Estimation of missing LSF parameters using Gaussian mixture models
نویسندگان
چکیده
Speech transmission over packet networks has to cope with packet delays and packet losses. When a packet loss occurs the missing information must be estimated. In this contribution we focus on restoring the spectral parameters of a speech coder. A novel approach to estimating missing Line Spectral Frequency (LSF) parameters using Gaussian Mixture Models (GMM) is proposed. We present the estimation algorithm and study its performance when one or several LSF parameters are lost. We show that a GMM of a relatively low order is sufficient to achieve a substantial improvement in parameter SNR. Therefore, the new estimation procedure requires much less memory than histogram based estimation methods.
منابع مشابه
Nonlinear estimation of missing ΔLSF parameters by a mixture of Dirichlet distributions
In packet networks, a reliable scheme to handle packet loss during speech transmission is of great importance. As a common representation of the linear predictive coding (LPC) model, the line spectral frequency (LSF) parameters are widely used in speech quantization and transmission. In this paper, we propose a novel scheme to estimate the missing values occurring during LPC model transmission....
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