Time-frequency and time-scale analysis of deformed stationary processes, with application to non-stationary sound modeling

Abstract : A class of random non-stationary signals termed timbre×dynamics is introduced and studied. These signals are obtained by non-linear transformations of sta-tionary random gaussian signals, in such a way that the transformation can be approximated by translations in an appropriate representation domain. In such situations, approximate maximum likelihood estimation techniques can be de-rived, which yield simultaneous estimation of the transformation and the power spectrum of the underlying stationary signal. This paper focuses on the case of modulation and time warping of station-ary signals, and proposes and studies estimation algorithms (based on time-frequency and time-scale representations respectively) for these quantities of interest. The proposed approach is validated on numerical simulations on synthetic signals, and examples on real life car engine sounds.
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Dernière modification le : dimanche 18 juin 2017 - 21:40:23
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Harold Omer, Bruno Torrésani. Time-frequency and time-scale analysis of deformed stationary processes, with application to non-stationary sound modeling. Applied and Computational Harmonic Analysis, Elsevier, 2017, 43 (1), pp.1-22. 〈http://www.sciencedirect.com/science/article/pii/S1063520315001438〉. 〈10.1016/j.acha.2015.10.002〉. 〈hal-01094835v2〉

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