K-singular Value Decomposition Algorithm Research Articles

A denoising algorithm based on ARIMA and competitive K-SVD for the diagnosis of rolling bearing faults

Rolling bearings are extensively employed in industrial production as essential support components for rotating machinery. However, under conditions of high load and harsh operation, bearings are highly susceptible to failure. The weak vibration signals associated with these failures may be obscured by complex harmonic interference and strong noise, posing challenges for the accurate diagnosis of rolling bearing failures. In this paper, an autoregressive integrated moving average and competitive K-singular value decomposition (ARIMA-CK-SVD) algorithm is proposed to realize effective extraction of faulty pulse signals in a strong interference environment. First, the ARIMA model is used to preprocess the original signal to eliminate the interference of harmonic components. Second, a method is proposed for the adaptive selection of parameters in the ARIMA model, with consideration given to the characteristics of K-SVD. Subsequently, a competitive mechanism is introduced during the dictionary update phase of the algorithm to adjust the pattern of atomic updates and eliminate noise atoms. Finally, the effectiveness of the ARIMA-CK-SVD has been validated through simulation experiments and engineering tests.

A novel gearbox local fault feature extraction method based on quality coefficient and dictionary learning

The performance of sparse decomposition is directly determined by the similarity between impact atoms and the actual fault impact waveform. The shift-invariant K-singular value decomposition (K-SVD) dictionary learning algorithm is capable of training impact patterns from vibration signals collected by sensors to construct impact atoms, thereby extracting fault impact components from the vibration signals. However, the impact pattern training using the shift-invariant K-SVD algorithm is influenced by the presence of harmonics and white noise in the gear transmission system vibration signals. To solve the above problems, a novel gearbox local fault feature extraction method based on the quality coefficient and dictionary learning is proposed in this paper. Firstly, the original signal is decomposed into a series of intrinsic mode functions (IMFs) by empirical mode decomposition. Then, a new quality coefficient is proposed by comprehensively considering the intensity of the impact, harmonics and noise components in each IMF, as well as the degree of correlation with the original signal. The IMF with the largest quality coefficient is used to train the impact pattern and solve the sparse coefficients. Finally, the orthogonal matching pursuit algorithm is adopted to solve the sparse coefficients, which are used to reconstruct the fault impact response signal from the dictionary. Simulation and experimental analysis demonstrate the superior performance of the proposed method compared to other state-of-the-art methods.

Partial Discharge Signal Denoising Algorithm Based on Aquila Optimizer–Variational Mode Decomposition and K-Singular Value Decomposition

Partial discharge (PD) is a primary factor leading to the deterioration of insulation in electrical equipment. However, it is hard for traditional methods to precisely extract PD signals in increasingly complex engineering environments. This paper proposes a new PD signal denoising method combining Aquila Optimizer–Variational Mode Decomposition (AO-VMD) and K-Singular Value Decomposition (K-SVD) algorithms. Firstly, the AO algorithm optimizes critical parameters of the VMD algorithm. For the PD signal overwhelmed by noise, the AO-VMD algorithm can decompose it and reconstruct it by using kurtosis. In this process, the majority of the noise is removed, and the characteristics of the original signal are shown. Subsequently, the K-SVD algorithm performs sparse decomposition on the signal after OA-VMD, constructs a learned dictionary, and captures the characteristics of the signal for continuous learning and updating. After the dictionary learning is completed, the best matching atoms from the dictionary are selected to precisely reconstruct the original noiseless signal. Finally, the proposed method is compared with three traditional algorithms, Adaptive Ensemble Empirical Mode Decomposition (AEEMD), SVD-VMD, and the Adaptive Wavelet Multilevel Soft Threshold algorithm, on the simulated signal and the actual engineering signal. The results both demonstrate that the algorithm proposed by this paper has superior noise reduction and signal extraction performance.

On-line monitoring of membrane fouling based on an improved electrical measurement method.

On-line monitoring of membrane fouling is essential in the water treatment process. Drawbacks such as low-sensitivity and off-line limitations limit the application of existing methods. An on-line monitoring method based on Electrical Resistance Tomography (ERT) sensors is put forward in this paper. The Particle Swarm Optimization with Simulated Annealing (PSO-SA) algorithm is used in optimizing the topologies of finite element models in order to decrease the ill-posedness of sensitivity matrices. The deep denoising extreme learning machine with an auto-encoder model and the K-singular value decomposition algorithm are used in ERT reconstruction to improve imaging quality. The lift-wavelet is adopted in measuring the permeate flux to improve measuring accuracy. The ERT pixel values of the membrane module and the result of flux are used to analyze the fouling status. The results of membrane fouling experiments demonstrate the following: (1) Based on the local ERT pixels, the "two stage" phenomenon of membrane fouling can be observed. (2) In the early stage, the fouling distribution of the localized membrane module is consistent with its ERT pixels. (3) The deposition process of foulants for the localized membrane module is synchronized with the variation of ERT pixels. (4) The integrity of the membrane module can be detected according to the ERT pixels. Therefore, the novel method can effectively reflect the membrane fouling process, especially in the early stages of membrane fouling.

Pulsation artifact removal from intra-operatively recorded local field potentials using sparse signal processing and data-specific dictionary.

Neural recordings frequently get contaminated by ECG or pulsation artifacts. These large amplitude components can mask the neural patterns of interest and make the visual inspection process difficult. The current study describes a sparse signal representation strategy that targets to denoise pulsation artifacts in local field potentials (LFPs) recorded intraoperatively. To estimate the morphology of the artifact, we first detect the QRS-peaks from the simultaneously recorded ECG trace as an anchor point. After the LFP data has been epoched with respect to each beat, a pool of raw data segments of a specific length is generated. Using the K-singular value decomposition (K-SVD) algorithm, we constructed a data-specific dictionary to represent each contaminated LFP epoch in a sparse fashion. Since LFP is aligned to each QRS complex and the background neural activity is uncorrelated to the anchor points, we assumed that constructed dictionary will be formed to mainly represent the pulsation artifact. In this scheme, we performed an orthogonal matching pursuit to represent each LFP epoch as a linear combination of the dictionary atoms. The denoised LFP data is thus obtained by calculating the residual between the raw LFP and its approximation. We discuss and demonstrate the improvements in denoised data and compare the results with respect to principal component analysis (PCA). We noted that there is a comparable change in the signal for visual inspection to observe various oscillating patterns in the alpha and beta bands. We also see a noticeable compression of signal strength in the lower frequency band (<13 Hz), which was masked by the pulsation artifact, and a strong increase in the signal-to-noise ratio (SNR) in the denoised data.Clinical Relevance- Pulsation artifact can mask relevant neural activity patterns and make their visual inspection difficult. Using sparse signal representation, we established a new approach to reconstruct the quasiperiodic pulsation template and computed the residue signal to achieve noise-free neural activity.

Compressed sampling of gearbox oil temperature signal based on an improved intrinsic mode function basis mixed-dictionary

The oil temperature signal of gearbox is always redundant in long-term use, which will occupy a lot of computer storage space. An improved intrinsic mode function basis mixed-dictionary K-singular value decomposition (IMDK-SVD) algorithm for compressed sampling of the aperiodic oil temperature signal is proposed. Firstly, an improved mixed-dictionary is designed for sparse representation of the aperiodic oil temperature signal, this mixed-dictionary is composed of a class-specific dictionary that extracts the commonness of the same group of data and a shared dictionary that extracts the difference of the different group of data. Secondly, the measurement matrix with variable-step sampling is proposed, and the sampling points is adaptively determined by the information entropy index of signal. Benefitting from these improvements, the advantages of high reconstruction accuracy and saving sampling points as possible are achieved, and the effectiveness of proposed method is verified based on the simulation signal and the experimental signal.

Interpretable deep dictionary learning for sound speed profiles with uncertainties.

Uncertainties abound in sound speed profiles (SSPs) measured/estimated by modern ocean observing systems, which impede the knowledge acquisition and downstream underwater applications. To reduce the SSP uncertainties and draw insights into specific ocean processes, an interpretable deep dictionary learning model is proposed to cater for uncertain SSP processing. In particular, two kinds of SSP uncertainties are considered: measurement errors, which generally exist in the form of Gaussian noises; and the disturbances/anomalies caused by potential ocean dynamics, which occur at some specific depths and durations. To learn the generative patterns of these uncertainties while maintaining the interpretability of the resulting deep model, the adopted scheme first unrolls the classical K-singular value decomposition algorithm into a neural network, and trains this neural network in a supervised learning manner. The training data and model initializations are judiciously designed to incorporate the environmental properties of ocean SSPs. Experimental results demonstrate the superior performance of the proposed method over the classical baseline in mitigating noise corruptions, detecting, and localizing SSP disturbances/anomalies.

Coherent Noise Attenuation by Kurtosis-Guided Adaptive Dictionary Learning Based on Variational Sparse Representation

In seismic data denoising, coherent noise is usually challenging to remove because it has similar features with signal. Therefore, attenuating coherent noise is of great importance for seismic data. In order to obtain a good denoising result, it is necessary to fully consider the features of coherent noise and choose the appropriate denoising methods. Here, we propose a kurtosis-guided adaptive dictionary learning (KGADL) algorithm based on variational sparse representation model. First, the variational sparse representation is to construct initial dictionary that depends on the seismic data, so that it can accurately contain the features of the seismic data, so as to improve the accuracy of sparse representation. Then, we use the K-singular value decomposition (K-SVD) algorithm to update the dictionary and introduce kurtosis to measure each atom after updating. Atoms with high kurtosis values usually exhibit strong irregularities and are considered noise, while atoms with low kurtosis values have more feature of valid waves and are considered signal. The atoms with low kurtosis values characterizing valid signal are retained to get the special dictionary that adapts to the complexity of the input seismic data, and this dictionary is used to suppress the coherent noise in seismic data. Finally, the denoising performance of the proposed method is illustrated by examples of both synthetic and field seismic data.

Fault Diagnosis of Rotating Equipment Bearing Based on EEMD and Improved Sparse Representation Algorithm

Aiming at the problem that the vibration signals of rolling bearings working in a harsh environment are mixed with many harmonic components and noise signals, while the traditional sparse representation algorithm takes a long time to calculate and has a limited accuracy, a bearing fault feature extraction method based on the ensemble empirical mode decomposition (EEMD) algorithm and improved sparse representation is proposed. Firstly, an improved orthogonal matching pursuit (adapOMP) algorithm is used to separate the harmonic components in the signal to obtain the filtered signal. The processed signal is decomposed by EEMD, and the signal with a kurtosis greater than three is reconstructed. Then, Hankel matrix transformation is carried out to construct the learning dictionary. The K-singular value decomposition (K-SVD) algorithm using the improved termination criterion makes the algorithm have a certain adaptability, and the reconstructed signal is constructed by processing the EEMD results. Through the comparative analysis of the three methods under strong noise, although the K-SVD algorithm can produce good results after being processed by the adapOMP algorithm, the effect of the algorithm is not obvious in the low-frequency range. The method proposed in this paper can effectively extract the impact component from the signal. This will have a positive effect on the extraction of rotating machinery impact features in complex noise environments.

Compressed Sensing Based on K-SVD for Brillouin Optical Fiber Distributed Sensors

The continuous operation of Brillouin optical fiber distributed sensors requires the acquisition and processing of large amount of data that imposes ample pressure on the data acquisition systems and storage devices. To overcome such limitations, the compressed sensing (CS) method based on K-Singular value decomposition (K-SVD) algorithm as transform is proposed in this paper for the accurate extraction of sensing information with less data acquired from Brillouin optical fiber distributed sensors without the hardware modification. In such algorithm, the parameters of the K-SVD dictionary are selected first through simulation and the signals are sampled and reconstructed successfully. Then, we apply the regularized orthogonal matching pursuit (ROMP) reconstruction algorithm to effectively reconstruct the experimental data obtained with different sampling rates, frequency steps and test temperatures. We also compare the performances of the CS method based on K-SVD with that based on discrete cosine transform (DCT), discrete Fourier transform (DFT) and principle component analysis (PCA) as transforms. The experimental results show that the proposed method can effectively reconstruct the experimental signals with much reduced amount of data as compared to other methods. The proposed method also provides satisfactory measurement accuracy in extracting sensing information. Therefore, the K-SVD based CS method can be an attractive alternative to make Brillouin optical fiber distributed sensors more suitable for faster operation.

Denoising Marine Controlled Source Electromagnetic Data Based on Dictionary Learning

Marine controlled source electromagnetic (CSEM) is an efficient method to explore ocean resources. The amplitudes of marine CSEM signals decay rapidly with the measuring offsets. The signal is easily contaminated by various kinds of noise when the offset is large. These noise include instrument internal noise, dipole vibration noise, seawater motion noise and environmental noise Suppressing noise is the key to improve data quality and interpretation accuracy. Sparse representation based denoising method has been used for denoising for a long time. provides a new way to remove noise. Under the framework of sparse representation, the denoising effect is closely related to the chosen transform matrix. This matrix is called dictionary and its column named atom. In general, the stronger the correlation between signal and dictionary is, the sparser representation will be, and further the better the denoising effect will be. In this article, a new method based on dictionary learning is proposed for marine CSEM denoising. Firstly, the signal segments suffering little from noise are captured to compose the training set. Then the learned dictionary is trained from the training set via K-singular value decomposition (K-SVD) algorithm. Finally, the learned dictionary is used to sparsely represent the contaminated signal and reconstruct the filtered one. The effectiveness of the proposed approach is verified by a synthetic data denoising experiment, in which windowed-Fourier-transform (WFT) and wavelet-transform (WT) denoising methods and three dictionaries (discrete-sine-transform (DST) dictionary, DST-wavelet merged dictionary and the learned dictionary) under a sparse representation framework are tested. The results demonstrate the superiority of the proposed dictionary-learning-based denoising method. Finally, the proposed approach is applied to field data denoising process, coupled with DST and DST-wavelet dictionaries based denoising methods. The outcomes further proves that the propsoed approach is effective and superior for marine CSEM data denoising.

Bayesian K-SVD for H and E blind color deconvolution. Applications to stain normalization, data augmentation and cancer classification.

Stain variation between images is a main issue in the analysis of histological images. These color variations, produced by different staining protocols and scanners in each laboratory, hamper the performance of computer-aided diagnosis (CAD) systems that are usually unable to generalize to unseen color distributions. Blind color deconvolution techniques separate multi-stained images into single stained bands that can then be used to reduce the generalization error of CAD systems through stain color normalization and/or stain color augmentation. In this work, we present a Bayesian modeling and inference blind color deconvolution framework based on the K-Singular Value Decomposition algorithm. Two possible inference procedures, variational and empirical Bayes are presented. Both provide the automatic estimation of the stain color matrix, stain concentrations and all model parameters. The proposed framework is tested on stain separation, image normalization, stain color augmentation, and classification problems.

Bearing Fault Feature Extraction Based on Adaptive OMP and Improved K-SVD

The condition of the bearing is closely related to the condition and remaining life of the rotating machine. Targeting the problem of the large number of harmonic signals and noise signals during the operation of rolling bearings, and given that it is difficult to identify the fault in time, an adaptive orthogonal matching pursuit algorithm (OMP) and an improved K-singular value decomposition (K-SVD) for bearing fault feature extraction are proposed. An adaptive OMP algorithm is applied, which uses the Fourier dictionary to improve the solution method of the OMP algorithm so that it can separate the harmonic components in the signal faster and more accurately. At the same time, the stopping criterion of the adaptive sparsity is improved in dictionary learning. There is no need to manually set the sparsity in the algorithm initialization process, which avoids the problem of algorithm performance degradation due to improper sparsity settings, and improves the efficiency of the K-SVD algorithm. As shown by theoretical verification, algorithm comparison, and experimental comparisons, the algorithm has certain advantages in fault feature extraction during rolling bearing operation, and the algorithm still has considerable practical value in long-duration and strong noise environments.

Wavefront reconstruction method for aero-optical distortion based on compressed sensing.

The wavefront sensor plays an important role in the adaptive optics (AO) system for aero-optical distortion correction. However, the bandwidth of the current data interfaces of wavefront sensors, as one of the key factors, limits applications of the AO system in extremely high-frequency aero-optical distortion correction, leading to unsatisfactory performance. In this paper, a framework for wavefront data compression using compressed sensing is established to improve the correction ability of the AO system, and a disturbed Zernike gradient dictionary (DZGD) learning over the k-singular value decomposition algorithm is proposed for achieving good performance in the compression of aero-optical wavefront data. Based on the proposed DZGD, a method for aero-optical distortion data compression and wavefront reconstruction is developed that can efficiently reduce the amount of data in the information channel without degradation of the correction effect in aero-optical distortion correction. The compressibility of aero-optical distortions over the DZGD is analyzed in detail by numerical simulations. In addition, the selection criteria of the measurement matrix and the anti-noise characteristic of the method are also discussed. Data compression using our method is feasible and highly adaptable in the correction of aero-optical distortions, and exhibits stronger resistance against detector noise compared with using the conventional dictionary.

ENIC: Ensemble and Nature Inclined Classification with Sparse Depiction based Deep and Transfer Learning for Biosignal Classification

The electrical activities of the brain are recorded and measured with Electroencephalography (EEG) by means of placing the electrodes on the scalp of the brain. It is quite a famous and versatile methodology utilized in both clinical and academic research activities. In this work, sparse depiction is initially incorporated to the EEG signals by means of using K-Singular Value Decomposition (K-SVD) algorithm and the features are extracted by means of using Self-Organizing Map (SOM) technique. The extracted features are initially classified with Extreme Learning Machine (ELM) and the proposed classification versions of ELM such as Ensemble ELM model and Nature Inclined ELM Model. The proposed ensemble ELM model makes use of the combination of Modified Adaboost. RT based on wavelet thresholding with ELM. The proposed Nature Inclined ELM makes use of the combination of some famous swarm intelligence algorithms such as Genetic Algorithm based ELM (GA-ELM), Particle Swarm Optimization based ELM (PSO-ELM), Ant Colony Optimization based ELM (ACO-ELM), Artificial Bee Colony based ELM (ABC-ELM) and Glowworm Swarm Optimization based ELM (GSO-ELM). The​ extracted features are also classified with deep learning methodology by means of utilizing an incidental Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN). Another famous methodology using Non-negative Matrix Factorization (NMF) and Affinity Propagation Congregation based Mutual Information (APCMI) with transfer learning techniques is also proposed and implemented once the sparse modelling is done and the results are analysed. The proposed methodology is implemented for two EEG datasets such as epilepsy dataset and schizophrenia dataset and a comprehensive analysis is done with very promising results.

SAR Image Change Detection Based on Joint Dictionary Learning With Iterative Adaptive Threshold Optimization

Synthetic aperture radar (SAR) image change detection is still a challenge due to inherent speckle noise and scarce datasets. This article proposes a joint-related dictionary learning algorithm based on the k-singular value decomposition (K-SVD) algorithm called JR-KSVD and an iterative adaptive threshold optimization (IATO) algorithm for unsupervised change detection. The JR-KSVD algorithm adds dictionary correlation learning to the K-SVD algorithm to generate a uniform initial dictionary for dual-temporal SAR images, thereby reducing the instability of sparse representations due to atomic correlations and enhancing the extraction of image edges and details. The IATO approach employs thresholds obtained by the &#x201C;difference-log ratio&#x201D; fusion image for indefinite residual energy minimization iterations to gradually shrink the threshold variation range and finally generate the change images, which have a high degree of adaptivity and strong real-time performance. Finally, experiments on six real datasets demonstrate that the proposed algorithm exhibits superior detection performance and robustness against some state-of-the-art algorithms.

Novel Machine Learning for Human Actions Classification Using Histogram of Oriented Gradients and Sparse Representation

Recognition of human actions is a trending research topic as it can be used for crucial medical applications like life care and healthcare. In this research, we propose a novel machine learning algorithm for the classification of human actions based on sparse representation theory. In the proposed framework, the input videos are initially partitioned into several temporal segments of a predefined length. From these temporal segments, the key-cuboids are then obtained. These cuboids are obtained based on the locations having maximum variation in orientation. From these regions, key-cuboids are extracted. From the key-cuboids, Histogram of Oriented Gradient (HOG) features are extracted. This new descriptor has the capability to express the dynamic features in the action videos. Using these features, a single shared dictionary is created from the videos belonging to different classes using K-Singular Value Decomposition (K-SVD) algorithm. This dictionary has the combined features of all the action classes. This shared dictionary is generated during the training phase. During the testing phase, the features belonging to a test class is classified using a novel Sparse Representation Modeling based Action Recognition (SRMAR) Algorithm using Orthogonal Matching Pursuit (OMP) and the shared dictionary. The proposed framework was evaluated using popular benchmark action recognition datasets like KTH dataset, Olympic dataset and the Hollywood dataset. The results obtained using these datasets were represented in the form of a confusion matrix. Evaluation was performed using metrics like overall classification accuracy, specificity, precision, recall and F-score that were obtained from the confusion matrix. This system achieved a high specificity of about 99.52%, 99.16% and 96.15% for the KTH dataset, Olympic dataset and the Hollywood datasets, respectively. Similarly, the proposed framework attained very good precision of 97.64%, 90.46% and 73.39% for the KTH dataset, Olympic dataset and the Hollywood datasets, respectively. Also, the average value of recall achieved was 97.58%, 90.86% and 74.09% for the KTH dataset, Olympic dataset and the Hollywood datasets, respectively. It was also observed that the proposed machine learning algorithm achieved outstanding results compared to the existing state-of-the-art human action recognition frameworks in the literature.

A dictionary learning method with atom splitting for seismic footprint suppression

The acquisition footprint causes serious interference with seismic attribute analysis, which severely hinders accurate reservoir characterization. Therefore, acquisition footprint suppression has become increasingly important in industry and academia. We have assumed that the time slice of 3D poststack migration seismic data mainly comprises two components: useful signals and the acquisition footprint. Useful signals describe the spatial distributions of geologic structures with local piecewise smooth morphological features. However, the acquisition footprint often behaves as periodic artifacts in the time-slice domain. In particular, the local morphological features of the acquisition footprint in marine seismic acquisition appear as stripes. Because useful signals and the acquisition footprint have different morphological features, we can train an adaptive dictionary and divide the atoms of the dictionary into two subdictionaries to reconstruct these two components. We have devised an adaptive dictionary learning method for acquisition footprint suppression in the time slice of 3D poststack migration seismic data. To obtain an adaptive dictionary, we use the K-singular value decomposition algorithm to sparsely represent the patches in the time slice of 3D poststack migration seismic data. Each atom of the trained dictionary represents certain local morphological features of the time slice. According to the difference in the variation level between the horizontal and vertical directions, the atoms of the trained dictionary are divided into two types. One type significantly represents the local morphological features of the acquisition footprint, whereas the other type represents the local morphological features of useful signals. Then, these two components are reconstructed using morphological component analysis based on different types of atoms, respectively. Synthetic and field data examples indicate that our method can effectively suppress the acquisition footprint with fidelity to the original data.

Application of improved double-dictionary K-SVD for compound-fault diagnosis of rolling element bearings

This paper proposes an improved double-dictionary K-singular value decomposition (IDDK-SVD) algorithm for the compound fault diagnosis of rolling element bearings under complex industrial environments. In the framework, a double-dictionary is first designed for respectively identifying and distinguishing compound-fault features. In addition, an atom selection strategy based on fault information is constructed by combining the Gini index with envelope periodic modulation intensity, which can simultaneously take the periodicity and impulsivity into consideration to find a set of atoms most related to fault for the update of double-dictionary. Finally, an estimation method for calculating the residual error in the sparse coding stage is also presented to meet a better result. Benefitting from these improvements, the proposed IDDK-SVD is effective in the application of compound-fault diagnosis, which is verified by both simulation signals and real signals collected from the locomotive bearing test rig.

Acquisition and Enhancement of Remote Human Vocal Signals Based on Doppler Radar

In order to achieve remote voice acquisition, a 9.75-GHz microwave Doppler radar with a high-sensitivity coherent homodyne demodulator is used to detect voice signals. However, the combined noise sources, including background noise and shock noise, are included in the Doppler audio radar-captured voice, which seriously damages the quality of speech. In the current work, a multi-stage enhancement method is used to enhance Doppler audio radar-captured voice. The first stage is dictionary learning, which uses the K-singular value decomposition algorithm to train speech and noise dictionaries. The noise training data come from the Doppler audio radar-captured shock noise signals under the influence of different relative motion modulations. The second stage generates a position mask of the shock noise. The third stage is the removal of background noise and shock noise. The removal of background noise adopts our proposed constrained low-rank sparse matrix decomposition algorithm based on power spectral density. The final speech spectrum with shock noise removed is obtained through sparse restoration using the LARC algorithm. The experimental results show that the 9.75-GHz Doppler audio radar can effectively acquire human voice signals, and the proposed multi-stage enhancement method can effectively remove the background noise and shock noise in the radar speech, thereby improving the quality of the audio radar voice. The obtained results imply the potential to implement practical Doppler audio radar systems for human voice signal acquisition.