ARMA model parameter estimation based on the equivalent MA approach
Digital Signal Processing
A new spectrum envelope estimation technique based on sample projections
Signal Processing
On cepstral and all-pole based spectral envelope modeling with unknown model order
Pattern Recognition Letters
Stabilised weighted linear prediction
Speech Communication
A comparative study of glottal source estimation techniques
Computer Speech and Language
Quasi Closed Phase Glottal Inverse Filtering Analysis With Weighted Linear Prediction
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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A method for parametric modeling and spectral envelopes when only a discrete set of spectral points is given is introduced. This method, called discrete all-pole (DAP) modeling, uses a discrete version of the Itakura-Saito distortion measure as its error criterion. One result is an autocorrelation matching condition that overcomes the limitations of linear prediction and produces better fitting spectral envelopes for spectra that are representable by a relatively small discrete set of values, such as in voiced speech. An iterative algorithm for DAP modeling that is shown to converge to a unique global minimum is presented. Results of applying DAP modeling to real and synthetic speech are also presented. DAP modeling is extended to allow frequency-dependent weighting of the error measure, so that spectral accuracy can be enhanced in certain frequency regions