. Discrete Convolution -- 4.4. Sampling in Time and Frequency -- 4.4.1. Finite-Time Signals -- 4.4.2. Hard-Sync Waveforms -- 4.5. Classic Waveforms -- 4.5.1. The Sawtooth -- 4.5.2. Triangle Wave -- 4.5.3. Pulses -- 4.5.4. Additive Synthesis -- 4.6. The Fourier Spectrum -- 5. Discrete Time, Discrete Frequency -- 5.1. The Discrete Fourier Transform -- 5.1.1. Programming the DFT -- 5.1.2. Interpreting the DFT -- 5.1.3. Analysis Windows -- 5.2. The Fast Fourier Transform -- 5.2.1. Radix-2 FFT -- 5.2.2. ReaTto-Complex and Complex-to-Real Transforms -- 5.2.3. Other Radices -- 5.3. Discrete-Time Convolution -- 5.3.1. Direct Convolution -- 5.3.2. Fast Convolution -- 5.3.3. Partitioned Convolution -- 5.3.4. Multiple Partitions -- 5.3.5. Spectral Design Applications -- 5.4. Time-Varying Convolution -- 5.4.1. Implementation -- 5.4.2. Spectral Design Applications -- 5.5. The Discrete Spectrum -- 6. Time-Frequency Processing -- 6.1. Sub-band Signals -- 6.1.1. Designing a Bandpass Filter -- 6.1.2. The Phase Vocoder -- 6.2. The Short-Time Fourier Transform -- 6.2.1. Analysis Frame Rate -- 6.2.2. Phase Alignment -- 6.2.3. Resynthesis -- 6.3. Spectral Analysis-Synthesis -- 6.3.1. Phase Difference Method -- 6.3.2. Instantaneous Frequencies -- 6.3.3. One-Sample Hopsize -- 6.3.4. Sliding Transform -- 6.3.5. Phase Integration -- 6.4. Streaming Spectral Processing -- 6.4.1. The Spectral Analysis -- Synthesis Class -- 6.4.2. Spectral Signals in Csound -- 6.5. Spectral Manipulation -- 6.5.1. Filters -- 6.5.2. Blurring -- 6.5.3. Tracing -- 6.5.4. Stenciling -- 6.5.5. Mixing and Demixing -- 6.5.6. Frequency Scaling and Shifting -- 6.5.7. Spectral Envelope -- 6.5.8. Morphing -- 6.5.9. Spectral Delays -- 6.6. Timescale Modifications -- 6.6.1. Phase Locking -- 6.6.2. Pitch and Timescale -- 6.6.3. Csound Opcodes -- 6.7. The Hilbert Transform -- 6.8. The Dynamic Spectrum -- 7. The Spectra of Filters -- 7.1. Filters and Delays -- 7.1.1. Pure Delays -- 7.1.2. Inverse Comb Filter -- 7.2. The Z-Transform -- 7.2.1. Complex Polynomials -- 7.2.2. Zeros -- 7.2.3. The Z-Transform and the DFT -- 7.3. Zeros on the Complex Plane -- 7.3.1. First-Order Filters -- 7.3.2. Second-Order Filters -- 7.3.3. Minimum Phase -- 7.3.4. Linear Phase -- 7.4. Filter Design -- 7.4.1. Time-Domain Method -- 7.4.2. Frequency-Domain Method -- 7.4.3. Design Example -- 7.5. Feedback -- 7.5.1. Poles -- 7.5.2. Resonators -- 7.5.3. Stability -- 7.5.4. Phase Response -- 7.6. Recursive Filter Design -- 7.6.1. Parallel and Series Connections -- 7.6.2. Modeling Physical Systems -- 7.6.3. String Resonators -- 7.6.4. Allpass Filters -- 7.6.5. The Channel Vocoder -- 7.7. Time-Varying Filters -- 7.7.1. Allpass Phasers -- 7.7.2. Audio-Rate Coefficient Modulation -- 7.7.3. Delay Time Modulation -- 7.8. A Generalized Concept of Spectrum -- 8. Non-Linear Synthesis of Spectra -- 8.1. Closed-Form Synthesis Formulae -- 8.1.1. Generalized Summation Methods -- 8.2. Frequency and Phase Modulation Synthesis -- 8.2.1. Phase Modulation -- 8.2.2. Signal Bandwidth and Aliasing -- 8.2.3. Carrier to Modulator Ratio -- 8.2.4. Implementation -- 8.2.5. Frequency Modulation -- 8.2.6. Splitting Sidebands -- 8.2.7. Feedback -- 8.2.8. Complex PM -- 8.2.9. Exponential FM -- 8.3. Phase Distortion Synthesis -- 8.3.1. Vector Phase Shaping -- 8.4. Modified Frequency Modulation Synthesis -- 8.4.1. Phase-synchronous ModFM -- 8.4.2. Extended ModFM -- 8.5. Polynomial Waveshaping -- 8.5.1. Dynamic Spectra -- 8.5.2. Normalization -- 8.5.3. Implementation -- 8.5.4. Chebyshev Polynomials -- 8.5.5. Quadrature Waveshaping -- 8.6. Other Distortion Functions -- 8.7. Adaptive Modulation Methods -- 8.7.1. Adaptive Frequency Modulation -- 8.8. The Non-Linear Spectrum -- 9. Noise -- 9.1. Random Processes and Noise Signals -- 9.1.1. Centroid and Bandwidth -- 9.1.2. Probability Distribution and Density -- 9.1.3. Power Spectrum Density -- 9.1.4. Fractional Noise -- 9.1.5. Spectral Moments -- 9.2. Computing Noise -- 9.2.1. Random Number Generators -- 9.2.2. Sample and Hold -- 9.2.3. Heterodyning -- 9.2.4. Filtered Noise -- 9.2.5. Wavetables -- 9.3. Grain -- 9.3.1. Asynchronous Granular Synthesis -- 9.3.2. Wavelets -- 9.3.3. Matching Pursuit -- 9.4. The Spectral Envelope Revisited -- 9.4.1. Linear Prediction -- 9.4.2. Computing Prediction Coefficients -- 9.4.3. Synthesis -- 9.4.4. Spectral Representations -- 9.4.5. Streaming Linear Prediction -- 9.5. Spectral Models -- 9.5.1. Partial Tracking -- 9.5.2. Peak Identification -- 9.5.3. Peak Interpolation -- 9.5.4. Track Formation -- 9.5.5. Frequency and Phase -- 9.5.6. Synthesis -- 9.5.7. Residual Extraction -- 9.5.8. Modeling the Residual -- 9.5.9. Transients -- 9.5.10. Streaming Partial Track Processing -- 9.5.11. ATS -- 9.6. The Non-Deterministic Spectrum -- pt. III DESIGN -- 10. Spectral Design in Music -- 10.1. The Emergence of Spectral Color as a Structuring Device -- 10.1.1. Chords and Spectra -- 10.1.2. Instrumentation and Spectra -- 10.2. Audio Technology -- 10.2.1. Recording and Broadcasting as Carriers of Spectral Information -- 10.2.2. Changes in Instrumental Sound -- 10.2.3. The Mechano-Acoustic and the Electro-Acoustic -- 10.3. Electronic Music -- 10.3.1. The Feedback on Instrumental Writing -- 10.3.2. Electric Jazz, Rock, and Pop -- 10.3.3. Spectromorphology -- 10.3.4. Spectral Hearing -- 10.4. Computer Music -- 10.4.1. Risset's Catalog -- 10.4.2. Case Studies -- 10.5. The Musical Spectrum -- 11. Computer Sound Design -- 11.1. Additive Synthesis -- 11.1.1. Recursion -- 11.2. Non-Linear Distortion -- 11.2.1. Operator FM -- 11.2.2. Synthesis of Resonance -- 11.3. Source-Modifier Techniques -- 11.3.1. String Machines -- 11.3.2. The Vocoder -- 11.4. Granular Processing -- 11.5. Analysis-Synthesis -- 11.5.1. Spectral Envelopes -- 11.5.2. Morphing -- 11.5.3. Timescaling -- 11.5.4. Spectral Delays -- 11.6. Design Methods -- 12. Composing the Spectrum -- 12.1. Spectral Music-Making -- 12.1.1. Metaphors -- 12.1.2. Terminology -- 12.1.3. Realtime Systems and Performance -- 12.1.4. Physical and Virtual Space -- 12.1.5. Approaches to Composing the Spectrum -- 12.2. The Composition of Mouvements -- 12.2.1. The Generative Principle -- 12.2.2. Variations -- 12.2.3. Other Variants -- 12.2.4. Macrostructure -- 12.2.5. Discussion -- 12.3. Conclusion: the Spectral Playground |
| Summary |
In 'Spectral Sound Design', author Victor Lazzarini offers a practical set of 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 electronic music practice |
|
This volume offers a complete guide to a computational approach to spectral music-making. It provides, in a stepwise manner, a [ ] to the signal processing techniques and their application to computer music. The book begins with a series of fundamental definitions, delineating the basic concepts of spectral audio. This includes both a technical and a historical appreciation of the ideas related to the spectrum. The core of the text is formed by six chapters on the techniques of spectral musical signal processing. These are thoroughly illustrated with examples and code excerpts using the Python and Csound languages. This section of the book traces a path from the Fourier theorem to the consideration of non-deterministic signals, also in a step-by-step way discussing the various elements of spectral audio. The final part of the book is dedicated to the aesthetics of spectral music, and methods of design and composition, which apply the ideas and techniques explored earlier in the volume |
| Notes |
Also issued in print: 2021 |
| Bibliography |
Includes bibliographical references and index |
| Audience |
Specialized |
| Notes |
Description based on online resource; title from PDF title page (Oxford Scholarship Online, viewed April 22, 2022) |
| Subject |
Spectral music.
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Computer music.
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Computer music
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Spectral music
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| Form |
Electronic book
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| ISBN |
9780197524053 |
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0197524052 |
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