Autoregressive modeling for speech coding: Estimation, interpolation and quantization

Applied Science

Quantization of the LPC Model with the Reconstruction-Error Distortion Measure

In Linear Predictive Coding algorithms, the coding of the speech signal consists of two separate stages: coding of the LPC model and coding of the excitation. In CELP, the LPC excitation is coded by Analysis-by-Synthesis in the reconstruction domain, not by minimization of the error in the LPC residual domain. Commonly used distortion measures for quantization of the LPC spectral model are the Spectral Distortion and the Likelihood Ratio. For small quantization errors, they belong to a class of similar distortion measures which express an error in the residual domain. A new spectral distortion measure is proposed, the Reconstruction Error Distortion measure, which expresses an error in the reconstruction domain. Preliminary results indicate that about five bits per frame can be gained with this new measure, without a loss in subjective quality

LPC interpolation by approximation of the sample autocorrelation function

Abstract—Conventionally, the energy of analysis frames is not taken into account for linear prediction (LPC) interpolation. Incorporating the frame energy improves the subjective quality of interpolation, but increases the spectral distortion (SD). The main reason for this discrepancy is that the outliers are increased in low energy parts of segments with rapid changes in energy. The energy is most naturally combined with a normalized autocorrelation representation. Index Terms—LPC interpolation, speech coding. I

Tracking of Nonstationary Noise Based on Data-Driven Recursive Noise Power Estimation

This paper considers estimation of the noise spectral variance from speech signals contaminated by highly nonstationary noise sources. The method can accurately track fast changes in noise power level (up to about 10 dB/s). In each time frame, for each frequency bin, the noise variance estimate is updated recursively with the minimum mean-square error (mmse) estimate of the current noise power. A time- and frequency-dependent smoothing parameter is used, which is varied according to an estimate of speech presence probability. In this way, the amount of speech power leaking into the noise estimates is kept low. For the estimation of the noise power, a spectral gain function is used, which is found by an iterative data-driven training method. The proposed noise tracking method is tested on various stationary and nonstationary noise sources, for a wide range of signal-to-noise ratios, and compared with two state-of-the-art methods. When used in a speech enhancement system, improvements in segmental signal-to-noise ratio of more than 1 dB can be obtained for the most nonstationary noise sources at high noise levels.MediamaticsElectrical Engineering, Mathematics and Computer Scienc