Modified Covariance Method Research Articles

Regularized Modified Covariance Method for Spectral Analysis of Bone-Conducted Speech

Regularized Modified Covariance Method for Spectral Analysis of Bone-Conducted Speech

Packet Loss Compensation for VoIP through Bone‐Conducted Speech Using Modified Linear Prediction

In this paper, we compare air‐conducted (AC) speech with bone‐conducted (BC) speech for the purpose of utilizing them in packet loss concealment (PLC) for speech quality of voice over internet protocol (VoIP). Instead of the autocorrelation method of linear prediction (LP), which was utilized in the conventional PLC techniques, we employ the modified covariance (MC) method. The MC method provides accurate LP estimation from short input data samples and avoids the numerical problem the autocorrelation method suffers from. The lost frame is compensated from both forward and backward directions in which linear gain and weighting are applied. When BC speech is used as the input speech data for PLC in the case where the speech sender is in noisy environments, BC speech behaves more accurately than the corresponding AC speech, resulting in an excellent performance of the LP‐based PLC technique. This unveils a useful use of BC speech in speech information systems. Experiments show that in severe noise environments of AC speech‐to‐noise ratio being less than 10 dB, BC speech is superior to AC speech for PLC. It is also shown that the transmitted BC speech is more accurately reconstructed for PLC than transmitted AC speech is done. © 2023 The Authors. IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Accurate Characterization of the Adhesive Layer Thickness of Ceramic Bonding Structures Using Terahertz Time-Domain Spectroscopy.

Ceramic adhesive structures have been increasingly used in aerospace applications. However, the peaks of the signal on the upper and lower surface of the adhesive layer are difficult to measure directly due to the thin thickness of the adhesive layer and the effect of the attenuation dispersion of the ceramic layer. Thus, the existing non-destructive testing techniques have been ineffective in detecting adhesive quality. In this paper, the thickness of the adhesive layer is measured using terahertz time-domain spectroscopy. A sparse deconvolution method is proposed for the terahertz time-domain spectral signal of ceramic adhesive structures with different adhesive layer thicknesses. The results show that the methods proposed in this paper can realize the separation of reflection signals for glue layers with a thickness of 0.20 mm. By comparing with a wavelet denoising method and a modified covariance method (AR/MCM), the effectiveness of the sparse deconvolution method in estimating the thickness of the glue layer is demonstrated. This work will provide the theoretical and experimental basis for using terahertz time-domain spectroscopy to detect the homogeneity of ceramic adhesive structures.

Accurate Heart Rate and Respiration Rate Detection Based on a Higher-Order Harmonics Peak Selection Method Using Radar Non-Contact Sensors.

Vital signs such as heart rate and respiration rate are among the most important physiological signals for health monitoring and medical applications. Impulse radio (IR) ultra-wideband (UWB) radar becomes one of the essential sensors in non-contact vital signs detection. The heart pulse wave is easily corrupted by noise and respiration activity since the heartbeat signal has less power compared with the breathing signal and its harmonics. In this paper, a signal processing technique for a UWB radar system was developed to detect the heart rate and respiration rate. There are four main stages of signal processing: (1) clutter removal to reduce the static random noise from the environment; (2) independent component analysis (ICA) to do dimension reduction and remove noise; (3) using low-pass and high-pass filters to eliminate the out of band noise; (4) modified covariance method for spectrum estimation. Furthermore, higher harmonics of heart rate were used to estimate heart rate and minimize respiration interference. The experiments in this article contain different scenarios including bed angle, body position, as well as interference from the visitor near the bed and away from the bed. The results were compared with the ECG sensor and respiration belt. The average mean absolute error (MAE) of heart rate results is 1.32 for the proposed algorithm.

Thickness characterization of multi-layer coated steel by terahertz time-of-flight tomography

Terahertz time-of-flight tomography is employed to characterize the individual layers in a multilayer coating on steel substrates. The multilayers, containing up to three layers, consist first of a cataphoretic layer, sealer, and varnish progressing upward from the steel substrate. The individual layer thicknesses were reconstructed from the experimental data using autoregressive spectral extrapolation based on the modified covariance method (AR/MCM), which extrapolates the frequency components of the transfer function into the low signal-to-noise-ratio (SNR) regions from the high SNR regions. Due to the high sensitivity of AR/MCM to the presence of noises, wavelet denoising, in some cases, was needed to precondition the data prior to application of AR/MCM. The method is verified numerically and experimentally, showing that it can reconstruct individual layers in the 10-μm range, and good agreement is found between the thicknesses of individual layers extracted by AR/MCM and characterized by eddy-current measurements.

Terahertz Time-of-Flight Tomography Beyond the Axial Resolution Limit: Autoregressive Spectral Estimation Based on the Modified Covariance Method

We present a time-of-flight tomography method for exceeding the naive axial (i.e., depth) resolution limit of terahertz (THz) deconvolution by autoregressive spectral extrapolation (AR) based on the modified covariance method (AR/MCM). In contrast to Wiener filtering combined with wavelet denoising, AR/MCM does not discard any frequency components in the low signal-to-noise (SNR) regions of the measured data, and unlike the AR approach based on the Burg method (AR/BM), no peak splitting (single peaks in the impulse response function appearing as double peaks) as well as frequency bias (spectral peaks shifted with respect to their correct positions) is observed after deconvolution. After verifying the advantages of AR/MCM over Wiener filtering in conjunction with wavelet denoising as well as over AR/BM, using synthetic data, AR/MCM is employed to reconstruct a single layer of mill scale on a steel coupon from experimental THz time-of-flight tomography data. The reconstruction shows good agreement with the film thickness obtained from destructive cross-sectional measurements. In addition, unlike AR/BM, optimizing the parameters to obtain stable reconstruction is straightforward relying of Akaike’s information criterion suggesting that AR/MCM can be an easier to implement for THz nondestructive characterization of stratigraphy under noisy conditions, particularly when estimates of the stratigraphy may not a priori be available.

Comparison of Signal Processing Algorithms for High-Resolution FMCW Terahertz Thickness Measurements

When using the frequency-modulated continuous-wave (FMCW) approach for layer thickness determination of dielectrics, such as multilayer pipe walls, the resolution is usually limited by the bandwidth. For thinner layers, we compare the measurement signal with potential model signal outputs. Since our approach results in high computation efforts by using brute force optimization, spectral estimation may present an alternative for high resolution distance and hence thickness determination. In this contribution, we compare results of a parametric algorithm (modified covariance method) and a subspace method (multiple signal classification, MUSIC) with the results of our approach.

EEG power spectrum as a biomarker of autism: a pilot study

The aim of this study was to assess whether power spectrum estimates of electroencephalogram (EEG) can be used as a biomarker for autism spectrum disorder (ASD). EEG collected from ASD and control participants performing a short-memory task was preprocessed to remove noise and artefacts, power spectral density (PSD) estimates were obtained by the modified covariance method and used as the study features that were subjected next to the Kruskal-Wallis analysis of differences. After verifying that the features (PSD estimates) were statistically different between the autistic and control subjects, these PSD estimates were classified using the 'k nearest neighbour' (KNN) classification algorithm with the average accuracy of 89.29%. This result indicates that EEG of autistic and control individuals may contain statistically different features; therefore, EEG power spectrum may be used as a biomarker for autism.

EEG power spectrum as a biomarker of autism: a pilot study

The aim of this study was to assess whether power spectrum estimates of electroencephalogram (EEG) can be used as a biomarker for autism spectrum disorder (ASD). EEG collected from ASD and control participants performing a short-memory task was preprocessed to remove noise and artefacts, power spectral density (PSD) estimates were obtained by the modified covariance method and used as the study features that were subjected next to the Kruskal-Wallis analysis of differences. After verifying that the features (PSD estimates) were statistically different between the autistic and control subjects, these PSD estimates were classified using the 'k nearest neighbour' (KNN) classification algorithm with the average accuracy of 89.29%. This result indicates that EEG of autistic and control individuals may contain statistically different features; therefore, EEG power spectrum may be used as a biomarker for autism.

Phase correction autocorrelation-based frequency estimation method for sinusoidal signal

To improve the precision of frequency estimation, a phase correction autocorrelation-based frequency estimation method for sinusoidal signal is proposed. Firstly, a phase correction autocorrelation is developed to reduce the effect of non-half period sampling signal on autocorrelation. Secondly, reference signal is generated according to phase correction autocorrelation signal. Finally, an error function between phase correction autocorrelation signal and reference signal is constructed and frequency estimation is obtained by calculating the minimum of error function. To demonstrate the superiority of the proposed method, computational complexity is analyzed, simulations and experiments are performed. Theoretical analysis and simulations demonstrate that the proposed method reduces the influence of non- half period sampling signal and has better frequency estimation performance than the interpolated DFT method, the modified covariance method for correlation, the two-stage autocorrelation method and the expanded autocorrelation method. The measurement experiments of LFMCW radars validate the effectiveness and superiority of the proposed method in practice.

Objective evaluation of nasal breathing based on rhinomanometric data

Currently, there are many objective diagnostic methods for nasal breathing difficulties. The main conventional method in otolaryngological practice is active rhinomanometry. The value of nasal resistance is considered as a diagnostic parameter of active anterior rhinomanometry. However, the results of the rhinomanometric measurements depend on race, age, gender, body mass index, and growth, etc. Thus, there is no evaluated criterion of physiological norm of nasal breathing according to the data from rhinomanometric studies. This fact significantly complicates the process of diagnosis and reduces diagnostic efficiency of rhinomanometry methods. For solving this problem, it is proposed to use additional spectral estimation data of the rhinomanometric studies using the modified covariance method. For this purpose, the spectral estimation software module is added to the software and hardware for rhinomanometric studies. The use of an advanced hardware and software system for rhinomanometric studies in clinical practice allows improving the diagnostic value of the active anterior rhinomanometry method.

An exact analysis of Modified Covariance frequency estimation algorithm based on correlation of single-tone

The Modified Covariance (MC) algorithm, which exploits the time-domain correlation of sinusoidal signals, has been shown to be a simple but efficient method for the frequency estimation of single-tone. Recently MC method based on correlation sequence was investigated. Both simulation and theoretical analyses show that it is more robust to noise than the conventional MC method that is directly based on the recorded signal [6]. However, the analysis in [6] is only valid when lower-order lags of correlations are applied. In this communication we provide new analytical result to tackle this difficult.

Improved Variant of Pisarenko Harmonic Decomposition for Single Sinusoidal Frequency Estimation

It is well known that Pisarenko's frequency estimate for a single real tone can be computed easily using the sample covariance with lags 1 and 2. In this Letter, we propose to use alternative covariance expressions, which are inspired from the modified covariance (MC) frequency estimator, in Pisarenko's algorithm. Computer simulations are included to corroborate the theoretical development of the variant and to demonstrate its superiority over the MC and Pisarenko's methods.

Applications of parametric spectral estimation methods on detection of power system harmonics

Harmonics are the major power quality problems in industrial and commercial power systems. Several methods for detection of power system harmonics have been investigated by engineers due to increasing harmonic pollution. Since the non-integer multiple harmonics (inter and sub-harmonics) become wide spread, the importance of harmonic detection has increased for sensitive filtration. This paper suggests parametric spectral estimation methods for the detection of harmonics, inter-harmonics and sub-harmonics. Yule Walker, Burg, Covariance and Modified Covariance methods are applied to generate cases. Not only integer multiple harmonics but also non-integer multiple harmonics are successfully determined in the computer simulations. Further, performances of proposed methods are compared with each other in terms of frequency resolution.

Comparison of AR and Welch Methods in Epileptic Seizure Detection

Brain is one of the most critical organs of the body. Synchronous neuronal discharges generate rhythmic potential fluctuations, which can be recorded from the scalp through electroencephalography. The electroencephalogram (EEG) can be roughly defined as the mean electrical activity measured at different sites of the head. EEG patterns correlated with normal functions and diseases of the central nervous system. In this study, EEG signals were analyzed by using autoregressive (parametric) and Welch (non-parametric) spectral estimation methods. The parameters of autoregressive (AR) method were estimated by using Yule-Walker, covariance and modified covariance methods. EEG spectra were then used to compare the applied estimation methods in terms of their frequency resolution and the effects in determination of spectral components. The variations in the shape of the EEG power spectra were examined in order to epileptic seizures detection. Performance of the proposed methods was evaluated by means of power spectral densities (PSDs). Graphical results comparing the performance of the proposed methods with that of Welch technique were given. The results demonstrate consistently superior performance of the covariance methods over Yule-Walker AR and Welch methods.

A new data extrapolation algorithm for high resolution ISAR imaging

A new data extrapolation algorithm for high resolution radar imaging is presented. The backscattered data are modeled as an autoregressive process where the prediction coefficients are computed using 1D least-square lattice filters. Unlike the well-known Burg or modified covariance methods, least square lattice modeling yields different prediction coefficients for forward and backward directions. The proposed method does not need to satisfy Levinson recursion, i.e. does not suffer from the limitations of the Burg method such as spectral splitting or bias in the locations of the scattering centers. Moreover, due to its lattice structure it does not need any matrix inversion like the modified covariance method. Results obtained for an experimental target are included to confirm the proposed algorithm.

Study of the experimental performance of AR‐based data‐extrapolation algorithms for high‐resolution radar imaging

AbstractIn this letter, we compare the experimental performance of data‐extrapolation algorithms for high‐resolution radar imaging. The three methods considered in this paper are based on the autoregressive (AR) model, including the Burg method, the modified covariance method, and the modified covariance method, coupled with singular value decomposition. The performance of each algorithm in terms of extrapolation accuracy is compared using the radar cross‐section (RCS) data of an F117 model measured in the compact range facility. © 2001 John Wiley & Sons, Inc. Microwave Opt Technol Lett 31: 151–156, 2001.

Evaluation of the use of the modified-covariance method in HF radar ocean measurement

High-frequency (HF) radar systems are currently used to collect wave and current data. By applying the fast Fourier transform (FFT) method to the radar backscatter from the ocean surface, the Doppler spectrum is calculated, and from this the directional wave spectrum and wave parameter measurements are obtained. Because of the random nature of the ocean surface, spectral measurements are subject to random variability. To obtain stable estimates, each spectrum is calculated by averaging a number of FFT estimates. This method requires long data series, and problems may arise particularly in rapidly varying sea conditions. We consider the use of an alternative technique, the modified-covariance method, which can provide stable spectral estimates from short data sets. We show that the modified-covariance method with smaller data sets gives satisfactory results, comparable to those of the FFT method as usually applied, while the FFT method with smaller data sets performs poorly. Accurate wave parameter measurements are still obtained when the amount of data is reduced by a factor of four. The work suggests that an autoregressive model order of 30 and the use of 1500 data samples is sufficient for the accurate measurement of significant waveheight, mean period and wind direction.

An energy-constrained signal subspace method for speech enhancement and recognition in white and colored noises

In this paper, an energy-constrained signal subspace (ECSS) method is proposed for speech enhancement and automatic speech recognition under additive noise condition. The key idea is to match the short-time energy of the enhanced speech signal to the unbiased estimate of the short-time energy of the clean speech, which is proven very effective for improving the estimation of the noise-like, low-energy segments in continuous speech. The ECSS method is applied to both white and colored noises where the additive colored noise is modelled by an autoregressive (AR) process. A modified covariance method is used to estimate the AR parameters of the colored noise and a prewhitening filter is constructed based on the estimated parameters. The performances of the proposed algorithms were evaluated using the TI46 digit database and the TIMIT continuous speech database. It was found that the ECSS method can achieve very high word recognition accuracy (WRA) for the digits set under low SNR conditions. For continuous speech data set, this method helped to improve the SNR by 2–6 dB and the WRA by 13.7–45.5% for the white noise and 18.6–55.9% for the colored noise under various SNR conditions.

Application of the singular valve decomposition–Prony method for analyzing deep-level transient spectroscopy capacitance transients

In this article, a modified covariance method for analyzing deep-level transient spectroscopy (DLTS) capacitance transients using a combined singular value decomposition/Prony (SVD–Prony) method is presented. This combined method is based upon using the SVD method first to accurately estimate the number of exponentials contained in transient capacitance data, then the Prony method is applied to obtain an accurate estimate of the exponential time constants. Results are presented for simulated exponential data with additive white-Gaussian noise and for real DLTS data to demonstrate the applicability of the presented technique. In addition, a statistical analysis is performed to study the behavior of this technique and its effectiveness in extracting the capacitance parameters at different noise levels. Finally, the problem of multiple exponential detection is addressed.