Index

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 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 : 16.2.3

autocorrelation method : 10.3.2.2 | 10.3.2.3

average : 16.1.8

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 : 5.8.2

bias of parabolic interpolation : 5.8.2

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 : 15.12.4

Chebyshev polynomials : 4.10.4.1

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 : 15.7

compression : 11

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 Fourier transform : 11.9.1.6

continuous probability distribution : 16.1.3

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 : 16.2

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 : 16.2.1

cross-power spectral density : 16.2.2 | 16.2.2

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 : 10.12

denoising : 6.1.1

deterministic : 5.9.2

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 probability distribution : 15.10

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 : 11.9.1.9

dyadic wavelet filter bank : 11.9.1.9

effective length of a window : 5.7.1

energy theorem : 3.3.8

ensemble average : 16.1.6

entropy : 15.11.1 | 15.11.1

envelope break-points : 10.6.1

envelope follower : 7.3.3.6 | 20.10.1

equivalent rectangular bandwidth : 18.8

excitation pattern : 7.3.1 | 7.3.2 | 7.3.3.2

expected value : 16.1.6 | 16.1.6 | 16.3

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 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 design : 18

example of window method : 17.4.2

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 : 15.12.1

flip operator : 14.1.8

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 | 5.7

frequency sampling for FIR filter design : 17.3

frequency shifting : 10.11 | 20.12.10

frequency trajectories : 10.6.3

frequency warping

allpass : 18

bilinear transform : 18.2

non-parametric : 19.5

frequency-shifting : 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 : 5.9.2

Gaussian distribution

maximum entropy property : 15.11

Gaussian function : 14.1.17.1 | 15

Gaussian integral : 15.6.1

gaussian pulse : 14.1.11

Gaussian random variable, closed under addition : 15.13

Gaussian window : 15.1

Gaussian window function : 4.11

Gaussian, Fourier transform of : 15.8

Gaussian-windowed chirp : 10.10

generalized function : 14.1.10

generalized Hamming window family : 4.2 | 4.2.6

generalized STFT : 11.9.1.10

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 : 4.2.4

Hammond organ : 20.4

Hann window : 4.2.1 | 4.2.1

Hann-Poisson window : 4.7

hanning window : 4.2.1

harmonic : 5.7.1

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 : 16.1.2 | 16.3.1

independent random variables : 16.3.1

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 : 4.9.3

Kaiser-Bessel window : 4.9

lagged product : 6.4

Laurent expansion : 17.9 | 17.9

least squares estimation : 5.9.1

least squares sinusoidal parameter estimation : 5.9.1

likelihood function : 5.9.3

linear least squares : 5.9.1.1

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 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

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

finding spectral peaks : 19.2.2

frequency warping : 19.5

fundamental frequency estimation : 19.6

linear prediction : 10.3.3

minimum zero-padding factor : 19.2.5

peak finder : 19.2

phase unwrapping : 19.4 | 19.4.1

spectral envelopes : 10.3.3

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 | 19.2.2

maxr : 19.2.3

myspectrogram : 19.3.1

npwarp : 19.5

oboeanal : 19.2.6

qint : 19.2.4

testmyspectrogram : 19.3.2 | 19.3.3

unwrap : 19.4.1

zero-phase blackman : 19.1.1

zpfmin : 19.2.5

maximum likelihood estimator : 5.9.2

maximum likelihood sinusoidal parameter estimation : 5.9.2

mean of a distribution : 15.12.1

mean of a random process : 16.1.7

minimum phase : 17.8

minimum phase filters : 17.8

minimum phase means a causal cepstrum : 17.9

modulated lapped transform : 4.2.13

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

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 : 16.1.7

synthesis example : 6.14.2

white : 16.3

noise process : 16.1.4

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 : 5.9.2

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 : 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 : 5.9.1.2

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 : 16.1.3

peak detection : 21.3

peak matching : 21.4

peak-finding : 5.9

peak-finding in matlab : 19.2.2

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

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 : 16.2.5

smoothed : 6.7

power spectrum : 16.2.5

power theorem : 3.3.8 | 14.1.9

pre-emphasis : 21.8

prediction coefficients : 10.3.2

prediction error : 10.3.2

preemphasis : 10.1.1

preprocessing : 10.1.1

probability density function : 16.1.3

probability distribution : 15.10 | 16.1.1 | 16.1.1

processing gain : 6.15

prolate spheroidal wave function : 4.8

prolate spheroidal window : 4.8

Pseudo-QMF filter bank : 11.7.1

quadratic interpolation : 5.8

quadratically interpolated FFT (QIFFT) method : 5.8

quadrature mirror filters (QMF) : 11.3.5

radians per second : 14.1.1

raised-cosine window : 4.2.1

random process : 16.1.4

random variable : 16.1.3

random variables : 16.1

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 : 17.4.2.4

repeat operator : 3.3.10

repeat theorem : 3.3.11

residual signal : 10.7.1

resolution bandwidth : 5.6

resolution of frequencies : 5.7

resolution window length : 5.7

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 : 16.1.8

sample mean of a random process : 16.1.8

sample power spectral density : 6.5

sample PSD : 6

sample variance : 6.4 | 16.1.10 | 16.1.10

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 : 15.12.2 | 16.1.9

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 : 5.9.1

similarity theorem : 14.1.4

sinc function : 5.5 | 17.4

sinc function, aliased (periodic) : 5.5

sine window : 4.2.13 | 4.2.13

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 : 5.9.1.1

sinusoidal model

frequency shifting : 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 : 5.9.1.3

known frequency : 5.9.1.2

known frequency and phase : 5.9.1.1

least squares : 5.9.1

sinusoidal spectrum analysis : 5

Slepian window : 4.8 | 4.8

sliding DFT : 9.3.4.2

sliding FFT : 20.10.1.1

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 | 8.2

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 : 16

time varying : 7

speech spectrogram : 7.2.1

speech synthesis examples : 20.11

square integrable : 3.2.1

stationary : 6.1.1 | 16.1.6

stationary stochastic process : 16.1.5

statistical signal processing : 16

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 : 8.7

STFT filter bank, downsampled : 9.8.1

stochastic part : 10.7.1

stochastic process : 6 | 16.1.4

stop-band attenuation : 17.4.2.3

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

time compression/expansion : 10.11

time domain aliasing : 8.1.2.2

time limited : 17.4

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 : 16.1.8 | 16.1.10

uncertainty principle : 14.1.17

unimodular polynomial matrix : 11.5.5

unwrapping phase : 19.4.1

upsampling (stretch) operator : 11.1.1

variance : 16.1.9 | 16.1.9

variance of a distribution : 15.12.2

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 : 8.7

weighted overlap-add : 8.7

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 | 16.3

whitening filter : 10.3.2

Wiener-Hopf equations : 10.3.2.3

window function : 4

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 : 4.9.5

generalized Hamming : 4.2 | 4.2.6

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 : 4.2.13

Slepian : 4.8

triangular : 4.5

windows for spectrum analysis : 4

Yule-Walker equations : 10.3.2.3

zero padding : 3.5.3

zero padding, minimum : 19.2.5

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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