This paper presents a multicomponent sinusoidal model of speech signals, obtained through a rigorous mathematical formulation that ensures an asymptotically exact reconstruction of these nonstationary signals, despite the presence of transients, voiced segments, or unvoiced segments. This result has been obtained by means of the iterated use of the Hilbert transform, and the convergence properties of the proposed method have been both analytically investigated and empirically tested. Finally, an adaptive segmentation algorithm used to accurately compute instantaneous frequencies from unwrapped phases, suited to complete the proposed AM-FM model, is presented.
Asymptotically exact AM-FM decomposition based on iterated Hilbert transform / Gianfelici, F; Biagetti, Giorgio; Crippa, Paolo; Turchetti, Claudio. - 2:(2005), pp. 1121-1124. (Intervento presentato al convegno 6th INTERSPEECH 2005 and 9th European Conference on Speech Communication and Technology (EUROSPEECH) tenutosi a Lisbona, Portogallo nel 4 - 8 Settembre 2005).
Asymptotically exact AM-FM decomposition based on iterated Hilbert transform
BIAGETTI, Giorgio;CRIPPA, Paolo;TURCHETTI, Claudio
2005-01-01
Abstract
This paper presents a multicomponent sinusoidal model of speech signals, obtained through a rigorous mathematical formulation that ensures an asymptotically exact reconstruction of these nonstationary signals, despite the presence of transients, voiced segments, or unvoiced segments. This result has been obtained by means of the iterated use of the Hilbert transform, and the convergence properties of the proposed method have been both analytically investigated and empirically tested. Finally, an adaptive segmentation algorithm used to accurately compute instantaneous frequencies from unwrapped phases, suited to complete the proposed AM-FM model, is presented.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
