A.G.A.T.A. is a Matlab-based interface which implements literature methods and original proposals concerning the autocorrelation function (ACF) analysis. The software offers several features on short time analysis. All the parameters in the software are user-selectable to give complete control on the performed computations. A.G.A.T.A. includes also different temporal windows, whose characteristics can be selected from a wide range of possibilities and non linear pre-processing, whose characteristics can be selected by the user. The influence of different shapes of the temporal window may be related to the auditory perception. Nonlinear pre-processing may improve the extraction of the factors based on ACF envelope. In this way the software offers to the user many possibilities, i.e. wide versatility. The completeness of the features and the availability of a Grafical User Interface (GUI) make temporal analysis easier and useful. Some examples of factors extracted from different signals are proposed.

A.G.A.T.A. a GUI for Ando's Temporal Analysis

DE CESARIS, SIMONA;D'ORAZIO, DARIO;GARAI, MASSIMO
2011

Abstract

A.G.A.T.A. is a Matlab-based interface which implements literature methods and original proposals concerning the autocorrelation function (ACF) analysis. The software offers several features on short time analysis. All the parameters in the software are user-selectable to give complete control on the performed computations. A.G.A.T.A. includes also different temporal windows, whose characteristics can be selected from a wide range of possibilities and non linear pre-processing, whose characteristics can be selected by the user. The influence of different shapes of the temporal window may be related to the auditory perception. Nonlinear pre-processing may improve the extraction of the factors based on ACF envelope. In this way the software offers to the user many possibilities, i.e. wide versatility. The completeness of the features and the availability of a Grafical User Interface (GUI) make temporal analysis easier and useful. Some examples of factors extracted from different signals are proposed.
Conference Proceedings from the Fifth International Symposium on TD 2011
44
49
De Cesaris S.; D’Orazio D.; Garai M.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/105513
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