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International Standard Book Number — Click for https://mathworld.wolfram.com/ISBN.html

Matlab is a high-level scripting language for scientific computing — Click for https://ccrma.stanford.edu/~jos/matlab/

A frequency warp is a non-uniform scaling of frequency. Frequency warps are commonly used in audio signal processing in order to better mimic the non-uniform frequency resolution of human hearing. — Click for https://ccrma.stanford.edu/~jos/bbt/Filter_Design_Example.html

The Gaussian probability density function is, in Matlab syntax, exp(-(x-mu)^2/(2*sigma^2))/sqrt(2*pi*sigma^2), where mu is the mean and sigma is the standard deviation. — Click for http://en.wikipedia.org/wiki/Normal_distribution

Click for https://ccrma.stanford.edu/~jos/sasp/Beginning_Statistical_Signal_Processing.html

The Fourier theorems establish important, elementary time-frequency relationships. The theorems are largely the same whether stated for the case of the Fourier Transform (FT), Discrete Fourier Transform (DFT), Discrete Time Fourier Transform (DTFT), or Fourier Series (FS). — Click for https://ccrma.stanford.edu/~jos/mdft/Fourier_Theorems.html

A set of filters that decompose a signal into a set of components — Click for http://en.wikipedia.org/wiki/Filter_bank

Click for https://ccrma.stanford.edu/~jos/sasp/Continuous_Wavelet_Transform.html#33749

A filter in the audio signal processing context is any operation that accepts a signal as an input and produces a signal as an output. Most practical audio filters are linear and time invariant, in which case they can be characterized by their impulse response or their frequency response. — Click for https://ccrma.stanford.edu/~jos/filters/What_Filter.html

The Short Time Fourier Transform (STFT) computes the spectrum (DFT) of successive time frames of a signal. — Click for https://ccrma.stanford.edu/~jos/sasp/Short_Time_Fourier_Transform.html

Noise (in signal processing) is a random signal. In the language of statistical signal processing, a noise signal is typically modeled as 'stochastic process', which is in turn defined as a sequence of random variables. — Click for https://ccrma.stanford.edu/~jos/sasp/What_Noise.html

The spectrum of a signal gives the distribution of signal energy as a function of frequency. — Click for http://ccrma.stanford.edu/~jos/mdft/Example_Applications_DFT.html

A sinusoid is any function of the form A sin(ω t+φ), where t is the independent variable, and A, ω, φ are fixed parameters of the sinusoid called the amplitude, (radian) frequency, and phase, respectively. Sinusoidal motion is produced by any 'pure' vibration, such as that of an ideal tuning fork or mass-spring system. — Click for https://ccrma.stanford.edu/~jos/mdft/Sinusoids.html

The spectrum of a signal gives the distribution of signal energy as a function of frequency. — Click for http://ccrma.stanford.edu/~jos/mdft/Example_Applications_DFT.html

A digital filter can be visualized as a 'black box' that accepts a sequence of numbers and emits a new sequence of numbers. In digital audio signal processing applications, such number sequences usually represent sounds. For example, digital filters are used to implement graphic equalizers and other digital audio effects. — Click for https://ccrma.stanford.edu/~jos/filters/

In digital signal processing, FIR is an acronym for Finite Impulse Response — Click for https://ccrma.stanford.edu/~jos/filters/FIR_Digital_Filters.html

Spectrum analysis of sound is analogous to decomposing white light into its component colors by means of a prism — Click for https://ccrma.stanford.edu/~jos/mdft/Example_Applications_DFT.html

The Fourier Transform (FT) gives the (complex) spectrum of a continuous-time signal of any length. — Click for https://ccrma.stanford.edu/~jos/mdft/Fourier_Transform_FT_Inverse.html

A signal is typically a real-valued function of time. A discrete-time signal is typically a real-valued function of discrete time, and is therefore a time-ordered sequence of real numbers. — Click for http://ccrma.stanford.edu/~jos/filters/Definition_Signal.html

The Fourier transform of a signal is a frequency-domain function — Click for https://ccrma.stanford.edu/~jos/mdft/

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In addition, appendices are provided containing material that extends and supplements various chapters in various directions. Others provide supporting background material:

Organization

``Spectral Audio Signal Processing'', by Julius O. Smith III, W3K Publishing, 2011, ISBN 978-0-9745607-3-1. Copyright © 2022-02-28 by Julius O. Smith III Center for Computer Research in Music and Acoustics (CCRMA), Stanford University

``Spectral Audio Signal Processing'', by Julius O. Smith III, W3K Publishing, 2011, ISBN 978-0-9745607-3-1.
Copyright © 2022-02-28 by Julius O. Smith III
Center for Computer Research in Music and Acoustics (CCRMA), Stanford University