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Identifier 000362020
Title Decomposition of AM-FM signals with applications in speech processing
Alternative Title Αποδιαμόρφωση ΑΜ-FM σημάτων με εφαρμογές στην επεξεργασία φωνής
Author Πανταζής, Ιωάννης Νικολάου
Thesis advisor Στυλιανού, Ιωάννης
Abstract During the last decades, sinusoidal model gained a lot of popularity since it is able to represent non-stationary signals very accurately. The estimation of the instantaneous components (i.e. instantaneous amplitude, instantaneous frequency and instantaneous phase) is an active area of research. In this thesis, we develop and test models and algorithms for the estimation of the instantaneous components of sinusoidal representation. Our goal is to reduce the estimation error due to the non-stationary character of the analyzed signals by taking advantage of time-domain information. Thus, we re-introduce a time-varying model referred to as QHM which is able to adjust its frequency values closer to the true frequency values. We further show that an iterative scheme based on QHM produce statistically effcient sinusoidal parameter estimation. Moreover, we extend QHM to chirp QHM (cQHM) which is able to capture linear evolution of instantaneous frequency quite satisfactorily. However, neither QHM nor cQHM are not able to represent highly non-stationary signals adequately. Thus, we further extend QHM to adaptive QHM (aQHM) which uses time-domain frequency information. aQHM is able to adjust its non-parametric basis functions to the timevarying characteristics of the signal. This results to reduction of the estimation error of the instantaneous components. Moreover, an adaptive AM-FM decomposition algorithm based on aQHM is proposed. Results on synthetic signals as well in voiced speech showed that aQHM greatly reduce the reconstruction error compared to QHM or sinusoidal model of McAulay and Quatieri [1]. Concentrating on speech applications, we develop an analysis/synthesis speech system based on aQHM. Actually, aQHM is used for the representation of the quasi-periodicities of speech while the aperiodic part of speech is modeled as a time- and frequency-modulated noise. The resynthesized speech signal produced by the proposed system is indistinguishable from the original. Finally, another application of speech analysis where aQHM can be applied is the extraction of vocal tremor characteristics. Since vocal tremor is defined as modulations of the instantaneous components of speech, aQHM is the appropriate model for the representation of these modula- tions. Indeed, results showed that the reconstructed signals are close to the original signals which validate our method.
Language English
Subject AM-FM signals
Analysis-synthesis of speech
Harmonic models
Sinusoidal models
Speech processing
ΑΜ-FΜ σήματα
Ανάλυση-σύνθεση φωνής
Αρμονικά μοντέλα
Επεξεργασία φωνής
Ημιτονοειδή μοντέλα
Issue date 2010-07-13
Collection   School/Department--School of Sciences and Engineering--Department of Computer Science--Doctoral theses
  Type of Work--Doctoral theses
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