Linear Predictive Coding Filter Research Articles

Linear Predictive Coding for Acute Stress Prediction from Computer Mouse Movements.

Prior work demonstrated the potential of using the Linear Predictive Coding (LPC) filter to approximate muscle stiffness and damping from computer mouse movements to predict acute stress levels of users. Theoretically, muscle stiffness and damping in the arm can be estimated using a mass-spring-damper (MSD) biomechanical model. However, the damping frequency (i.e., stiffness) and damping ratio values derived using LPC were not yet compared with those from a theoretical MSD model. This work demonstrates that the damping frequency and damping ratio from LPC are significantly correlated with those from an MSD model, thus confirming the validity of using LPC to infer muscle stiffness and damping. We also compare the stress level binary classification performance using the values from LPC and MSD with each other and with neural network-based baselines. We found comparable performance across all conditions demonstrating LPC and MSD model-based stress prediction efficacy, especially for longer mouse trajectories.Clinical relevance- This work demonstrates the validity of the LPC filter to approximate muscle stiffness and damping and predict acute stress from computer mouse movements.

Improved EEG Segmentation Using Non-linear Volterra Model in Bayesian Method

ABSTRACTIn order to analyze non-stationary signals, like Electroencephalogram (EEG), it is sometimes easier to segment signals into pseudo-stationary segments. In this paper, the cascade of linear predictive coding (LPC) and non-linear Volterra filter is employed for modeling of noise in EEG signal and this methodology is applied to the procedure of change-point detection, for estimating the number of change-points and their exact location which is a powerful way to detect the change-points as precisely as possible. The earlier results are completed by constructing algorithms that use the cascade of LPC and non-linear Volterra filter for modeling the relation between noisy signal and noise in practical situations. In a Bayesian configuration, the posterior distribution of the change-point sequence is constructed and then Markov Chain Monte Carlo procedure is used for sampling this posterior distribution. The simulation results for segmentation of synthetic and real EEG data show that by applying our newly proposed methodology, the specificity and sensitivity of the segmentation are highly improved. In the case of synthetic data, the change-points are estimated completely precise (100% correct) in 70% of times and they are estimated with at least 98% accuracy in other times.

IMPROVED NONLINEAR NOISE ESTIMATION IN EEG SIGNAL BASED ON VOLTERRA FILTERS

Recognition and compensation of undesired nonlinearity is one of the important subjects in the field of digital signal processing. The Volterra model is widely used for nonlinearity identification in practical applications. The current tendency in the digital systems design is the identification and compensation of unwanted nonlinearities. In this paper, we employed a nonlinear noise estimation approach for electroencephalogram (EEG) signal based on a combination of linear predictive coding (LPC) and Volterra filter that is a new and good way to estimate noise in EEG signal. We initially used LPC filter to estimate the noise present in EEG signal (correlated and uncorrelated noise) plus the uncorrelated portion of the signal (the part of the signal that has no linear relation to its past samples). After that, we employed nonlinear Volterra model to estimate the existing noise in EEG signal (correlated and uncorrelated noise). We show that by employing the cascade of LPC and Volterra filter, we can considerably improve the signal-to-noise ratio (SNR) in EEG signal by the ratio of at least 1.94. Also, we compared the simulation results to the case where we used just Volterra filter. In comparison with just Volterra filter, we have a significant increase in the SNR.

Steganalysis of QIM Steganography in Low-Bit-Rate Speech Signals

Steganalysis of the quantization index modulation (QIM) steganography in a low-bit-rate encoded speech stream is conducted in this research. According to the speech generation theory and the phoneme distribution properties in language, we first point out that the correlation characteristics of split vector quantization (VQ) codewords of linear predictive coding filter coefficients are changed after the QIM steganography. Based on this observation, we construct a model called the Quantization codeword correlation network (QCCN) based on split VQ codeword from adjacent speech frames. Furthermore, the QCCN model is pruned to yield a stronger correlation network. After quantifying the correlation characteristics of vertices in the pruned correlation network, we obtain feature vectors that are sensitive to steganalysis. Finally, we build a high-performance detector using the support vector machine (SVM) classifier. It is shown by experimental results that the proposed QCCN steganalysis method can effectively detect the QIM steganography in encoded speech stream when it is applied to low-bit-rate speech codec such as G.723.1 and G.729.

주입력신호의 LPC 필터 이득을 이용한 반향제거기의 수렴전 동시통화검출 성능 개선

본 논문에서는 반향제거기가 수렴하기 전에 동작할 수 있는 기존 동시통화검출법의 성능을 개선하기 위한 방법을 제안하였다. 제안된 방법은 LPC 필터 계수를 주입력신호로부터 추정하게 된다. 동시통화검출을 위한 문턱치는 주입력신호의 크기별로 기대되는 LPC 필터 이득치를 사용하여 가변적으로 설정하게 된다. 제안한 방법은 기존의 방법이 갖고 있는 단일통화를 동시통화로 잘못 판단하는 오검출률을 개선할 수 있을 뿐만 아니라 동시통화 검출 지연 시간도 단축시킬 수 있다. 장시간의 음성신호를 사용한 모의실험을 수행하였다. 제안한 방법이 기존 방법의 오검출률을 감소시키고 동시통화 검출 지연시간을 단축시킴을 확인할 수 있었다. This paper proposes a performance improvement method of the conventional double talk detection method which can operate before convergence of the echo canceller. The proposed method estimates the coefficients of the linear predictive coding(LPC) filter by using the primary input signal. The time-varying threshold for double talk detection is determined based on the LPC filter gain of the primary input signal level. The proposed method can reduce not only false detection rate which means wrong detection of single talk as double talk but also double talk detection delay. Computer simulation was performed using a long-term real speech signals. It is shown that the proposed method improves the conventional method in terms of lowering the false detection rate and shortening the detection delay.

Wavelet transform and Huffman coding based electrocardiogram compression algorithm: Application to telecardiology

We present in this work an algorithm for electrocardiogram (ECG) signal compression aimed to its transmission via telecommunication channel. Basically, the proposed ECG compression algorithm is articulated on the use of wavelet transform, leading to low/high frequency components separation, high order statistics based thresholding, using level adjusted kurtosis value, to denoise the ECG signal, and next a linear predictive coding filter is applied to the wavelet coefficients producing a lower variance signal. This latter one will be coded using the Huffman encoding yielding an optimal coding length in terms of average value of bits per sample. At the receiver end point, with the assumption of an ideal communication channel, the inverse processes are carried out namely the Huffman decoding, inverse linear predictive coding filter and inverse discrete wavelet transform leading to the estimated version of the ECG signal. The proposed ECG compression algorithm is tested upon a set of ECG records extracted from the MIT-BIH Arrhythmia Data Base including different cardiac anomalies as well as the normal ECG signal. The obtained results are evaluated in terms of compression ratio and mean square error which are, respectively, around 1:8 and 7%. Besides the numerical evaluation, the visual perception demonstrates the high quality of ECG signal restitution where the different ECG waves are recovered correctly.

Cepstral coefficients, covariance lags, and pole-zero models for finite data strings

One of the most widely used methods of spectral estimation in signal and speech processing is linear predictive coding (LPC), LPC has some attractive features, which account for its popularity, including the properties that the resulting modeling filter (i) matches a finite window of n+1 covariance lags, (ii) is rational of degree at most n, and (iii) has stable zeros and poles. The only limiting factor of this methodology is that the modeling filter is "all-pole," i.e., an autoregressive (AR) model. In this paper, we present a systematic description of all autoregressive moving-average (ARMA) models of processes that have properties (i)-(iii) in the context of cepstral analysis and homomorphic filtering. We show that each such an ARMA model determines and is completely determined by its finite windows of cepstral coefficients and covariance lags. We show that these nth-order windows form local coordinates for all ARMA models of degree n and that the pole-zero model can be determined from the windows as the unique minimum of a convex objective function. We refine this optimization method by first noting that the maximum entropy design of an LPC filter is obtained by maximizing the zeroth cepstral coefficient, subject to the constraint (i). More generally, we modify this scheme to a more well-posed optimization problem where the covariance data enter as a constraint and the linear weights of the cepstral coefficients are "positive"-in a sense that a certain pseudo-polynomial is positive-rather succinctly generalizing the maximum entropy method. This new problem is a homomorphic filter generalization of the maximum entropy method. providing a procedure for the design of any stable, minimum-phase modeling filter of degree less or equal to n that interpolates the given covariance window. We present an algorithm for realizing these filters in a lattice-ladder form, given the covariance window and the moving average part of the model, While we also show how to determine the moving average part using cepstral smoothing, one can make use of any good a priori estimate for the system zeros to initialize the algorithm. We conclude the paper with an example of this method, incorporating an example from the literature on ARMA modeling.

Method and apparatus for decoding LPC-encoded speech using a median filter modification of LPC filter factors to compensate for transmission errors

Disclosed herein are methods and apparatus for improving the quality of synthesized speech that is transmitted through a channel that is susceptible to transmission errors. In a presently preferred embodiment of the invention a speech signal is assumed to be first encoded using a Linear Predictive Coding (LPC) technique prior to transmission. The parameters that describe the short-term spectral behavior of the speech signal are received and then applied to and processed by a non-linear median processing block only on an occurrence of a predetermined number of transmission errors in the received LPC speech signal. The median-processed short term speech parameters are subsequently employed, together with a received excitation signal, in a synthesis filter to synthesize a speech signal of improved quality over what would be obtained if the short term speech parameters were not median processed to compensate for the transmission errors.

An efficient stochastically excited linear predictive coding algorithm for high quality low bit rate transmission of speech

The Stochastically Excited Linear Prediction (SELP) algorithm for speech coding offers good performance at bit rates as low as 4.8 kbit/s. Linear Predictive Coding (LPC) techniques remove the short-term correlation from the speech. A pitch loop removes long-term correlation, producing a noise-like residual, which is vector quantized. Information describing the LPC filter coefficients, the long-term predictor, and the vector quantization is transmitted. In this paper, we describe improvements to the SELP algorithm which result in better speech quality and higher computational efficiency. In its closed-loop form, the pitch loop can be interpreted as a vector quantization of the desired excitation signal with an adaptive codebook populated by previous excitation sequences. To better model the non-stationarity of speech we extend this adaptive codebook with a special set of candidate vectors which are transform of other codebook entries. The second stage vector quantization is performed using a fixed stochastic codebook. In its original form, the SELP algorithm requires excessive computational effort. We employ a new recursive algorithm which performs a very fast search through the adaptive codebook. In this method, we modify the error criterion, and exploit the resulting symmetries. The same fast vector quantization procedure is applied to the stochastic codebook.

Speech synthesis system utilizing variable frame rate

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The generation of propeller modulation sounds for sonar trainers

Sounds of propeller modulation were synthesized using linear predictive coding (LPC) techniques resident in digital signal processing hardware for the purpose of training submarine sonar operators. The combination of LPC techniques and digital sound generation produces realistic propeller modulation sounds which can be conveniently varied by the instructor to characterize the type, speed, and depth of surface and underwater vehicles. The synthesized sounds were generated with digital LPC (all‐pole) filters similar to those commonly used in speech synthesizers. The coefficients to drive the LPC filters were calculated from spectral models which were formulated from the cavitation index, KTIP. The cavitation index was calculated based on the instructor's commands of the vehicle type, speed, and depth. Three different LPC filter designs were implemented to evaluate the computational efficiency and sound realism of each approach. The first design was a standard 12th‐order LPC filter which was not capable of changing the spectrum over short time periods. The second design was a low order LPC filter with coefficients which changed quickly with time. The last design was a baseband LPC filter modulated by a time‐varying sinusoidal carrier. Various propeller modulation sounds will be played to demonstrate the quality and flexibility of these synthesizer techniques for training sonar operators. [Work performed in support of NUWES, Keyport, WA.]

A geometric description of stable linear predictive coding digital filters (Corresp.)

If the linear predictive coding filter coefficients are imagined as specifying a point in an n -dimensional space, then the set of all stable filters fills up a certain region in this space. The shape of this region is determined for low-order filters, and general properties are deduced for higher-order filters.