Title :
Time varying linear prediction using sparsity constraints
Author :
Chetupalli, Srikanth Raj ; Sreenivas, T.V.
Author_Institution :
Dept. of Electr. Commun. Eng., Indian Inst. of Sci., Bangalore, India
Abstract :
Time-varying linear prediction has been studied in the context of speech signals, in which the auto-regressive (AR) coefficients of the system function are modeled as a linear combination of a set of known bases. Traditionally, least squares minimization is used for the estimation of model parameters of the system. Motivated by the sparse nature of the excitation signal for voiced sounds, we explore the time-varying linear prediction modeling of speech signals using sparsity constraints. Parameter estimation is posed as a 0-norm minimization problem. The re-weighted 1-norm minimization technique is used to estimate the model parameters. We show that for sparsely excited time-varying systems, the formulation models the underlying system function better than the least squares error minimization approach. Evaluation with synthetic and real speech examples show that the estimated model parameters track the formant trajectories closer than the least squares approach.
Keywords :
autoregressive processes; compressed sensing; least squares approximations; linear predictive coding; parameter estimation; speech processing; time-varying systems; autoregressive coefficients; excitation signal; least squares error minimization; norm minimization problem; parameter estimation; sparse nature; speech signals; time varying linear prediction; voiced sounds; Frequency estimation; Minimization; Resonant frequency; Speech; Time-frequency analysis; Time-varying systems; Trajectory; 1-norm minimization; Linear prediction; non-stationary signals; sparse representation; speech analysis; time-varying systems;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on
Conference_Location :
Florence
DOI :
10.1109/ICASSP.2014.6854814