Modal synthesis is a practical and efficient way to model sounding structures with strong resonances. In order to create realistic sounds, one has to be able to extract the parameters of this model from recorded sounds produced by the physical system of interest. Many methods are available to achieve this goal, and most of them require a careful parametrization and a post-selection of the modes to guarantee a good quality/complexity trade-off. This paper introduces a two step analysis method aiming at an automatic and reliable identification of the modes. The first step is performed at a global level with few assumptions about the spectro/temporal content of the considered signal. From the knowledge gained with this global analysis, one can focus on specific frequency regions and perform a local analysis with strong assumptions. The gains of such a two step approach are a better estimation of the number of modal components as well as a better estimate of their parameters.

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Exponentially damped sinusoids (EDS) model-based analysis of sound signals often requires a precise estimation of initial amplitudes and phases of the components found in the sound, on top of a good estimation of their frequencies and damping. This can be of the utmost importance in many applications such as high-quality re-synthesis or identification of structural properties of sound generators (e.g. a physical coupling of vibrating devices). Therefore, in those specific applications, an accurate estimation of the onset time is required. In this paper we present a two-step onset time estimation procedure designed for that purpose. It consists of a “rough" estimation using an STFT-based method followed by a time-domain method to “refine" the previous results. Tests carried out on synthetic signals show that it is possible to estimate onset times with errors as small as 0.2ms. These tests also confirm that operating first in the frequency domain and then in the time domain allows to reach a better resolution vs. speed compromise than using only one frequency-based or one time-based onset detection method. Finally, experiments on real sounds (plucked strings and actual percussions) illustrate how well this method performs in more realistic situations.

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