PARCOR Coefficient Research Articles

Automatic Vowel Sequence Reproduction for a Talking Robot Based on PARCOR Coefficient Template Matching

This paper describes an automatic vowel sequence reproduction system for a talking robot built to reproduce the human voice based on the working behavior of the human articulatory system. A sound analysis system is developed to record a sentence spoken by a human (mainly vowel sequences in the Japanese language) and to then analyze that sentence to give the correct command packet so the talking robot can repeat it. An algorithm based on a short-time energy method is developed to separate and count sound phonemes. A matching template using partial correlation coefficients (PARCOR) is applied to detect a voice in the talking robot's database similar to the spoken voice. Combining the sound separation and counting the result with the detection of vowels in human speech, the talking robot can reproduce a vowel sequence similar to the one spoken by the human. Two tests to verify the working behavior of the robot are performed. The results of the tests indicate that the robot can repeat a sequence of vowels spoken by a human with an average success rate of more than 60%.

Low-complexity PARCOR coefficient quantization and prediction order estimation designed for entropy coding of prediction residuals

Low-complexity PARCOR coefficient quantization and prediction order estimation designed for entropy coding of prediction residuals

Adaptive DOA Estimation Using a Database of PARCOR Coefficients

An adaptive direction-of-arrival (DOA) tracking method based upon a linear predictive model is developed. This method estimates the DOA by using a database that stores PARCOR coefficients as key attributes and the corresponding DOAs as non-key attributes. The k-dimensional digital search tree is used as the data structure to allow efficient multidimensional searching. The nearest neighbour to the current PARCOR coefficient is retrieved from the database, and the corresponding DOA is regarded as the estimate. The processing speed is very fast since the DOA estimation is obtained by the multidimensional searching. Simulations are performed to show the effectiveness of the proposed method.

Adaptive Peak Frequency Estimation Using a Database of PARCOR Coefficients

This paper presents an adaptive peak frequency estimation method using a database that stores PARCOR coefficients as key attributes and the corresponding peak frequencies as nonkey attributes. The least-square lattice algorithm is used to recursively estimate the PARCOR coefficients to adapt to changing circumstances. The nearest neighbor to the current PARCOR coefficient is retrieved from the database, and the corresponding peak frequency is regarded as the estimation. A simultaneous execution of database construction and peak estimation with database update is performed to accelerate the processing time and to improve the estimation accuracy.

Convergence analysis of the adaptive lattice filter for a mixed Gaussian input sequence

The convergence performance of the adaptive lattice filter (ALF) using the stochastic gradient algorithm is measured by the convergence speed and estimated error variance of the PARCOR coefficient. The convergence properties of the ALF are analysed when the filter input has a Gaussian mixture distribution. First, theoretical expressions for the convergence rate and asymptotic error variance of the PARCOR coefficient are derived, and then the theoretical expressions are compared for single and mixed Gaussian input sequences. It is shown that the convergence performance of the ALF improves as the distribution of the input signal approaches a single Gaussian distribution.

Convergence performance of adaptive lattice filters with nonlinear parameter updates

The convergence properties of adaptive lattice filters with nonlinear parameter updates have been analysed. The theoretical expressions for the convergence rate and the asymptotic error variance of the PARCOR coefficient are derived. Using these expressions, the convergence performance in the absence of impulsive noise is investigated and compared with those of the linear-type and sign-type adaptive lattice filters. Furthermore, the mean parameter variation caused by an impulsive noise and the parameter recovery time are evaluated to comprehensively compare the convergence performances in the presence of impulsive noise.

Convergence properties of an adaptive lattice filter exploiting nonlinear dynamics of a biochemical reaction system

An adaptive lattice filter (ALF) which computes the PARCOR coefficients through a cyclic enzyme system has recently been developed by the author. Using nonlinear dynamics of the cyclic enzyme system, the ALF becomes robust against impulsive noise, and the stability of the estimated AR model can be ensured. The convergence properties of the ALF are studied. First, a theoretical expression for the asymptotic error variance of the PARCOR coefficient is derived. Simulation results are presented, and the theoretical and simulated values show a very good match. Next, the convergence speed of the proposed ALF is compared with that of the simplified ALF. The step sizes are then determined by using the above theoretical expression such that both ALF's achieve the same error variance in steady states. The results show that the proposed ALF has excellent convergence properties than the simplified one.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Trellis in-loop PARCOR coefficients quantiser for speech signals

A newly developed PARCOR coefficients quantisation scheme is presented which is referred to as trellis in-loop PARCOR coefficients quantisation. In the in-loop quantisation scheme, each PARCOR coefficient is determined and quantised inside the analysis computation loop prior to the high order one. Therefore, the quantisation error is taken into account at each stage of lattice filter analysis. This scheme, when combined with trellis structure and associated with appropriate search algorithms, can achieve lower distortions than the conventional scalar quantisation. Simulation results show that by incorporating trellis structure into the in-loop quantisation scheme and using (M, L)-search algorithm with M = 4 and L = 8, an average gain of six bits per frame over the traditional methods can be achieved.

A normalized ladder form of the residual energy ratio algorithm for PARCOR estimation via projections

In this paper, we give a square-root normalized ladder algorithm for the residual energy ratio (RER) method of PARCOR coefficient estimation. The derivation uses a Hilbert space projection approach similar to that of Lee and Morf, but requires an additional related Hilbert space and its corresponding projection operators. The resulting partial correlation estimates guarantee a stable synthesis filter. In addition, we show how the algorithm may be cast in a form suitable for implementation by a CORDIC processor. Finally, we show how the normalized covariance form corresponds to an RER formulation where the prediction error energies used are chosen properly.

Variable rate speech compression by encoding subsets of the PARCOR coefficients

In LPC analysis, the speech signal is divided into frames each of which is represented by a vector of estimated vocal tract parameters, assumed to be constant throughout the frame. For many sounds, these parameters do not change significantly from one frame to the next, and some of them can often be adequately represented by previously transmitted values. In the LPC coding systems described in this paper, a number of alternative representations are considered for each frame. These representations (vectors) are combinations of PARCOR coefficients from the current frame and from previous frames. Several consecutive frames are analyzed at once, and all the possible sequences of PARCOR coefficient vectors are examined. The sequence which minimizes a preselected cost function is chosen for transmission, resulting in a reduced overall data rate. The examination of all the decision sequences is equivalent to a decision tree search, which is most efficiently accomplished through dynamic programming. Using these techniques, LPC encoded speech at 1200 bits/s is demonstrated to be of quality comparable to a constant rate LPC vocoder at 2400 bits/s.

Convergence properties of an adaptive digital lattice filter

Convergence properties of a continuously adaptive digital lattice filter used as a linear predictor are investigated for both an unnormalized and a normalized gradient adaptation algorithm. The PARCOR coefficient mean values and the output mean-square error (MSE) are approximated and a simple model is described which approximates these quantities as functions of time. Calculated curves using this model are compared with simulation results. Results obtained for a two-stage lattice are then compared with the two-stage least mean-square (LMS) transversal filter algorithm, demonstrating that it is possible but unlikely for the transversal filter to converge faster than the analogous lattice filter.

Convergence properties of an adaptive digital lattice filter

Convergence properties of a continuously adaptive digital lattice filter used as a linear predictor are investigated for both an unnormalized and a normalized gradient adaptation algorithm. The PARCOR coefficient mean values and the output mean-square error (MSE) are approximated and a simple model is described which approximates these quantities as functions of time. Calculated curves using this model are compared with simulation results. Results obtained for a two-stage lattice are then compared with the two-stage least mean-square (LMS) transversal filter algorithm, demonstrating that it is possible but unlikely for the transversal filter to converge faster than the analogous lattice filter.