Index for this Document

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FIR filter design

optimal least-squares impulse response : 17.2

absolutely integrable : 3.2.1

acyclic convolution : 3.3.5

acyclic FFT convolution : 8.1.2

additive synthesis : 5 | 10.4 | 20 | 20.8 | 21.6

admissibility condition : 11.9.1.6

alias component matrix : 11.3.8

aliased sinc function : 5.5

aliasing components : 3.3.12

aliasing theorem for the DTFT : 3.3.12

aliasing, time domain : 8.1.4.3

allpass filter : 11.5.2

amplitude envelope : 10.4

analysis modulation matrix : 11.3.8

analytic signal : 5.1 | 17.5 | 17.6

analytic signal processing : 20.10.1

applications of the STFT : 10

asinc function : 5.5

associate peaks : 10.6.3

audio filter banks : 10.12

audio spectrogram : 7.3

audio spectrogram hop size : 7.3.2

auditory filter : 7.3.3.3

auditory filter bank : 7.3.3.2

auditory filter banks : 7.3.1

autocorrelation : 3.3.7

autocorrelation computation : 6.9

autocorrelation function : 15.2.3

autocorrelation method : 10.3.2.2 | 10.3.2.3

average : 15.1.8

bandlimited signals cannot be time limited : 14.1.17

bandpass filter : 17.5

Bark frequency scale : 18.5

Bark warping : 18.7

Bartlett window : 4.5

baseband signal : 9.1.2

basis signals : 11.9.1

bias : 5.7.2

bias of parabolic interpolation : 5.7.2

biased autocorrelation : 6

biased sample autocorrelation : 6.6

bilinear transform : 18.6

bilinear transform frequency warping : 18.2

bin number : 7.1.3

Blackman window : 4.3.3

Blackman window matlab example : 19.1.1

Blackman-Harris window : 4.3.6

Blackman-Harris window family : 4.3

Blackman-Harris window, frequency-domain implementation : 4.3.7

bounded variation : 14.2

breakpoints : 20.10.1.2

brown noise : 6.14

Burg's method : 10.3.2.2

cepstral windowing : 10.3.1

cepstrum : 17.8

channel vocoder : 20.5

characteristic function : 16.12.4

Chebyshev optimal windows : 4.13.2

Chebyshev polynomials : 4.10.4.1

Chebyshev window : 4.10

chirp signal : 9.2.1

chirp, Gaussian-windowed : 10.10

chirplet : 10.10

chirplet estimation : 10.10.2.1

chirplet modeling : 20.8.2

chirplets : 4.11 | 20.8.2

circular convolution : 8.1

coherent addition of signals : 6.15

COLA (constant overlap-add) : 7.1.1

COLA constraint : 8.2.1

COLA constraint, frequency domain : 8.3.2

COLA dual : 8.3

colored noise : 6.14

complex demodulation : 9.3.2

complex Gaussian integral : 16.7

compression : 11

confidence interval : 15.3.3

confidence level : 15.3.3

Conjugate Quadrature Filters : 11.3.7

constant overlap-add (COLA) : 21.1

constant overlap-add (COLA) property : 7.1.1

constant overlap-add property : 8

constant-overlap-add : 8.2.1

constant-Q filter banks : 10.12.1

constant-Q Fourier transform : 11.9.1.6

continuous probability distribution : 15.1.3

continuous wavelet transform : 11.9.1.6

continuous-time Fourier theorems : 3.4 | 14

convolution : 3.3.5 | 8.1

acyclic : 8.1.2

acyclic in matlab : 8.1.2.1

cyclic : 8.1.1

cyclic, or circular : 8.1

FFT overlap-add in matlab : 8.2.5

FFT, overlap-add : 8.2

in Matlab or Octave : 8.1.3

short signals : 8.1

convolution theorem : 3.3.5 | 3.3.5 | 14.1.7

convolution, continuous time : 14.1.7

correlation : 3.3.6

correlation analysis : 15.2

correlation theorem : 3.3.6 | 3.3.7

covariance : 6.4

covariance lattice methods : 10.3.2.2

covariance method : 10.3.2.2 | 10.3.2.2

critical band of hearing : 7.3.2

critical downsampling : 20.10.1.2

cross synthesis : 10.2

cross-correlation : 15.2.1

cross-power spectral density : 15.2.2 | 15.2.2

cubic polynomial phase interpolation : 10.6.1

cut-off frequency : 17.1

cycles per second : 14.1.1

cyclic autocorrelation : 6.8

cyclic convolution : 8.1

cyclic FFT convolution : 8.1.1

dc sampling filter : 9.3.1

decimation operator : 11.1.2

deconvolution : 8.1.2

delta function : 14.1.10

demos : 10.13

denoising : 6.1.1

deterministic : 5.8.2

deterministic part : 10.7.1

detrend : 6.9

DFT filter bank : 9.3 | 9.3.4.2

differentiation theorem : 14.1.2 | 14.2

differentiation theorem dual, DTFT : 3.3.13

differentiation theorem dual, FT : 14.1.3

digital filter design, FIR : 17

digital prolate spheroidal sequence (DPSS) : 4.8

Dirichlet function : 5.5

Discrete Prolate Spheroidal Sequences (DPSS) : 4.8

discrete time Fourier transform (DTFT) : 3.1

discrete wavelet filterbank : 11.9.1.8

discrete wavelet transform : 11.9.1.7

Dolph window : 4.10

Dolph-Chebyshev and Hamming windows compared : 4.10.3

Dolph-Chebyshev window : 4.10 | 4.10

Dolph-Chebyshev window length computation : 4.10.4.4

Dolph-Chebyshev window, theory : 4.10.4

downsampling : 3.3.12

downsampling (decimation) operator : 11.1.2

DPSS window : 4.8

DTFT

aliasing theorem : 3.3.12

convolution theorem : 3.3.5

correlation theorem : 3.3.6

downsampling theorem : 3.3.12

energy theorem : 3.3.8

even symmetry : 3.3.3.1

linearity : 3.3.1

power theorem : 3.3.8

repeat operator : 3.3.10

repeat theorem : 3.3.11

scaling operator : 3.3.10

scaling theorem : 3.3.11

shift theorem : 3.3.4

stretch operator : 3.3.9

stretch theorem : 3.3.11

symmetry : 3.3.3

time reversal : 3.3.2

DTFT Fourier theorems : 3.3

Durbin recursion : 10.3.2.3

dyadic filter bank : 10.12.1 | 11.9.1.9

dyadic wavelet filter bank : 11.9.1.9

effective length of a window : 5.6.3

energy theorem : 3.3.8

ensemble average : 15.1.6

entropy : 16.11.1 | 16.11.1

envelope break-points : 10.6.1

envelope follower : 7.3.3.6 | 20.10.1

equivalent rectangular bandwidth : 18.8

ergodic : 15.1.6

Estimator Variance : 15.3.3

excitation pattern : 7.3.1 | 7.3.2 | 7.3.3.2

expected value : 15.1.6 | 15.1.6 | 15.3

exponential window : 4.6

extended lapped transforms : 11.7.2

F0 estimation : 10.1

f0est detection in matlab : 19.6

FBS modifications : 9.8.2.1

FFT convolution speed : 8.1.4

FFT filter banks : 10.12

FFT input buffer : 21.2

fftshift utility in matlab : 3.5.4.1

filter

overlap-add FFT convolution : 8.2

filter bank summation interpretation of the STFT : 9

filter bank, perfect reconstruction : 11.3

filter banks : 11

paraunitary : 11.5

filter banks, FFT based : 10.12

filter design : 18

example of window method : 17.4.3

Hilbert transform filter : 17.5

least-squares, linear-phase FIR : 17.10.6

filter design, FIR

frequency-sampling method : 17.3

window method : 17.4

filter-bank interpretation of the STFT : 9.1.2

Filter-Bank Summation (FBS) : 9.3.4

filtered white noise : 6.14 | 6.14

filters

audio, FIR : 8.1.4.1

lossless : 11.5.2

lossless examples : 11.5.3

finite support : 6.6

finite-impulse-response : 17.4

FIR digital filter design

frequency-sampling method : 17.3

window method : 17.4

FIR filter design

by linear programming : 4.13

least-squares, linear phase : 17.10.6

optimal methods : 17.10

first-order moment : 16.12.1

flip operator : 14.1.8

floor function : 6.13

FM brass synthesis : 20.9.2

FM spectra : 20.9.1

FM synthesis : 20.9

FM voice synthesis : 20.9.3

formants : 7.2.1

Fourier dual : 3.5 | 9.5

Fourier theorems

continuous time : 3.4 | 14.1

discrete time : 3.3

DTFT

differentiation dual : 3.3.13

FT

differentiation dual : 14.1.3

Fourier theorems (continuous time)

convolution theorem : 14.1.7

differentiation : 14.1.2

flip theorem : 14.1.8

gaussian pulse : 14.1.11

impulse train : 14.1.14

modulation theorem : 14.1.6

power theorem : 14.1.9

rectangular pulse : 14.1.12

sampling theorem : 14.1.16

scaling or similarity : 14.1.4

shift theorem : 14.1.5

uncertainty principle : 14.1.17

Fourier transform : 3.2

Fourier transform existence : 3.2.1

Fourier transforms for continuous/discrete time/frequency : 3

frame : 7.1.3

frequency modulation : 20.9

frequency resolution : 5.5.2 | 5.6.2

frequency sampling for FIR filter design : 17.3

frequency scaling : 20.12.10

frequency shifting : 10.11

frequency trajectories : 10.6.3

frequency warping

allpass : 18

bilinear transform : 18.2

non-parametric : 19.5

frequency-scaling : 20.12.10

fundamental frequency estimation : 10.1

fundamental frequency estimation in matlab : 19.6

fundamental frequency estimation test program : 19.6.1

Gaussian chirp : 4.11

Gaussian distributed : 5.8.2

Gaussian distribution

maximum entropy property : 16.11

Gaussian function : 14.1.17.1 | 16

Gaussian integral : 16.6.1

Gaussian moments : 16.12

gaussian pulse : 14.1.11

Gaussian random variable, closed under addition : 16.13

Gaussian window : 16.1

Gaussian window function : 4.11

Gaussian, Fourier transform of : 16.8

Gaussian-windowed chirp : 10.10

Gaussians Closed under Multiplication : 16.2

generalized function : 14.1.10

generalized Hamming window family : 4.2 | 4.2.5

generalized STFT : 11.9.1.11

geometric signal theory : 11.9.1

Gibbs phenomenon : 5.5.1

glossary of notation : 13

graphic equalizer : 17.7

graphical convolution : 8.1

graphical equalizers : 8.3.3

group-additive synthesis : 20.8.4.2

Haar filter bank : 11.3.3

Hamming and Dolph-Chebyshev windows compared : 4.10.3

Hamming window : 4.2.3

Hammond organ : 20.4

Hann window : 4.2.1 | 4.2.1

Hann-Poisson window : 4.7

hanning window : 4.2.1

harmonic : 5.6.3

Heisenberg uncertainty principle : 14.1.17.1

Hermitian : 3.3.3

Hermitian spectrum : 17.5

heterodyne-comb : 20.12.1

Hilbert space : 11.9.1

Hilbert transform : 17.6

Hilbert transform filter design : 17.5

Hilbert transform kernel : 17.6

history of spectral modeling : 20

hop size : 6.12 | 7.1.3 | 8.2.1

ideal lowpass filter : 17.4

identity system : 20.10.1.3

impulse train : 14.1.14

impulse, continuous time : 14.1.10

impulse, sinc : 14.1.13

independent events : 15.1.2 | 15.3.1

independent random variables : 15.3.1

inner product : 3.3.8 | 14.1.9

innovations sequence : 10.3.2

instantaneous amplitude : 20.10.1

instantaneous loudness : 7.3.2 | 7.3.3.6

instantaneous phase : 20.10.1

interpolation kernel : 3.5.2 | 7.3.3.3

interpolation kernel, spectral, ideal : 19.5.1

interpolation of a DFT : 3.5.2

inverse FFT synthesis : 20.8.1

inverse filter : 10.3.2

inverse-FFT synthesis : 20.12.3

Kaiser window : 4.9

Kaiser window beta parameter : 4.9.1

Kaiser-Bessel window : 4.9

lagged product : 6.4

Laurent expansion : 17.9 | 17.9

least squares estimation : 5.8.1

least squares sinusoidal parameter estimation : 5.8.1

likelihood function : 5.8.3

linear least squares : 5.8.1.1

linear phase : 8.1.4.2

linear phase term : 3.3.4

linear prediction

autocorrelation method : 10.3.2.2

covariance method : 10.3.2.2

linear prediction peak sensitivity : 10.3.2.1

linear prediction spectral envelope : 10.3.2

linearity of the DTFT : 3.3.1

long-term loudness : 7.3.3.6

lossless filter : 11.5.2

lossless filter examples : 11.5.3

lossless filters : 11.5.2

lossless transfer function matrix : 11.5.2

loudness : 7.3 | 7.3.1

loudness perception : 18.11

loudness spectrogram : 7.3.2 | 7.3.2

loudness spectrogram, examples : 7.3.3

loudness versus time : 7.3.3.6

loudness versus time and frequency : 7.3.2

low-pass filtering by FFT : 8.1.4.2

lowpass filter, ideal : 17.1

LPC : 10.3.3.4

magnitude-only analysis/synthesis : 21.7

main lobe width : 5.6.1

main-lobe bandwidth : 5.6

main-lobe width : 5.6

masking : 10.1.1

matlab

bandlimited impulse train : 10.3.3.1

cepstrum : 10.3.3

discrete prolate spheroidal window : 19.1.2

DPSS window : 4.8.1

frequency warping : 19.5

fundamental frequency estimation : 19.6

linear prediction : 10.3.3

minimum zero-padding factor : 19.2.4

peak finder : 19.2

phase unwrapping : 19.4 | 19.4.1

spectral envelopes : 10.3.3

spectral peak-finding : 19.2.1

spectrogram : 19.3

spectrum analysis windows : 19.1

window method for FIR filter design : 17.4.1

matlab examples : 19

matlab listing

dpssw : 19.1.2

f0est : 19.6

findpeaks : 19.2.1

maxr : 19.2.2

myspectrogram : 19.3.1

npwarp : 19.5

oboeanal : 19.2.5

qint : 19.2.3

testmyspectrogram : 19.3.2 | 19.3.3

unwrap : 19.4.1

zero-phase blackman : 19.1.1

zpfmin : 19.2.4

maximum likelihood estimator : 5.8.2

maximum likelihood sinusoidal parameter estimation : 5.8.2

mean of a distribution : 16.12.1

mean of a random process : 15.1.7

minimum phase : 17.8

minimum phase filters : 17.8

minimum phase means a causal cepstrum : 17.9

modulated lapped transform : 4.2.6

modulation theorem : 10.10.1 | 14.1.6

Morlet wavelet : 11.9.1.6

mother wavelet : 11.9.1.6

MPEG filter banks : 11.7

mu-law amplitude compression : 18.11

multi-resolution STFT : 7.3.2

multirate filter banks : 11

multirate noble identities : 11.2.5

multiresolution sinusoidal modeling : 20.12.5

multiresolution STFT : 7.3.3.1 | 7.3.3.1

munchkinization : 10.11

myspectrogram : 19.3.1

natural basis : 11.9.1.1

noble identities : 11.2.5

noise : 6.1.2

mean : 15.1.7

synthesis example : 6.14.2

white : 15.3

noise process : 15.1.4

noise spectral analysis

periodogram : 6.11

Welch's method : 6.12

noise spectrum analysis : 6

pink noise example : 6.14.3

noise, filtered : 6.14

non-coherent addition of signals : 6.15

nonparametric method : 10.3

nonparvocoder : 20.8.3

nonuniform resampling : 7.3.3.3

normal distribution : 5.8.2

normal equations : 10.3.2.3

Normalized Discrete Fourier Transform (NDFT) : 11.9.1.2

normalized frequency : 3.1

normalized radian frequency : 5.2

notation glossary : 13

oboe spectrum analysis : 4.4

octave filter bank : 10.12.1 | 11.9.1.9

oddly-stacked Princen-Bradley filter bank : 11.7.2

OLA modifications : 9.8.2.1

optimized windows : 4.12

orthogonal two-channel filter banks : 11.3.8

orthogonality principle : 5.8.1.2

orthonormal : 11.9.1

overcomplete basis : 11.9.1.5

overlap-add convolution in matlab : 8.2.5

overlap-add decomposition : 8.2.1

overlap-add FFT convolution : 8.2

overlap-add FFT processor : 8

overlap-add interpretation of the STFT : 8 | 9.1.1

overlap-add method : 7.1.4

overlap-add, with modifications : 8.5

overtone : 10.4

panning : 6.16

paraconjugate : 11.3.8

paraconjugation : 11.5.1

parametric method : 10.3

paraunitary filter bank : 11.5.5

paraunitary filter banks : 11.5

Parseval's theorem : 3.3.8

partial overtone : 10.4

partition of unity property : 8.2.1

PDF : 15.1.3

peak detection : 21.3

peak matching : 21.4

peak-finding : 5.8

peak-finding in matlab : 19.2.1

perceptual audio coding : 20.13

perfect reconstruction : 9.1.3

perfect reconstruction filter bank, conditions for : 11.4.5

perfect reconstruction filter banks : 11.4

perfect reconstruction filter banks, critically sampled : 11.3

periodic sinc function : 5.5

periodogram : 6.11

periodogram method : 6.12 | 6.12

periodogram method for power spectrum estimation : 6.12

phase modulation : 20.9 | 20.10

phase unwrapping : 19.4.1

phase vocoder : 20.7

FFT implementation : 20.7.1

phase vocoder sinusoidal modeling : 20.10

phasiness : 10.11.2

phons : 7.3.3.6

piecewise linear approximation : 20.10.1.2

pink noise : 6.14 | 6.14.2

pitch detection : 10.1 | 10.1

Poisson summation formula : 8.3.1

Poisson summation formula, continuous time : 14.1.15

Poisson window : 4.6

polyphase component filters : 11.2.1

polyphase components : 11.2

polyphase decomposition : 11.1.3 | 11.2.1 | 11.2.2

polyphase filter bank : 11.1.3

polyphase matrix : 11.4

polyphase signals : 11.1.3

Portnoff window : 9.7

power spectral density : 15.2.5

smoothed : 6.7

power spectrum : 15.2.5

power theorem : 3.3.8 | 14.1.9

pre-emphasis : 21.8

prediction coefficients : 10.3.2

prediction error : 10.3.2

preemphasis : 4.4.4 | 10.1.1

preprocessing : 10.1.1

probability density function : 15.1.3

probability distribution : 15.1.1 | 15.1.1

processing gain : 6.15

prolate spheroidal wave function : 4.8

prolate spheroidal window : 4.8

Pseudo-QMF filter bank : 11.7.1

quadratic interpolation : 5.7

quadratically interpolated FFT (QIFFT) method : 5.7

quadrature mirror filters (QMF) : 11.3.5

quasi octave filter bank : 10.12.4.4

radians per second : 14.1.1

raised-cosine window : 4.2.1

random process : 15.1.4

random variable : 15.1.3 | 15.1.3

random variables : 15.1

Rayleigh's energy theorem : 3.3.8

rectangular pulse : 14.1.12

rectangular window : 4.1 | 5.3 | 5.5

rectangular window side-lobes : 5.5.1

Remez multiple exchange algorithm : 17.4.3.4

repeat operator : 3.3.10

repeat theorem : 3.3.11

residual signal : 10.7.1

resolution of frequencies : 5.6.2

resolution window length : 5.6.2

resolving sinusoids : 5.6

rheotomes : 20.2

Riemann Lemma : 3.4.2 | 14.2

roll-off rate : 14.2

running-sum lowpass filter : 9.3.1

sample autocorrelation : 6 | 6.4

sample autocorrelation function : 6.9

sample mean : 15.1.8

sample mean of a random process : 15.1.8

sample power spectral density : 6.5

sample PSD : 6

sample variance : 6.4 | 15.1.10 | 15.1.10

sampled rectangular pulse : 14.1.14

sampling synthesis : 20.8.4.1

sampling theory : 14.1.16

scale parameter : 11.9.1.6

scaling theorem : 14.1.4

scalogram : 11.9.1.6

second central moment : 15.1.9 | 16.12.2

second moments of a signal : 14.1.17.1

shah symbol : 14.1.14

shift operator : 3.3.4

shift theorem : 3.3.4 | 3.3.4 | 14.1.5

short time Fourier transform : 7

downsampling : 9.8

modifications : 9.9

short-term loudness : 7.3.3.6

short-time Fourier transform (STFT) : 7.1

side-lobe width : 5.6

sifting property : 5.1 | 14.1.10

signal model : 5.8.1

similarity theorem : 14.1.4

sinc function : 5.5 | 17.4

sinc function, aliased (periodic) : 5.5

sine window : 4.2.6 | 4.2.6

sines + noise + transients model : 10.9

sines + noise spectral modeling : 10.7

sines+noise synthesis : 20.12.4

sines+noise+transients : 10.4

sinusoidal amplitude estimation : 5.8.1.1

sinusoidal model

frequency scaling : 20.12.10

time-scale modification : 20.12.10

sinusoidal modeling : 10.4 | 10.4 | 20

sinusoidal modeling history : 20.12.2

Sinusoidal Modeling Software (PARSHL) : 21

sinusoidal parameter estimation

general case : 5.8.1.3

known frequency : 5.8.1.2

known frequency and phase : 5.8.1.1

least squares : 5.8.1

sinusoidal spectrum analysis : 5

Slepian window : 4.8 | 4.8

sliding DFT : 9.3.4.2

sliding FFT : 20.10.1.1

sone amplitude scale : 18.11

sones : 7.3.3.6

source-filter decomposition : 10.3.2.5

specific loudness : 7.3.1 | 7.3.2 | 7.3.3.4

spectral display : 7.1

spectral envelope : 10.3

cepstral windowing : 10.3.1

cepstral windowing method : 10.3.3.2

linear prediction : 10.3.2

linear prediction method : 10.3.3.3

spectral envelope examples : 10.3.3

spectral interpolation : 3.5

spectral interpolation, ideal : 3.5.1 | 19.5.1

spectral modeling : 10.4 | 20

history : 20.12 | 20.12

spectral modeling applications : 21.9

spectral modeling overview : 2

spectral modeling synthesis : 10.4

spectral modeling synthesis (SMS) : 20.12

spectral modifications : 8

spectral resolution : 5.6

spectral transformations : 21.5

spectrogram : 7.2

spectrogram parameters : 7.2

spectrogram, for audio display : 7.3

spectrum : 5.1

spectrum analysis : 4

noise : 6

oboe data : 4.4

sinusoids or spectral peaks : 5

statistical formulation : 15

time varying : 7

speech spectrogram : 7.2.1

speech synthesis examples : 20.11

square integrable : 3.2.1

stationary : 6.1.1 | 15.1.6

stationary stochastic process : 15.1.5

statistical signal processing : 15

step size : 7.1.3

stereo panning : 6.16

STFT : see short-time Fourier transformtextbf

filter-bank interpretation : 9.1.2

overlap-add interpretation : 9.1.1

weighted overlap-add : 8.6

STFT filter bank, downsampled : 9.8.1

stochastic part : 10.7.1

stochastic process : 6 | 15.1.4

stop-band attenuation : 17.4.3.3

stretch operator : 3.3.9 | 3.3.9 | 11.1.1

stretch theorem : 3.3.11

strong COLA constraint : 8.3.2.1 | 8.3.2.1

subtractive synthesis : 10.7

symmetric Toeplitz operator : 4.8

symmetry of the DTFT for real signals : 3.3.3

Telharmonium : 20.2

third-octave filter bank : 7.3.1 | 10.12.1

time compression/expansion : 10.11

time domain aliasing : 8.1.2.2

time limited : 17.4

time normalized : 7.1.3

time reversal and the DTFT : 3.3.2

time scale modification : 10.8.3 | 10.11

time-bandwidth product : 14.1.17.3

time-domain aliasing : 8.1.4.3

time-frequency displays : 7

time-frequency distributions : 7.1

time-frequency reassignment : 20.12.8

time-limited interpolation : 3.5.2

time-limited signals : 14.1.17.2

time-scale modification : 20.12.10 | 20.12.10

time-varying OLA modifications : 8.5

Toeplitz matrix : 10.3.2.3

tone wheels : 20.2

total variation : 14.2

transform coders : 7.1.4

transient detector : 10.8.2

transpose, filter bank : 11.3.4 | 11.4.7

triangular window : 4.5

twiddle factor : 11.1.2

two-sided Taylor expansion : 17.9

type II polyphase decomposition : 11.2.3

unbiased estimator : 15.1.8 | 15.1.10

uncertainty principle : 14.1.17

unimodular polynomial matrix : 11.5.5

unwrapping phase : 19.4.1

upsampling (stretch) operator : 11.1.1

variance : 15.1.9 | 15.1.9

variance of a distribution : 16.12.2

vocoder : 20.5

Voder : 20.6

wavelet coefficient : 11.9.1.6

wavelet filter banks : 11.9

wavetable synthesis : 20.8.4.1

weak COLA constraint : 8.3.2

weighted overlap add : 8.6

weighted overlap-add : 8.6

Welch autocorrelation : 6.12.1 | 6.12.2

Welch's method for spectrum analysis : 6.12

Welch's method, windowed : 6.13

white noise : 6.1.1 | 6.1.2 | 6.3 | 6.3.1 | 6.4 | 6.4 | 6.5 | 6.5 | 6.7 | 6.10 | 6.11 | 6.11.1 | 6.14 | 6.14 | 6.14 | 6.14.2 | 15.3

whitening filter : 10.3.2

Wiener-Hopf equations : 10.3.2.3

window function : 4

window length, minimum for resolving sinusoids : 5.6.4

window method filter design demo : 17.4.2

window method, FIR filter design : 17.4 | 17.7

windowing effect : 5.4

windows

Bartlett : 4.5

Blackman : 4.3.3 | 19.1.1

Chebyshev : 4.10

Dolph-Chebyshev : 4.10

Dolph-Chebyshev theory : 4.10.4

DPSS : 4.8

exponential : 4.6

frequency resolution : 4.9.3

generalized Hamming : 4.2 | 4.2.5

Hann-Poisson : 4.7

Kaiser : 4.9

Kaiser-Bessel : 4.9

no side-lobes case : 4.7

optimized : 4.12

Poisson : 4.6

Prolate Spheroidal : 4.8

rectangular : 4.1 | 5.5

sine : 4.2.6

Slepian : 4.8

triangular : 4.5

windows for spectrum analysis : 4

windows, resolution bandwidth : 5.6

Yule-Walker equations : 10.3.2.3

zero padding : 3.5.3

zero padding, minimum : 19.2.4

zero padding, zero-phase form : 3.5.4

zero-centered : 5.3

zero-padding factor : 7.1.3

zero-phase windows : 5.5

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About the Author: Julius Orion Smith III
Julius Smith's background is in electrical engineering (BS Rice 1975, PhD Stanford 1983). He is presently Professor of Music and Associate Professor (by courtesy) of Electrical Engineering at Stanford's Center for Computer Research in Music and Acoustics (CCRMA), teaching courses and pursuing research related to signal processing applied to music and audio systems. See http://ccrma.stanford.edu/~jos/ for details.