Adaptive time-varying parametric modeling (original) (raw)

NONLINEAR ADAPTIVE PREDICTION OF NONSTATIONARY SIGNALS WITH APPLICATION T O SPEECH CODING

The purpose of this contribution is to present a new approach for the prediction of speech signals that is appropriate to speech coding. T h e procedure is based upon the principles of blind equalisation. In an earlier publication we examined these principles from the prediction point of view as a general method. The present contribution examines the approach in relation to speech signal representation for coding and compression. T h e method outlined in this contribution offers significant advantages compared to the standard prediction methods. This is because the signal estimation is carried out on a sample by sample basis, it needs no estimation of the covariance matrix or some other long term statistical attributes, it makes no assumption on a minimum phase vocal tract transfer function, and hence, it is faithful to the nonstationarities in the analysed signal. The method can be seen as mapping a given signal into a set of signals of increased correlational properties, which in turn may be so mapped until the signals exhibit piecewise constant behaviour. At this stage they are easily modelled and the reverse process can be put into effect for the original signal reconstruction. Speech signals can be rendered piece-wise constant approximately after some signal decompositions. Then, the percentage error of predicted value is involved t o examine if the decomposition stops. Examples illustrating these principles are included.

Adaptive Sinusoidal Models for Speech with Applications in Speech Modifications and Audio Analysis

2014

Sinusoidal Modeling is one of the most widely used parametric methods for speech and audio signal processing. The accurate estimation of sinusoidal parameters (amplitudes, frequencies, and phases) is a critical task for close representation of the analyzed signal. In this thesis, based on recent advances in sinusoidal analysis, we propose high resolution adaptive sinusoidal models for analysis, synthesis, and modifications systems of speech. Our goal is to provide systems that represent speech in a highly accurate and compact way. Inspired by the recently introduced adaptive Quasi-Harmonic Model (aQHM) and adaptive Harmonic Model (aHM), we overview the theory of adaptive Sinusoidal Modeling and we propose a model named the extended adaptive Quasi-Harmonic Model (eaQHM), which is a non-parametric model able to adjust the instantaneous amplitudes and phases of its basis functions to the underlying time-varying characteristics of the speech signal, thus significantly alleviating the so...