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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