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This paper introduces reduced rank statistical processing to residual error linear predictive source coding. A reduced rank predictive weight vector is generated using the reduced order correlation kernel estimation technique (ROCKET). The results illustrate a significant reduction in the reconstruction error of a reduced rank filter when the residual error is corrupted by noise. The noise may be due to either quantization noise or channel noise. The analysis shows that a filter's impulse response determines the impact of noise on its signal reconstruction and it is the ability of the predictive filter to alter its impulse response as a function of rank, which improves its performance. The results are demonstrated on recorded speech data and compared with the conventional Levinson-Durbin algorithm. Finally it is interesting to note that the reason for this reduced rank performance gain is not related to limited training data for the predictive filter.
Statistical Signal Processing, 2003 IEEE Workshop on
Date of Conference: 28 Sept.-1 Oct. 2003