A cepstrum (/ -, - /,; plural cepstra) is the result of taking the inverse Fourier transform (IFT) of the logarithm of the estimated signal spectrum. There is a complex cepstrum, a real cepstrum, a power cepstrum, and a phase cepstrum. The power cepstrum in particular has applications in the analysis of human speech.
The name "cepstrum" was derived by reversing the first four letters of "spectrum". Operations on cepstra are labelled quefrency analysis (aka quefrency alanysis), liftering, or cepstral analysis. It may be pronounced in the two ways given, the second having the advantage of avoiding confusion with "kepstrum", which also exists (see below).
Origin and definition
The power cepstrum was defined in a 1963 paper by Bogert et al. The power cepstrum of a signal is defined as the squared magnitude of the inverse Fourier transform of the logarithm of the squared magnitude of the Fourier transform of a signal:
- power cepstrum of signal
References to the Bogert paper, in a bibliography, are often edited incorrectly. The terms quefrency, alanysis, cepstrum and saphe were invented by the authors by rearranging some letters in frequency, analysis, spectrum and phase. The new invented terms are defined by analogies to the older terms.
The complex cepstrum was defined by Oppenheim in his development of homomorphic system theory and is defined as the inverse Fourier transform of the logarithm of the complex-valued Fourier transform of the signal (with unwrapped phase):
- complex cepstrum of signal = IFT(log(FT(the signal)) + j2πm),
- 4(real cepstrum)2 = power cepstrum
and to the complex cepstrum as
- real cepstrum = 0.5 × (complex cepstrum + time reversal of complex cepstrum).[clarification needed]
The phase cepstrum is related to the complex cepstrum as
- phase spectrum = (complex cepstrum − time reversal of complex cepstrum)2.
The complex cepstrum holds information about magnitude and phase of the initial spectrum, allowing the reconstruction of the signal. The real cepstrum uses only the information of the magnitude of the spectrum.
Many texts define the process as FT → abs() → log → IFT, i.e., that the power cepstrum is the "inverse Fourier transform of the log-magnitude Fourier spectrum". (the difference between squaring or taking the absolute value amounts to an overall factor of 2).
The kepstrum, 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.
The cepstrum can be seen as information about the rate of change in the different spectrum bands. It was originally invented for characterizing the seismic echoes resulting from earthquakes and bomb explosions. It has also been used to determine the fundamental frequency of human speech and to analyze radar signal returns. Cepstrum pitch determination is particularly effective because the effects of the vocal excitation (pitch) and vocal tract (formants) are additive in the logarithm of the power spectrum and thus clearly separate.
The autocepstrum is defined as the cepstrum of the autocorrelation. The autocepstrum is more accurate than the cepstrum in the analysis of data with echoes.
The cepstrum is a representation used in homomorphic signal processing, to convert signals combined by convolution (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 the mel scale. The result is called the mel-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 the vocal cords and the formant filtering of the vocal tract, which convolve in the time domain and multiply in the frequency domain, are additive and in different regions in the quefrency domain.
Recently cepstrum based deconvolution was used to remove the effect of the stochastic impulse trains, which originates an sEMG signal, from the power spectrum of sEMG signal itself. In this way, only information on motor unit action potential (MUAP) shape and amplitude were maintained, and then, used to estimate the parameters of a time-domain model of the MUAP itself.
The independent variable of a cepstral graph is called the quefrency. The quefrency is a measure of time, though not in the sense of a signal in the time 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 said fundamental frequency. Note that a pure sine wave should not be used to test the cepstrum for its pitch determination from quefrency as a pure sine wave does not contain any harmonics. 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).
Playing further on the anagram theme, a filter that operates on a cepstrum might be called a lifter. A low-pass lifter is similar to a low-pass filter in the frequency 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 smoother signal.
A very important property of the cepstral domain is that the convolution of two signals can be expressed as the addition of their complex cepstra:
- B. P. Bogert, M. J. R. Healy, and J. W. Tukey: "The Quefrency Alanysis [sic] of Time Series for Echoes: Cepstrum, Pseudo Autocovariance, Cross-Cepstrum and Saphe Cracking". Proceedings of the Symposium on Time Series Analysis (M. Rosenblatt, Ed) Chapter 15, 209-243. New York: Wiley, 1963.
- Norton, Michael Peter; Karczub, Denis (November 17, 2003). Fundamentals of Noise and Vibration Analysis for Engineers. Cambridge University Press. ISBN 0-521-49913-5.
- A. Michael Noll and Manfred R. Schroeder, "Short-Time 'Cepstrum' Pitch Detection," (abstract) Journal of the Acoustical Society of America, Vol. 36, No. 5, p. 1030
- A. Michael Noll (1964), “Short-Time Spectrum and Cepstrum Techniques for Vocal-Pitch Detection”, Journal of the Acoustical Society of America, Vol. 36, No. 2, pp. 296–302.
- A. Michael Noll (1967), “Cepstrum Pitch Determination”, Journal of the Acoustical Society of America, Vol. 41, No. 2, pp. 293–309.
- A. V. Oppenheim, "Superposition in a class of nonlinear systems" Ph.D. diss., Res. Lab. Electronics, M.I.T. 1965.
- A. V. Oppenheim, R. W. Schafer, "Digital Signal Processing", 1975 (Prentice Hall).
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- G. Biagetti, P. Crippa, S. Orcioni, and C. Turchetti, “Homomorphic deconvolution for muap estimation from surface emg signals,” IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 2, pp. 328– 338, March 2017.
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