InFourier analysis, thecepstrum (/ˈkɛpstrʌm,ˈsɛp-,-strəm/; pluralcepstra, adjectivecepstral) is the result of computing theinverse Fourier transform (IFT) of thelogarithm of the estimatedsignal spectrum. The method is a tool for investigating periodic structures infrequency spectra. Thepower cepstrum has applications in the analysis ofhuman speech.

The termcepstrum was derived by reversing the first four letters ofspectrum. Operations on cepstra are labelledquefrency analysis (orquefrency alanysis^[1]),liftering, orcepstral analysis. It may be pronounced in the two ways given, the second having the advantage of avoiding confusion withkepstrum.

The concept of the cepstrum was introduced in 1963 by B. P. Bogert, M. J. Healy, andJ. W. Tukey.^[1] It serves as a tool to investigate periodic structures in frequency spectra.^[2] Such effects are related to noticeable echos orreflections in the signal, or to the occurrence of harmonic frequencies (partials,overtones). Mathematically it deals with the problem ofdeconvolution of signals in the frequency space.^[3]

References to the Bogert paper, in a bibliography, are often edited incorrectly.^{[citation needed]} The terms "quefrency", "alanysis", "cepstrum" and "saphe" were invented by the authors by rearranging the letters in frequency, analysis, spectrum, and phase. The invented terms are defined in analogy to the older terms.

The cepstrum is the result of following sequence of mathematical operations:

• Fourier transformation of asignal from thetime domain to thefrequency domain
• computation of the logarithm of the spectral amplitude
• Inverse Fourier transformation to time domain, where the final independent variable, the quefrency, has a time scale.^[1][2][3]

The cepstrum is used in many variants. Most important are:

• power cepstrum: The logarithm is taken from the "power spectrum"
• complex cepstrum: The logarithm is taken from the spectrum, which is calculated via Fourier analysis

The following abbreviations are used in the formulas to explain the cepstrum:

Abbreviation	Explanation
${\displaystyle f(t)}$	Signal, which is a function of time
${\displaystyle C}$	Cepstrum
${\displaystyle \mathcal{F}}$	Fourier transform: The abbreviation can stand i.e. for acontinuous Fourier transform, adiscrete Fourier transform (DFT) or even az-transform, as the z-transform is a generalization of the DFT.[3]
${\displaystyle {\mathcal{F}}^{-1}}$	Inverse of the fourier transform
${\displaystyle \log (x)}$	Logarithm ofx. The choice of the baseb depends on the user. In some articles the base is not specified, others prefer base 10 or e. The choice of the base has no impact on the basic calculation rules, but sometimes basee leads to simplifications (see "complex cepstrum").
${\displaystyle \left|x\right|}$	Absolute value, or magnitude of acomplex value, which is calculated from real- and imaginary part using thePythagorean theorem.
${\displaystyle {\left|x\right|}^2}$	Absolute square
${\displaystyle \phi }$	Phase angle of acomplex value

The "cepstrum" was originally defined aspower cepstrum by the following relationship:^[1][3]

${\displaystyle C_p={\left|{\mathcal{F}}^{-1}\left\{\log \left({\left|\mathcal{F}\{f(t)\}\right|}^2\right)\right\}\right|}^2}$

The power cepstrum has main applications in analysis of sound and vibration signals. It is a complementary tool to spectral analysis.^[2]

Sometimes it is also defined as:^[2]

${\displaystyle C_p={\left|\mathcal{F}\left\{\log \left({\left|\mathcal{F}\{f(t)\}\right|}^2\right)\right\}\right|}^2}$

Due to this formula, the cepstrum is also sometimes called thespectrum of a spectrum. It can be shown that both formulas are consistent with each other as the frequency spectral distribution remains the same, the only difference being a scaling factor^[2] which can be applied afterwards. Some articles prefer the second formula.^[2][4]

Other notations are possible due to the fact that the log of the power spectrum is equal to the log of the spectrum if a scaling factor 2 is applied:^[5]

${\displaystyle \log |\mathcal{F}{|}^2=2\log |\mathcal{F}|}$

and therefore:

${\displaystyle C_p={\left|{\mathcal{F}}^{-1}\left\{2\log |\mathcal{F}|\right\}\right|}^2,\text{ or}}$

${\displaystyle C_p=4\cdot {\left|{\mathcal{F}}^{-1}\left\{\log |\mathcal{F}|\right\}\right|}^2,}$

which provides a relationship to thereal cepstrum (see below).

Further, it shall be noted, that the final squaring operation in the formula for the power cepstrum${\displaystyle C_p}$ is sometimes called unnecessary^[3] and therefore sometimes omitted.^[4][2]

Thereal cepstrum is directly related to the power cepstrum:

${\displaystyle C_p=4\cdot C_r^2}$

It is derived from the complex cepstrum (defined below) by discarding the phase information (contained in theimaginary part of thecomplex logarithm).^[4] It has a focus on periodic effects in the amplitudes of the spectrum:^[6]

${\displaystyle C_r={\mathcal{F}}^{-1}\left\{\log (\mathcal{|}\mathcal{F}\{\mathcal{f}\mathcal{(}\mathcal{t}\mathcal{)}\}\mathcal{|})\right\}}$

Thecomplex cepstrum was defined by Oppenheim in his development of homomorphic system theory.^[7][8] The formula is provided also in other literature.^[2]

${\displaystyle C_c={\mathcal{F}}^{-1}\left\{\log (\mathcal{F}\{f(t)\})\right\}}$

As${\displaystyle \mathcal{F}}$ is complex the log-term can be also written with${\displaystyle \mathcal{F}}$ as a product of magnitude and phase, and subsequently as a sum. Further simplification is obvious, if log is anatural logarithm with base e:

${\displaystyle \log (\mathcal{F})=\log (\mathcal{|}\mathcal{F}\mathcal{|}\cdot {\mathcal{e}}^{\mathcal{i}\phi })}$

${\displaystyle {\log }_e(\mathcal{F})={\log }_e(\mathcal{|}\mathcal{F}\mathcal{|})+{\log }_e(e^{i\phi })={\log }_e(\mathcal{|}\mathcal{F}\mathcal{|})+i\phi }$

Therefore: The complex cepstrum can be also written as:^[9]

${\displaystyle C_c={\mathcal{F}}^{-1}\left\{{\log }_e(\mathcal{|}\mathcal{F}\mathcal{|})+i\phi \right\}}$

The complex cepstrum retains the information about the phase. Thus it is always possible to return from the quefrency domain to the time domain by the inverse operation:^[2][3]

${\displaystyle f(t)={\mathcal{F}}^{-1}\left\{b^{\left(\mathcal{F}\{C_c\}\right)}\right\},}$

whereb is the base of the used logarithm.

Main application is the modification of the signal in the quefrency domain (liftering) as an analog operation to filtering in the spectral frequency domain.^[2][3] An example is the suppression of echo effects by suppression of certain quefrencies.^[2]

Thephase cepstrum (afterphase spectrum) is related to the complex cepstrum as

phase spectrum = (complex cepstrum − time reversal of complex cepstrum)².

Theindependent variable of a cepstral graph is called thequefrency.^[10] The quefrency is a measure of time, though not in the sense of a signal in thetime domain. For example, if the sampling rate of an audio signal is 44100 Hz and there is a large peak in the cepstrum whose quefrency is 100 samples, the peak indicates the presence of a fundamental frequency that is 44100/100 = 441 Hz. This peak occurs in the cepstrum because the harmonics in the spectrum are periodic and the period corresponds to the fundamental frequency, since harmonics are integer multiples of the fundamental frequency.^[11]

Thekepstrum, which stands for "Kolmogorov-equation power-series time response", is similar to the cepstrum and has the same relation to it as expected value has to statistical average, i.e. cepstrum is the empirically measured quantity, while kepstrum is the theoretical quantity. It was in use before the cepstrum.^[12][13]

The autocepstrum is defined as the cepstrum of theautocorrelation. The autocepstrum is more accurate than the cepstrum in the analysis of data with echoes.

Playing further on the anagram theme, a filter that operates on a cepstrum might be called alifter. A low-pass lifter is similar to alow-pass filter in thefrequency domain. It can be implemented by multiplying by a window in the quefrency domain and then converting back to the frequency domain, resulting in a modified signal, i.e. with signal echo being reduced.

The cepstrum can be seen as information about the rate of change in the different spectrum bands. It was originally invented for characterizing the seismicechoes resulting fromearthquakes andbomb explosions. It has also been used to determine the fundamental frequency of human speech and to analyzeradar signal returns. Cepstrum pitch determination is particularly effective because the effects of the vocal excitation (pitch) andvocal tract (formants) are additive in the logarithm of the power spectrum and thus clearly separate.^[14]

The cepstrum is a representation used inhomomorphic signal processing, to convert signals combined byconvolution (such as a source and filter) into sums of their cepstra, for linear separation. In particular, the power cepstrum is often used as a feature vector for representing the human voice and musical signals. For these applications, the spectrum is usually first transformed using themel scale. The result is called themel-frequency cepstrum or MFC (its coefficients are called mel-frequency cepstral coefficients, or MFCCs). It is used for voice identification,pitch detection and much more. The cepstrum is useful in these applications because the low-frequency periodic excitation from thevocal cords and theformant filtering of thevocal tract, which convolve in thetime domain and multiply in thefrequency domain, are additive and in different regions in the quefrency domain.

Note that a puresine wave can not be used to test the cepstrum for its pitch determination from quefrency as a pure sine wave does not contain any harmonics and does not lead to quefrency peaks. Rather, a test signal containing harmonics should be used (such as the sum of at least two sines where the second sine is some harmonic (multiple) of the first sine, or better, a signal with a square or triangle waveform, as such signals provide many overtones in the spectrum.).

An important property of the cepstral domain is that theconvolution of two signals can be expressed as the addition of their complex cepstra:

${\displaystyle x_1\ast x_2\mapsto x_1^{\prime }+x_2^{\prime }.}$

The concept of the cepstrum has led to numerous applications:^[2][3]

• dealing with reflection inference (radar, sonar applications, earth seismology)
• estimation of speaker fundamental frequency (pitch)
• speech analysis and recognition
• medical applications in analysis of electroencephalogram (EEG) and brain waves
• machine vibration analysis based on harmonic patterns (gearbox faults, turbine blade failures, ...)^[2][4][5]

Recently, cepstrum-based deconvolution was used on surface electromyography signals, to remove the effect of the stochastic impulse train, which originates ansEMG signal, from the power spectrum of the sEMG signal itself. In this way, only information about the motor unit action potential (MUAP) shape and amplitude was maintained, which was then used to estimate the parameters of a time-domain model of the MUAP itself.^[15]

A short-time cepstrum analysis was proposed bySchroeder and Noll in the 1960s for application to pitch determination of human speech.^[16][17][14]

• Childers, D.G.; Skinner, D.P.; Kemerait, R.C. (1977). "The cepstrum: A guide to processing".Proceedings of the IEEE.65 (10). Institute of Electrical and Electronics Engineers (IEEE):1428–1443.Bibcode:1977IEEEP..65.1428C.doi:10.1109/proc.1977.10747.ISSN 0018-9219.S2CID 6108941.
• Oppenheim, A.V.; Schafer, R.W. (2004). "Dsp history - From frequency to quefrency: a history of the cepstrum".IEEE Signal Processing Magazine.21 (5). Institute of Electrical and Electronics Engineers (IEEE):95–106.Bibcode:2004ISPM...21...95O.doi:10.1109/msp.2004.1328092.ISSN 1053-5888.S2CID 1162306.
• "Speech Signal Analysis"
• "Speech analysis: Cepstral analysis vs. LPC", www.advsolned.com
• "A tutorial on Cepstrum and LPCCs"

• Frequency-domain analysis
• Signal processing

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