Index for this Document

filter design

optimal least-squares impulse response : 5.3

absolutely integrable : 3.2.1

acyclic convolution : 3.3.5

acyclic FFT convolution : 9.1.2

additive synthesis : 6 | 11.4.1 | 20 | 20.8 | 21.6

admissibility condition, wavelets : 12.9.1.6

alias component matrix : 12.3.8

aliased sinc function : 4.1

aliasing cancellation : 12.3

aliasing components : 3.3.12

aliasing theorem for the DTFT : 3.3.12

aliasing, time domain : 9.1.4.3

allpass filter : 12.5.1

amplitude envelope : 11.4 | 20.10.1

analysis modulation matrix : 12.3.8

analytic signal : 5.6 | 5.6.1.1 | 6.1 | 20.10.1

applications of the STFT : 11

asinc function : 4.1

audio

filter banks : 11.7

spectrogram : 8.3

spectrogram hop size : 8.3.2.1

auditory filter bank : 8.3.1

auditory filter shape : 8.3.3.4

autocorrelation : 3.3.7

autocorrelation computation : 7.9

autocorrelation function : 16.2.3

autocorrelation method of linear prediction : 11.3.2.2

bandpass filter : 5.6

Bark frequency scale : 18.1

Bark warping : 18.3

Bartlett window : 4.5

baseband signal : 10.1.2

basis signals : 12.9.1

bias : 6.6.2

biased autocorrelation : 7

biased sample autocorrelation : 7.6

bilinear transform : 18.2

bin number : 8.1.3

Blackman window : 4.3.1

Blackman window matlab example : 19.1.1

Blackman-Harris window : 4.3.4

family : 4.3

bounded variation : 15.18

breakpoints : 20.10.1.2

brown noise : 7.14

Burg's method : 11.3.2.2

central limit theorem : 17.9.1

cepstral windowing : 11.3.1

cepstrum : 5.8

cepstrum, causal : 5.9

channel vocoder : 20.5

characteristic function : 17.12.4

Chebyshev bandpass filter design : 5.5.2.2

Chebyshev FIR filters : 5.10.2

Chebyshev optimal windows : 4.13.2

Chebyshev polynomials : 4.10.4.1

Chebyshev window : 4.10

by linear programming : 4.13

chirp signal : 10.2.1 | 11.6

chirp, Gaussian-windowed : 11.6

chirplet : 11.6 | 11.6

chirplet frequency estimation : 11.6.3.1

chirplet signal modeling : 20.8.2

chirplet, spectrum : 11.6.1

circular convolution : 9.1

coherent addition of signals : 7.15

COLA (constant overlap-add) : 8.1.1

COLA constraint : 9.2.1

COLA constraint, frequency domain : 9.3.2

COLA dual : 9.3

colored noise : 7.14

complex demodulation : 10.3.2

complex signal modulation : 10.3.2

compression : 12

confidence interval : 16.3.3

confidence level : 16.3.3

conjugate quadrature filters : 12.3.7

constant overlap-add : 21.1

constant overlap-add window : 8.1.1 | 9

constant-overlap-add : 9.2.1

constant-Q filter banks : 11.7.1

constant-Q Fourier transform : 12.9.1.6

continuous Fourier theorems : 3.4 | 15

continuous wavelet transform : 12.9.1.6

convolution : 3.3.5 | 9.1

acyclic : 9.1.2

acyclic in matlab : 9.1.2.1

continuous time : 15.7

cyclic : 9.1.1

cyclic, or circular : 9.1

FFT overlap-add in matlab : 9.2.5

FFT, overlap-add : 9.2

in matlab : 9.1.3

short signals : 9.1

convolution theorem : 3.3.5 | 3.3.5 | 15.7

correlation : 3.3.6

correlation analysis : 16.2

correlation theorem : 3.3.6 | 3.3.7

covariance : 7.4

covariance lattice methods : 11.3.2.2

covariance method, linear prediction : 11.3.2.2

critical band of hearing : 8.3.2

critical downsampling : 20.10.1.2

cross-correlation : 16.2.1

cross-power spectral density : 16.2.2 | 16.2.2

cross-synthesis : 11.2

cubic phase interpolation : 11.4.2.1

cubic splines : 5.7

cut-off frequency : 5.1

cycles per second : 15.1

cyclic autocorrelation : 7.8

cyclic convolution : 9.1

cyclic FFT convolution : 9.1.1

dc sampling filter : 10.3.1

decimation operator : 12.1.2

deconvolution : 9.1.2

delta function : 15.10

demodulation, complex : 10.3.2

demos : 11.9

denoising : 7.1.1

deterministic : 6.7.2

deterministic part : 11.4.3.2

detrend : 7.9

DFT filter bank : 10.3 | 10.3.4.2

differentiation theorem : 15.2 | 15.18

differentiation theorem dual, DTFT : 3.3.13

differentiation theorem dual, FT : 15.3

digital filter design : see filter designtextbf

digital prolate spheroidal sequence : 4.8

window : 4.8

Dirichlet function : 4.1

discrete time Fourier transform (DTFT) : 3.1

discrete wavelet filterbank : 12.9.1.8 | 12.9.1.8

discrete wavelet transform : 12.9.1.7

discrete-time Fourier transform : see DTFTtextbf

Dolph window : 4.10

Dolph-Chebyshev window : 4.10

comparison to Hamming : 4.10.3

definition : 4.10.4.2

length computation : 4.10.4.4

main-lobe width : 4.10.4.3

theory : 4.10.4

double-factorial : 17.12.3

downsampling : 3.3.12

downsampling (decimation) operator : 12.1.2

DPSS : see digital prolate spheroidal sequencetextbf

DPSS window : 4.8

DTFT definition : 3.3

DTFT Fourier theorems : 3.3

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 : 3.3.3.2

linearity : 3.3.1

power theorem : 3.3.8

real signals : 3.3.3.1

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

duality, Fourier : 9.3 | 10.5

Durbin recursion : 11.3.2.3

dyadic filter bank : 11.7.1 | 12.9.1.9

dyadic wavelet filter bank : 12.9.1.9

effective length of a window : 6.5.3

energy theorem : 3.3.8

ensemble average : 16.1.6

entropy : 17.11.1 | 17.11.1

envelope break-points : 11.4.2.1

envelope follower : 8.3.3.7 | 20.10.1

equation error : 18.3.1

equiripple : 5.3.1

equivalent rectangular bandwidth : 18.5

ergodic : 16.1.6

estimator variance : 16.3.3

excitation pattern : 8.3.1 | 8.3.2 | 8.3.3.2

expected value : 16.1.6 | 16.1.6 | 16.3

exponential polynomial signal : 11.6

exponential window : 4.6

extended lapped transforms : 12.7.2

extremal frequencies : 5.10.3

F0 : see fundamental frequencytextbf

F0 estimation : 11.1

fast Fourier transform : see FFTtextbf

FBS : see filter-bank summationtextbf

FFT

convolution : 5

convolution speed : 9.1.4

filter banks : 11.7

input buffer : 21.2

fftshift utility in matlab : 3.5.4.1

filter

audio, FIR : 9.1.4.1

lossless : 12.5.1

lossless examples : 12.5.2

overlap-add FFT convolution : 9.2

filter bank

auditory : 8.3.1

DFT : 10.3

discrete wavelet : 12.9.1.8

FFT based : 11.7

Haar : 12.3.3

multirate : 12

paraunitary : 12.5

perfect reconstruction : 12.3

polyphase : 12.1.3

Princen-Bradley : 12.7.2

pseudo-QMF : 12.7.1

wavelet : 12.9

filter design : 5

frequency-sampling method : 5.4 | 5.6.2.3

Hilbert transform filter : 5.6 | 5.6.2

least-squares, linear-phase : 5.10.3

linear programming : 4.13

nonlinear-phase : 5.10.6

nonparametric : 5.6.3

optimal methods : 5.10

specifications : 5.2

window method : 5.5

window method example : 5.5.2

filter-bank interpretation of STFT : 10

filtered white noise : 7.14 | 7.14

finite support : 7.6

FIR (finite impulse response) filter : 5.5

FIR filter design : see filter designtextbf

first-order moment : 17.12.1

flip operator : 15.8

floor function : 7.13

FM : see frequency modulationtextbf

formants : 8.2.1

Fourier dual : 3.5 | 10.5

Fourier theorems

continuous time : 3.4 | 15

discrete time : 3.3

DTFT : see DTFT Fourier theoremstextbf

differentiation dual : 3.3.13

FT

differentiation dual : 15.3

Fourier theorems (continuous time)

convolution theorem : 15.7

differentiation : 15.2

flip theorem : 15.8

Gaussian pulse : 15.11

impulse train : 15.14

modulation theorem : 15.6

power theorem : 15.9

rectangular pulse : 15.12

sampling theorem : 15.16

scaling or similarity : 15.4

shift theorem : 15.5

uncertainty principle : 15.17

Fourier transform

continuous/discrete : 3

definition : 3.2

existence : 3.2.1

inverse : 3.2

frame (of data) : 8.1.2

frequency envelopes : 20.10

frequency modulation : 20.9

brass synthesis : 20.9.2

modulation index : 20.9.1

operator : 20.9.3

spectra : 20.9.1

synthesis : 20.9

voice synthesis : 20.9.3

frequency resolution : 6.4.1 | 6.5.2

frequency sampling method for FIR digital filter design : 5.4

frequency scaling : 11.5 | 11.5

frequency trajectories : 11.4.2.3

frequency warping

allpass : 18

nonparametric : 19.5

fundamental frequency estimation : 11.1

in matlab : 19.6

test program : 19.6.1

Gaussian : 17

characteristic function : 17.12.5

closure under convolution : 17.3

closure under multiplication : 17.2

complex integral : 17.7

distribution : 17.11.3.3

Fourier transform : 17.8

integral : 17.6.1

maximum entropy property : 17.11

moments : 17.12

probability density : 17.10

pulse : 15.11

random variable, closed under addition : 17.13

window : 4.11 | 17.1

window transform : 4.11.2

Gaussian-windowed chirp : 11.6

generalized function : 15.10

generalized Hamming window : 4.2

generalized Hamming window family : 4.2.5

generalized STFT : 12.9.1.11

geometric signal theory : 12.9.1

Gibbs phenomenon : 4.1.1

glossary of notation : 14

graphic equalizer : 5.7 | 9.3.3

graphical convolution : 9.1

group-additive synthesis : 20.8.4.2

Haar filter bank : 12.3.3

Hamming window : 4.2.3

comparison to Chebyshev : 4.10.3

Hammond organ : 20.4

Hann window : 4.2.1 | 4.2.1

Hann-Poisson window : 4.7

hanning window : 4.2.1

harmonic : 6.5.3

harmonic comb : 11.1.2

Heisenberg uncertainty principle : 15.17.1

Hermitian : 3.3.3.1

Hermitian spectrum : 5.6

heterodyne-comb : 20.11.1

Hilbert space : 12.9.1

Hilbert transform : 5.6.1

Hilbert transform filter design : 5.6

Hilbert transform kernel : 5.6.1.1 | 5.6.1.1

history of spectral modeling : 20

hop size : 7.12 | 8.3.2.1 | 9.2.1

hop size (STFT) : 8.1.3

ideal lowpass filter : 5.5

identity system : 20.10.1.3

impulse train : 15.14

impulse, continuous time : 15.10

impulse, sinc : 15.13

independent events : 16.1.2 | 16.3.1

independent random variables : 16.3.1

inner product : 3.3.8 | 15.9

innovations sequence : 11.3.2

instantaneous amplitude : 20.10.1

instantaneous frequency : 20.10.1

instantaneous loudness : 8.3.2

instantaneous phase : 20.10.1

interpolation

bandlimited : 3.3.12

cubic phase : 11.4.2.1

DFT bins : 3.5.2

spectral : 3.5.1

time-limited : 3.5.2

interpolation kernel : 3.5.2 | 8.3.3.3

inverse filter : 11.3.2

inverse-FFT synthesis : 20.8.1 | 20.11.3

Kaiser window : 4.9

beta parameter : 4.9.1

Kaiser-Bessel window : 4.9

lagged product : 7.4

Laurent expansion : 5.9 | 5.9

least squares estimation : 6.7.1

sinusoid parameters : 6.7.1

likelihood function : 6.7.3

linear inequality constraints : 5.10.4

linear least squares : 6.7.1.1

linear objective : 5.10.4

linear phase : 9.1.4.2

linear phase term : 3.3.4

linear prediction

autocorrelation method : 11.3.2.2

covariance method : 11.3.2.2

peak sensitivity : 11.3.2.1

spectral envelope : 11.3.2

linear programming : 4.13 | 4.13.1 | 5.10.4

linearity of the DTFT : 3.3.1

lossless filter : 12.5.1

lossless filter examples : 12.5.2

lossless transfer function matrix : 12.5.1

loudness : 8.3 | 8.3.1

instantaneous : 8.3.3.7

long-term : 8.3.3.7

short-term : 8.3.3.7

spectrogram : 8.3.2

spectrogram examples : 8.3.3

lowpass filter

by FFT : 9.1.4.2

design specifications : 5.2

ideal : 5.1

Lp norms : 5.10.1

LPC : 11.3.3.4

magnitude-only analysis/synthesis : 21.7

magnitude-only reconstruction : 20.11.1

main-lobe bandwidth : 6.5 | 6.5 | 6.5.1

masking : 11.1.1

matlab

bandlimited impulse train : 11.3.3.1

cepstrum : 11.3.3

Chebyshev bandpass design : 5.5.2.2

DPSS window : 4.8.1 | 19.1.2

F0 estimation : 19.6

frequency warping : 19.5

Hilbert transform filter : 5.6.2

linear prediction : 11.3.3

minimum zero-padding factor : 19.2.4

nonlinear-phase filters : 5.10.7

peak finder : 19.2

phase unwrapping : 19.4 | 19.4.1

spectral envelopes : 11.3.3

spectral peak-finding : 19.2.1

spectrogram : 19.3

spectrum analysis windows : 19.1

window method for FIR filter design : 5.5.1

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

sinusoid parameter estimation : 6.7.2

mean of a distribution : 17.12.1

mean of a random process : 16.1.7

minimum phase and causal cepstra : 5.9

minimum phase filters : 5.8

modal decomposition : 20.1

model : 20.11

modulated lapped transform : 4.2.6

modulation theorem : 11.6.2 | 15.6

modulation, complex : 10.3.2

moment-generating function : 17.12.3

Morlet wavelet : 12.9.1.6

mother wavelet : 12.9.1.6

MPEG filter banks : 12.7

multirate filter banks : 12

multirate noble identities : 12.2.5

multirate systems : 12.1

multiresolution sinusoidal modeling : 20.11.5

multiresolution STFT : 8.3.2 | 8.3.3.1 | 8.3.3.1 | 11.4.4.1

munchkinization : 11.5

music information retrieval : 11.1.3

myspectrogram : 19.3.1

natural basis : 12.9.1.1

noble identities : 12.2.5

noise : 7.1.2

filtered : 7.14

mean : 16.1.7

stochastic process : 16.1

synthesis example : 7.14.2

white : 16.3

noise modeling : 11.4.4.2

noise process : 16.1.4

noise spectrum analysis : 7

periodogram : 7.11

pink noise example : 7.14.3

Welch's method : 7.12

noise substitution : 11.4.4.1

non-coherent addition of signals : 7.15

nonlinear-phase filter design : 5.10.6

nonparametric method : 11.3

nonparametric representation : 20.11

nonuniform resampling : 8.3.3.3

normal distribution : 6.7.2

normal equations : 5.10.3 | 11.3.2.3

normalized DFT : 12.9.1.2

normalized frequency : 3.1

normalized radian frequency : 6.2

notation glossary : 14

oboe spectrum analysis : 4.4

octave filter bank : 11.7.1 | 12.9.1.9

oddly stacked : 12.7.2

OLA : see overlap-addtextbf

optimized windows : 4.12

orthogonal projection matrix : 5.10.3

orthogonal two-channel filter banks : 12.3.8

orthogonality principle : 6.7.1.2

orthonormal : 12.9.1

overcomplete basis : 12.9.1.5

overlap-add

convolution in matlab : 9.2.5

decomposition : 9.2.1

FFT convolution : 9.2

FFT processor : 9

frequency domain : 11.7.1

interpretation of STFT : 9 | 10.1.1

time domain : 9.2.1

time-varying modifications : 9.5

oversampled filter banks : 12.3

overtone : 11.4

panning : 7.16

parabolic interpolation bias : 6.6.2

paraconjugate : 12.3.8

parametric method : 11.3

parametric model : 20.11

paraunitary filter bank : 12.5

Parks-McClellan algorithm : 5.10.2

Parseval's theorem : 3.3.8

partial overtone : 11.4

partition of unity property : 9.2.1

PDF : see probability density functiontextbf

peak detection : 21.3

peak matching : 21.4

peak-finding : 6.7

peak-finding in matlab : 19.2.1

peak-tracking in spectrogram : 11.4.2.3

perceptual audio coding : 20.12

perfect reconstruction : 10.1.3

cosine modulated filter bank : 12.7.2

filter bank, conditions for : 12.4.5

filter banks : 12.4

filter banks, critically sampled : 12.3

periodic sinc function : 4.1

periodogram : 7.11

periodogram method for power spectrum estimation : 7.12

phase interpolation : 11.4.2.1

phase modulation : 20.9

phase modulation envelopes : 20.10

phase unwrapping : 19.4.1

phase vocoder : 20.7

FFT implementation : 20.7.1

sinusoidal modeling : 20.10

phasiness : 11.5.3

phons : 8.3.3.7

piecewise linear approximation : 20.10.1.2

pink noise : 7.14 | 7.14.2

pitch detection : 11.1 | 11.1

Poisson summation formula : 9.3.1

continuous time : 15.15

Poisson window : 4.6

polyphase component filters : 12.2.1

polyphase components : 12.2

polyphase decomposition : 12.1.3 | 12.2.1

two-channel case : 12.2

type I : 12.2.2

type II : 12.2.3

polyphase filter bank : 12.1.3

polyphase matrix : 12.4

polyphase signals : 12.1.3

Portnoff window : 10.7

power spectral density : 16.2.5

smoothed : 7.7

power spectrum : 16.2.5

power theorem : 3.3.8 | 15.9

prediction coefficients : 11.3.2

prediction error : 11.3.2

preemphasis : 4.4.4 | 11.1.1 | 21.8

preprocessing : 11.1.1

Princen-Bradley filter bank : 12.7.2

probability density function : 16.1.3

probability distribution : 16.1.1 | 16.1.1

continuous : 16.1.3

discrete : 16.1.1

processing gain : 7.15

prolate spheroidal wave function : 4.8

prolate spheroidal window : 4.8

PSD : see power spectral densitytextbf

pseudo-inverse : 5.10.3

Pseudo-QMF filter bank : 12.7.1

QMF : see quadrature mirror filtertextbf

quadratic form : 5.10.3

quadratic interpolation : 6.6

quadratically interpolated FFT (QIFFT) method : 6.6

quadrature mirror filters (QMF) : 12.3.5

quasi octave filter bank : 11.7.10.1

radians per second : 15.1

raised-cosine window : 4.2.1

random phase : 11.4.3.2

random process : 16.1.4

random variable : 16.1.3 | 16.1.3

Rayleigh's energy theorem : 3.3.8

real signal DTFT : 3.3.3.1

rectangular pulse : 15.12

rectangular window : 4.1 | 4.1.2 | 6.3

rectangular window side-lobes : 4.1.1

Remez exchange algorithm : 4.13.8 | 5.10.2

repeat operator : 3.3.10

repeat theorem : 3.3.11

residual signal : 11.4.3.1

resolution of frequencies : 6.5.2

resolution window length : 6.5.2

resolving sinusoids : 6.5

reverse polyphase decomposition : 12.2.3

rheotomes : 20.2

Riemann Lemma : 3.4.2 | 15.18

roll-off rate : 15.18

running-sum lowpass filter : 10.3.1

S+N+T time scale modification : 11.5.1

sample autocorrelation : 7 | 7.4 | 7.9

sample mean of a random process : 16.1.8

sample power spectral density : 7.5

sample PSD : 7

sample variance : 7.4 | 16.1.10 | 16.1.10

sampled rectangular pulse : 15.14

sampling synthesis : 20.8.4.1

sampling theory : 15.16

scale parameter, wavelets : 12.9.1.6

scaling theorem : 15.4

scalogram : 12.9.1.6

second central moment : 16.1.9 | 17.12.2

second moments of a signal : 15.17.1

shah symbol : 15.14

shift operator : 3.3.4

shift theorem : 3.3.4 | 3.3.4 | 15.5

short-time Fourier transform (STFT) : 8 | 8.1

downsampling : 10.8

filter bank, downsampled : 10.8.1

filter-bank interpretation : 10.1.2

generalized : 12.9.1.11

modifications : 10.9

overlap-add interpretation : 10.1.1

time-scaling : 11.5.2

weighted overlap-add : 9.6

side-lobe width : 6.5

sifting property : 6.1 | 15.10

signal model : 6.7.1

similarity theorem : 15.4

sinc function : 4.1 | 5.5

sinc function, aliased (periodic) : 4.1

sine window : 4.2.6 | 4.2.6

sines+noise spectral modeling : 11.4.3

sines+noise synthesis : 20.11.4

sines+noise+transients : 11.4.4

sinusoidal amplitude estimation : 6.7.1.1

sinusoidal model : 11.4 | 20

frequency scaling : 11.5

history : 20.11.2

nonparametric : 20.8.3

software (PARSHL) : 21

time-scale modification : 11.5

sinusoidal parameter estimation

general case : 6.7.1.3

known frequency : 6.7.1.2

known frequency and phase : 6.7.1.1

least squares : 6.7.1

sinusoidal spectrum analysis : 6

Slepian window : 4.8

sliding DFT : 10.3.4.2

sliding FFT : 20.10.1.1

SNT : see sines+noise+transientstextbf

sones : 8.3.3.7

source-filter decomposition : 11.3.2.5

source-filter model : 20.5

specific loudness : 8.3.1 | 8.3.2 | 8.3.3.5

spectral display : 8.1

spectral envelope : 11.3

cepstral method : 11.3.1 | 11.3.3.2

examples : 11.3.3

linear prediction method : 11.3.2 | 11.3.3.3

spectral interpolation : 3.5

spectral modeling : 20

applications : 21.9

history : 20.11 | 20.11

overview : 2

synthesis : 20.11

spectral modeling synthesis : 11.4 | 11.4

spectral models : 11.4

spectral modifications : 9

spectral resolution : 6.5

spectral transformations : 21.5

spectrogram : 8.2

audio display : 8.3

hop size : 8.3.2.1

loudness : 8.3.2

speech : 8.2.1

spectrogram parameters : 8.2

spectrum : 6.1

spectrum analysis : 4

noise : 7

oboe data : 4.4

sinusoids or spectral peaks : 6

statistical formulation : 16

time varying : 8

speech spectrogram : 8.2.1

speech synthesis examples : 20.5.1

square integrable : 3.2.1

stationary : 7.1.1 | 16.1.6

stationary stochastic process : 16.1.5

statistical signal processing : 16

Steiglitz-McBride algorithm : 18.3.1

step size (STFT) : 8.1.3

stereo panning : 7.16

STFT : see short-time Fourier transformtextbf

stochastic part : 11.4.3.2

stochastic process : 7 | 16.1.4

stop-band attenuation : 5.5.2.1

stretch operator : 3.3.9 | 3.3.9 | 12.1.1

stretch theorem : 3.3.11

strong COLA constraint : 9.3.2.1 | 9.3.2.1

subtractive synthesis : 11.4.3

symmetric Toeplitz operator : 4.8

symmetry of DTFT, real signals : 3.3.3

synthesis, additive : 11.4.1

Telharmonium : 20.2

third-octave filter bank : 8.3.1 | 11.7.1

time compression/expansion : 11.5

time limited : 5.5

time normalized : 8.1.3

time reversal and the DTFT : 3.3.2

time-bandwidth product : 15.17.3

time-domain aliasing : 9.1.2.2 | 9.1.4.3

time-frequency

displays : 8

map : 11.4.4.1

reassignment : 20.11.7

time-limited interpolation : 3.5.2

time-limited signals : 15.17.2

time-scale modification (TSM) : 11.5

time-varying OLA modifications : 9.5

Toeplitz matrix : 11.3.2.3

tone wheels : 20.2

total variation : 15.18

tracking peaks in spectrograms : 11.4.2.3

transform coders : 8.1.4

transient models : 20.11.6

transpose, filter bank : 12.3.4 | 12.4.7

triangular window : 4.5

TSM : see time-scale modificationtextbf

twiddle factor : 12.1.2

two-sided Taylor expansion : 5.9

unbiased estimator : 16.1.8 | 16.1.10

uncertainty principle : 15.17

unimodular polynomial matrix : 12.5.3

unwrapping phase : 19.4.1

upsampling (stretch) operator : 12.1.1

variance : 16.1.9 | 16.1.9

of a distribution : 17.12.2

of estimators : 16.3.3

vocoder : 20.5

voder : 20.6

wavelet

admissibility condition : 12.9.1.6

coefficient : 12.9.1.6

filter banks : 12.9

scale parameter : 12.9.1.6

wavetable synthesis : 20.8.4.1

weak COLA constraint : 9.3.2

weighted least squares : 5.10.3

weighted overlap-add (WOLA) : 9.6

phase-vocoder : 11.5.2

Welch autocorrelation : 7.12.1 | 7.12.2

Welch's method, spectrum analysis : 7.12

Welch's method, windowed : 7.13

white noise : 7.1.1 | 7.1.2 | 7.3 | 7.3.1 | 7.4 | 7.4 | 7.5 | 7.5 | 7.7 | 7.10 | 7.11 | 7.11.1 | 7.14 | 7.14 | 7.14 | 7.14.2 | 16.3

whitening filter : 11.3.2

Wiener-Hopf equations : 11.3.2.3

window : 4

Bartlett : 4.5

Blackman : 4.3.1 | 19.1.1

Chebyshev : 4.10

design by linear programming : 4.13

Dolph-Chebyshev : 4.10

Dolph-Chebyshev theory : 4.10.4

DPSS : 4.8

exponential : 4.6

frequency resolution : 4.9.3

frequency-domain implementation : 4.3.5

generalized Hamming : 4.2 | 4.2.5

Hann-Poisson : 4.7

introduction : 4

Kaiser : 4.9

Kaiser-Bessel : 4.9

minimum length for resolving sinusoids : 6.5.4

no side-lobes case : 4.7

optimized : 4.12

Poisson : 4.6

prolate spheroidal : 4.8

qualitative effect : 6.4

rectangular : 4.1 | 4.1.2

resolution bandwidth : 6.5

sine : 4.2.6

Slepian : 4.8

triangular : 4.5

zero phase : 4.1

window method, FIR filter design : 5.5 | 5.7

WOLA : see weighted overlap-addtextbf

Yule-Walker equations : 11.3.2.3

zero padding : 3.5.3

definition : 8.1.3

matlab : 19.2.4

zero-phase form : 3.5.4

zero-centered : 6.3

zero-phase windows : 4.1

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