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Synchronized Swept-Sine: Theory, Application, and Implementation

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Exponential swept-sine signals are very often used to analyze nonlinear audio systems. A reexamination of this methodology shows that a synchronization procedure is necessary for the proper analysis of higher harmonics. An analytical expression of spectra of the swept-sine signal is derived and used in the deconvolution of the impulse response. Matlab code for generation of the synchronized swept-sine, deconvolution, and separation of the impulse responses is given. This report provides a discussion of some application issues and an illustrative example of harmonic analysis of current distortion of a woofer. An analysis of the higher harmonics of the current distortion of a woofer is compared using both the synchronized and the non-synchronized swept-sine signals.

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JAES Volume 63 Issue 10 pp. 786-798; October 2015
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Raimonds Skuruls


Comment posted November 17, 2015 @ 17:42:16 UTC (Comment permalink)

Authors are too much briefly describing the concept of the start and the final frequency of the sweep. It means that the spectrum of such sweep will have components very close to zero in bands before the start frequency and after the final frequency that will be causing division by zero in the „deconvolution” process or in another words – the deconvolution filter will have enormous high gain in mentioned bands and will bring up all noses and errors of the real measurement in that bands.


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Antonin Novak


Comment posted November 19, 2015 @ 21:28:34 UTC (Comment permalink)

The spectrum of the synchronized swept-sine signal have components very close to zero in bands before the start frequency and after the final frequency. This is normal (and intuitive) for all the chirp-like signals.

The division by zero problem in the „deconvolution” process appears in the classical methods (Eq.4). In our method, the deconvolution process consists in designing the "inverse filter" directly in frequency domain in an analytical way (Eq.43, derived in Appendix A.1). The deconvolution is then made using (Eq.50) in which no division appears. Since the inverse filter X_tilde(f) is designed analytically, there are no enormous high gains in you mentioned.

You can check this out using the Matlab code from Appendix A.2.4 in which the inverse filter is defined as:
X_ = 2*sqrt(f_ax/L).*exp(-j*2*pi*f_ax*L.*(1-log(f_ax/f1)) + j*pi/4);

Note, that the vector f_ax goes from 0 to fs, so that the filter X_ is defined also outside the region <f1,f2>


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