Laser speech detection uses a non-contact Laser Doppler Vibrometry (LDV)-based acoustic sensor to obtain speech signals by precisely measuring voice-generated surface vibrations. Over long distances, however, the detected signal is very weak and full of speckle noise. To enhance the quality and intelligibility of the detected signal, we designed a two-sided Linear Prediction Coding (LPC)-based locator and interpolator to detect and replace speckle noise. We first studied the characteristics of speckle noise in detected signals and developed a binary-state statistical model for speckle noise generation. A two-sided LPC-based locator was then designed to locate the polluted samples, composed of an inverse decorrelator, nonlinear filter and threshold estimator. This greatly improves the detectability of speckle noise and avoids false/missed detection by improving the noise-to-signal-ratio (NSR). Finally, samples from both sides of the speckle noise were used to estimate the parameters of the interpolator and to code samples for replacing the polluted samples. Real-world speckle noise removal experiments and simulation-based comparative experiments were conducted and the results show that the proposed method is better able to locate speckle noise in laser detected speech and highly effective at replacing it.

We propose a method of non-blind speech watermarking based on direct spread spectrum (DSS) using a linear prediction scheme to solve sound distortion due to spread spectrum. Results of evaluation simulations revealed that the proposed method had much lower sound-quality distortion than the DSS method while having almost the same bit error ratios (BERs) against various attacks as the DSS method.

We previously proposed a lossless image coding scheme using example-based probability modeling, wherein the probability density function of image signals was dynamically modeled pel-by-pel. To appropriately estimate the peak positions of the probability model, several examples, i.e., sets of pels whose neighborhoods are similar to the local texture of the target pel to be encoded, were collected from the already encoded causal area via template matching. This scheme primarily makes use of non-local information in image signals. In this study, we introduce a prediction technique into the probability modeling to offer a better trade-off between the local and non-local information in the image signals.

The transmission on fronthaul links in the cloud radio access network has become a bottleneck with the increasing data rate. In this paper, we propose a novel two-stage compression scheme for fronthaul links. In the first stage, the commonly used techniques like cyclic prefix stripping and sampling rate adaptation are implemented. In the second stage, a structure called linear prediction coding with decision threshold (LPC-DT) is proposed to remove the redundancies of signal. Considering that the linear prediction outputs have large dynamic range, a two-piecewise quantization with optimized decision threshold is applied to enhance the quantization performance. In order to further lower the transmission rate, a multi-level successive structure of lossless polar source coding is proposed to compress the quantization output with low encoding and decoding complexity. Simulation results demonstrate that the proposed scheme with LPC-DT and LPSC offers not only significantly better compression ratios but also more flexibility in bandwidth settings compared with traditional ones.

This paper proposes a method based on modulation transfer function (MTF) to restore the power envelope of noisy reverberant speech by using a Kalman filter with linear prediction (LP). Its advantage is that it can simultaneously suppress the effects of noise and reverberation by restoring the smeared MTF without measuring room impulse responses. This scheme has two processes: power envelope subtraction and power envelope inverse filtering. In the subtraction process, the statistical properties of observation noise and driving noise for power envelope are investigated for the criteria of the Kalman filter which requires noise to be white and Gaussian. Furthermore, LP coefficients drastically affect the Kalman filter performance, and a method is developed for deriving LP coefficients from noisy reverberant speech. In the dereverberation process, an inverse filtering method is applied to remove the effects of reverberation. Objective experiments were conducted under various noisy reverberant conditions to evaluate how well the proposed Kalman filtering method based on MTF improves the signal-to-error ratio (SER) and correlation between restored power envelopes compared with conventional methods. Results showed that the proposed Kalman filtering method based on MTF can improve SER and correlation more than conventional methods.

In this paper, we propose a modified-error adaptive feedback active noise control (ANC) system using a linear prediction filter. The proposed ANC system is advantageous in terms of the rate of convergence, while maintaining stability, because it can reduce narrowband noise while suppressing disturbance, including wideband components. The estimation accuracy of the noise control filter in the conventional system is degraded because the disturbance corrupts the input signal to the noise control filter. A solution of this problem is to utilize a linear prediction filter. The linear prediction filter is utilized for the modified-error feedback ANC system to suppress the wideband disturbance because the linear prediction filter can separate narrowband and wideband noise. Suppressing wideband noise is important for the head-mounted ANC system we have already proposed for reducing the noise from a magnetic resonance imaging (MRI) device because the error microphones are located near the user's ears and the user's voice consequently corrupts the input signal to the noise control filter. Some simulation and experimental results obtained using a digital signal processor (DSP) demonstrate that the proposed feedback ANC system is superior to a conventional feedback ANC system in terms of the estimation accuracy and the rate of convergence of the noise control filter.

A method for efficiently estimating the time-varying spectra of nonstationary autoregressive (AR) signals is derived using an indefinite matrix-based sliding window fast linear prediction (ISWFLP). In the linear prediction, the indefinite matrix plays a very important role in sliding an exponentially weighted finite-length window over the prediction error samples. The resulting ISWFLP algorithm successively estimates the time-varying AR parameters of order N at a computational complexity of O(N) per sample. The performance of the AR parameter estimation is superior to the performances of the conventional techniques, including the Yule-Walker, covariance, and Burg methods. Consequently, the ISWFLP-based AR spectral estimation method is able to rapidly track variations in the frequency components with a high resolution and at a low computational cost. The effectiveness of the proposed method is demonstrated by the spectral analysis results of a sinusoidal signal and a speech signal.

In many wireless sensor applications, skyline monitoring queries that continuously retrieve the skyline objects as well as the complete set of nodes that reported them play an important role. This paper presents SKYMON, a novel energy-efficient monitoring approach. The basic idea is to prune nodes that cannot yield a skyline result at the sink, as indicated by their (error bounded) prediction values, to suppress unnecessary sensor updates. Every node is associated with a prediction model, which is maintained at both the node and the sink. Sensors check sensed data against model-predicted values and transmit prediction errors to the sink. A data representation scheme is then developed to calculate an approximate view of each node's reading based on prediction errors and prediction values, which facilitates safe node pruning at the sink. We also develop a piecewise linear prediction model to maximize the benefit of making the predictions. Our proposed approach returns the exact results, while deceasing the number of queried nodes and transferred data. Extensive simulation results show that SKYMON substantially outperforms the existing TAG-based approach and MINMAX approach in terms of energy consumption.

In this paper, we propose a switchable linear prediction (LP)/warped linear prediction (WLP) hybrid scheme for the transform coded excitation (TCX) coder, which is adopted as a core codec in AMR-WB+ and USAC. The proposed algorithm selects either an LP or WLP filter on a per-frame basis. To provide a smooth transitions between LP and WLP frames, a window switching scheme is developed using sine and rectangular windows. In addition, a Gaussian Mixture Model (GMM)-based classification module is used to determine the prediction mode. Through a subjective listening test it was confirmed that the proposed LP/WLP switching scheme offers improved sound quality.

The hyper H∞ filter derived in our previous work provides excellent convergence, tracking, and robust performances for linear time-varying system identification. Additionally, a fast algorithm of the hyper H∞ filter, called the fast H∞ filter, is successfully developed so that identification of linear system with impulse response of length N is performed at a computational complexity of O(N). The gain matrix of the fast filter is recursively calculated through estimating the forward and backward linear prediction coefficients of an input signal. This suggests that the fast H∞ filter may be applicable to linear prediction of the signal. On the other hand, an alternative fast version of the hyper H∞ filter, called the J-fast H∞ filter, is derived using a J-unitary array form, which is amenable to parallel processing. However, the J-fast H∞ filter explicitly includes no linear prediction of input signals in the algorithm. This work reveals that the forward and backward linear prediction coefficients and error powers of the input signal are indeed included in the recursive variables of the J-fast H∞ filter. These findings are verified by computer simulations.

This letter addresses the edge effect on a windowed discrete Fourier transform (WDFT)-based channel estimator for orthogonal frequency division multiplexing (OFDM) systems with virtual carriers in non-sample spaced channels and derives a sufficient condition to reduce the edge effect. Moreover, a modified WDFT-based channel estimator with multi-step linear prediction as an edge effect reduction technique is proposed. Simulation results show that it offers around 5 dB signal-to-noise ratio (SNR) gain over the conventional WDFT-based channel estimator at bit error rate (BER) of 10-3.

In this paper, we present and analyze a predictive closed-loop power control (CLPC) scheme which employs a comb-type sample arrangement to effectively compensate multiple power control group (PCG) delays over mobile fading channels. We consider both least squares and recursive least squares filters in our CLPC scheme. The effects of channel estimation error, prediction filter error, and power control bit transmission error on the performance of the proposed CLPC method along with competing non-predictive and predictive CLPC schemes are thoroughly investigated. Our results clearly indicate the superiority of the proposed scheme with its improved robustness under non-ideal conditions. Furthermore, we carry out a Monte-Carlo simulation study of a 55 square grid cellular network and evaluate the user capacity. Capacity improvements up to 90% are observed for a typical cellular network scenario.

Speech dereverberation is one of the most difficult tasks in acoustic signal processing. Of the various problems involved in this task, this paper highlights "over-whitening," which flattens the characteristics of recovered speech. This distortion sometimes happens when inverse filters are directly calculated from microphone signals. This paper reviews two studies related to this problem. The first study shows the possibility of compensating for such over-whitening to achieve precise speech-dereverberation. The second study presents a new approach for approximating the original speech by removing the effect of late reflections from observed reverberant speech.

By utilizing the second and fourth order linear prediction errors, a novel estimator for a single noisy sinusoid is devised. The frequency estimate is obtained from a solving a cubic equation and a simple root selection procedure is provided. Asymptotical variance of the estimated frequency is derived and confirmed by computer simulations. It is demonstrated that the proposed estimator is superior to the reformed Pisarenko harmonic decomposer, which is the improved version of Pisarenko harmonic decomposer.

A two-stage least square identification method is proposed for estimating ARMA (autoregressive moving average) coefficients from speech signals. A pulse-train like input sequence is often employed to account for the source effects in estimating vocal tract parameters of voiced speech. Due to glottal and radiation effects, the pulse train, however, does not represent the effective voice source. The authors have already proposed a simple but effective model of voice source for estimating AR (autoregressive) coefficients. This letter extends our approach to ARMA analysis to wider varieties of speech sounds including nasal vowels and consonants. Analysis results on both synthetic and natural nasal speech are presented to demonstrate the analysis ability of the method.

This paper proposes a maximum likelihood sequence estimation (MLSE) for the differential space-time block code (DSTBC) in cooperation with blind linear prediction (BLP) of fast frequency-flat fading channels. This method that linearly predicts the fading complex envelope derives its linear prediction coefficients by the method of Lagrange multipliers, and does not require data of decision-feedback or information on the channel parameters such as the maximum Doppler frequency in contrast to conventional ones. Computer simulations under fast fading conditions demonstrate that the proposed method with an appropriate degree of polynomial approximation is superior in BER performance to the conventional method that estimates the coefficients by the RLS algorithm using a training sequence.

This paper proposes low-complexity blind detection for orthogonal frequency division multiplexing (OFDM) systems with the differential space-time block code (DSTBC) under time-varying frequency-selective Rayleigh fading. The detector employs the maximum likelihood sequence estimation (MLSE) in cooperation with the blind linear prediction (BLP), of which prediction coefficients are determined by the method of Lagrange multipliers. Interpolation of channel frequency responses is also applied to the detector in order to reduce the complexity. A complexity analysis and computer simulations demonstrate that the proposed detector can reduce the complexity to about a half, and that the complexity reduction causes only a loss of 1 dB in average Eb/N0 at BER of 10-3 when the prediction order and the degree of polynomial approximation are 2 and 1, respectively.

It is well known that speech captured in a room by distant microphones suffers from distortions caused by reverberation. These distortions may seriously damage both speech characteristics and intelligibility, and consequently be harmful to many speech applications. To solve this problem, we proposed a dereverberation algorithm based on multi-channel linear prediction. The method is as follows. First we calculate prediction filters that cancel out the room reverberation but also degrade speech characteristics by causing excessive whitening of the speech. Then, we evaluate the prediction-filter degradation to compensate for the excessive whitening. As the reverberation lengthens, the compensation performance becomes worse due to computational accuracy problems. In this paper, we propose a new computation that may improve compensation accuracy when dealing with long reverberation.

Voice activity detection (VAD) determines whether a slice of waveform is voice or silence. The proposed VAD algorithm applying Elliptical Basis Function (EBF) neural networks uses k-means clustering and least mean square for the update algorithm. The error rates achieved by the EBF network have superior performance to those of G.729 Annex B and RBF.

A new system identification based method has been proposed for accurate estimation of vocal tract parameters. An often encountered problem in using the conventional linear prediction analysis is due to the harmonic structure of the excitation source of voiced speech. This harmonic characteristic is coupled with the estimation of autoregressive (AR) coefficients that results in difficulties in estimating the vocal tract filter. This paper models the effective voice source from the residual obtained through the covariance analysis in the first-pass which is then used as input to the second-pass least-square analysis. A better source-filter separation is thus achieved. The formant frequencies and corresponding bandwidths obtained using the proposed method for synthetic vowels are found to be accurate up to a factor of more than three (in percent) compared to the conventional method. Since the source characteristic is taken into account, local variations due to the positioning of analysis window are reduced significantly. The validity of the proposed method is also examined by inspecting the spectra obtained from natural vowel sounds uttered by high-pitched female speaker.