Autocorrelation Function Method Research Articles

Strength Characteristics of DMJ Piles Based on Indoor Tests

The Deep Cement Mixing Integrated Drilling, Mixing and Jetting (DMJ) method represents a novel approach to the construction of slurry piles in the Yellow River Floodplain, offering the potential to enhance the quality of these structures. In order to investigate the pile strength and its distribution characteristics under different conditions, an unconfined compressive strength test was conducted on DMJ pile core samples. The Kolmogorov-Smirnov (K-S) test was employed to evaluate the normal distribution characteristics of the strength, and the fluctuation of the pile strength was evaluated by the autocorrelation function method to elucidate the distribution characteristics. Moreover, a resistivity non-destructive test was conducted to ascertain the correlation between strength and resistivity and to perform supplementary strength testing. The distribution of the pile body strength is normal at the 5% level of significance. A clear correlation was observed between the strength of the pile core and depth, while the correlation between the strength of the pile outer side and depth was less pronounced. Additionally, a positive linear correlation was observed between resistivity and strength, which can be used to evaluate the strength of DMJ piles.

Pre-selection of solar analogues with differential rotation signature

The light curve of a star allows studying the variation of its brightness over time, being suitable for determining rotation periods, detecting planets, studying binary systems and differential rotation. The light curve analysis can be performed with the autocorrelation function (ACF), a robust mathematical tool in the detection of the stellar rotation period, by finding patterns in the photometric data. In this work, a sample of stars from the CoRoT mission is analyzed. The analysis is carried out with the ACF to carry out a pre-selection of stars that show signs of rotational modulation, a determining characteristic for the study of differential rotation. 17 stars were selected by visual inspection of the curves and by the ACF function. It was found that three stars have an excellent profile of autocorrelation function and rotational modulation, three have a good profile and the remaining eleven have inappropriate profiles for the study of differential rotation. The photometric time series analyzes show that the autocorrelation function method is very suitable as a previous criterion for the selection of candidate stars for the study of differential rotation, when they present exponential decay of the oscillation peaks of the light curve.

Enhancing the accuracy of the scale of fluctuation estimating for tunnel surrounding rock mass properties

The scale of fluctuation (SOF) is used to evaluate the correlation between two locations of geotechnical parameter, which is important for mineral exploration and engineering construction. In this study, the performances of conventional moment methods are firstly estimated, and then the exponential autocorrelation function method that can achieve the best performance in estimating SOF is selected. After that, the Torque Penetration Index (TPI) and the Field Penetration Index (FPI) obtained from the Yinsong Water Diversion Project are used to detect the scale effect of SOF and to quantify the influences of rock types and surrounding rock mass grades on SOF. As the spatial scale increases, the SOF values increase, and the uncertainty in the SOF of the surrounding rock mass properties increases. The SOF calculated using TPI is slightly higher than using FPI. The mean values of SOF for granite, tuff, diorite, and limestone are quite close at the 50 m–200 m scales, with coefficients of variation (COVs) of SOF range from about 0.2 to 0.4. There are differences in SOF between grade III and grade IV surrounding rock masses at the 50 m–500 m scales. The mean values of SOF in grade III surrounding rock mass are about 4%–15% higher than that in grade IV surrounding rock mass, and this difference in general increases as scale increases. The rock mass with better conditions has a stronger spatial correlation. The COVs of SOF for the two rock grades reached about 0.3–0.5, owning to the inherent spatial variability.

FRA–A new fast, robust, and automated pipeline for the detection and measurement of solar‐like oscillations in time‐series photometry of red‐giant stars

AbstractWe developed, tested, and validated a new Fast, Robust, and Automated (FRA) tool to detect solar‐like oscillations. FRA is based on the detection and measurement of the frequency of maximum oscillation power , without relying on the detection of a regular frequency spacing to guide the search. We applied the FRA pipeline to 254 synthetic power spectra representative of red giants observed by the Transiting Exoplanet Survey Satellite (TESS), as well as 1689 red giants observed by Kepler and 2344 red giants observed by TESS. We obtain a consistency rate for compared with existing measurements of 99% for Kepler red giants and of 98% for TESS red giants. We find that using as an input parameter to guide the search for the large frequency separation through the existing Envelope Auto Correlation Function method significantly improves the consistency of the measured in the case of TESS stars, allowing to reach a consistency rate above 99%. Our analysis reveals that we can expect to get consistent and measurements while minimizing both the false positive measurements and the non‐detections for stars with a minimum of four observed sectors and a maximum G magnitude of 9.5.

Ambient noise autocorrelation scheme for imaging theP-wave reflectivity of the lithosphere

SUMMARYAmbient noise autocorrelations can be used to reconstruct the seismic reflection response of the Earth structure beneath single stations using continuous recordings without the need for either active sources or earthquakes. In the last decade, this technique has emerged as an inexpensive approach with the potential to provide similar information to that from the classical receiver function (RF) analysis. Previous studies have located and mapped discontinuities at different crustal depths with ambient noise autocorrelations by applying different processing techniques. An ambient noise autocorrelation function (ACF) provides the body-wave reflectivity of the local structure, assuming a homogeneous distribution of noise sources. However, an effective method design is required in order to determine a reliable reflection response. Here, we review the theory behind the ambient noise ACF method and design a workflow to obtain the P-wave reflectivity with a special focus on the Moho depth. In particular, we calculate a smooth function to fit and subtract the zero-lag component in the time domain, that is, the large-amplitude signal near 0 lag time in the ACF. The zero-lag component can interfere with the reflection component, so its removal allows us to increase the frequency band to use. We bandpass filter the ACFs between 1 and 6 s. We also derive and apply a phase shift correction in the ACFs due to the integration of a homogeneously distributed noise field dominated by distant sources from deep below, such as teleseismic sources. Both linear and nonlinear, phase-weighted stacks are used. Linear stacking is used to identify the main interfaces since it ensures the linearity of the processing steps; nevertheless, nonlinear, phase-weighted stacking help validate the coherent signals. We test and apply our method to continuous vertical recordings from three stations in Ireland and five stations in different cratons and obtain clear P-wave reflection from the Moho and other crustal and upper-mantle discontinuities in most cases. However, noise coming from local heterogeneities, non-homogeneous distribution of the ambient noise sources or instrumental noise is also expected. Therefore, additional, a priori information is desirable to help identify key phases in single ACFs. We compute synthetic ACFs using P-wave velocity (VP) models from controlled-source profiles in Ireland. The relatively complex ACF traces obtained at the stations in Ireland show a close data-synthetic match for the Moho and mid-crustal discontinuities. The ACF traces from the stations in different cratons are directly compared with receiver functions showing overall agreement and offering complementary information on the origin of the signal.

An indicator to quantify the complexity of signals and surfaces based on scaling behaviors transcending fractal

In the well-established fractal scheme, the fractal dimension (D) is a central indicator of the complexity of fractal features. The D values of non-fractal signals and surfaces are 1 and 2, respectively, while there can be varieties in their complexities. In this study, the scaling characteristics of root-mean-squared roughness could exhibit a continuous variation transcending the boundary between fractal and non-fractal by using the roughness scaling extraction (RSE) method proposed in previous study, and an universal indicator (HRSE, Hurst exponent calculated by RSE method) to quantify the complexity of both fractal and non-fractal features is demonstrated. The actual signals (milling vibration) and surfaces (silver thin films) together with the artificial ones generated through Weierstrass–Mandelbrot (W–M) function were analyzed. Within the fractal scope, the calculated results with RSE method could be close to the ideal D values of W–M function with an accuracy higher than those of the traditional fractal methods (including Box-Counting, Higuchi, Katz, power spectral density, structure function, and autocorrelation function methods). For the non-fractal features, the complexity could also be quantified effectively by HRSE. Chatter could be recognize by HRSE of milling vibration signals, because it was larger than 1, from 0.5 to 1, and less than 0.5 in idling, stable milling and chatter milling states, respectively; For thin film surfaces, HRSE increased monotonically from 0.79 to 1.32 along with Sq increasing, indicating a strong positive correlation. The findings indicated that the scaling analysis could be utilized for both fractal and non-fractal features, which would be beneficial for various engineering applications.

Evaluation of the scale of fluctuation based on variance reduction method

Many uncertainties exist in geotechnical engineering. Spatial variability is a type of uncertainty that has received more and more attention in recent years. Scale of fluctuation (SOF) is a parameter for measuring the spatial variability of soil properties. To investigate the proper method for computing the SOF of soil properties, two types of methods (auto-correlation function method (ACM), and variance reduction method (VRM)) are compared theoretically and numerically, and the influence factors, such as number of data points involved in curve fitting for the auto-correlation function (ACF) or variance reduction function (VRF) and sampling interval, on the SOFs are analyzed. To improve the accuracy of curve fitting for the ACF or VRF, a weighted least squares (WLS) method is proposed, in which the weight of each data point of sample function decreases with the increasing of lag distance between two points in a random field. Three types of soil parameters, including the CPT, the physical, and the hydraulic parameters, are tested by in-situ and laboratory tests, and the SOFs of these parameters are computed. Computation of the SOFs proves that the VRM combined with the suggested WLS is a good method for computing the SOFs of soil properties. Comparison of the SOFs shows that there are negligible differences among the three types of soil parameters of clay layers in Hefei, which is further proved by the Wilcoxon test from a statistical point of view. By using the VRM combine with WLS, the SOFs of clay are computed based on 509 sets of CPT data from 34 sites in Hefei. The statistics of SOFs can give a reference for the selection of SOF for reliability analysis or reliability-based design of geo-structures with spatial variability.

An estimation method of fractal parameters on rough surfaces based on the exact spectral moment using artificial neural network

Fractal parameters (FP) significantly influence contact mechanics characteristics, such as contact stiffness, friction and wear. The existing methods liking the power spectral density (PSD), structure function (SF), autocorrelation function (ACF), box-counting (Box) and roughness length (RMS) methods are limited by the identification accuracy of FP (fractal dimension D and fractal roughness G). These methods are not suitable for global D interval (Kulesza et al., 2014; Feng et al., 2018), thereby resulting in large estimation errors (error (D) exceeds 40 %, and the value of G is incorrect). Thus, in this paper, a neural network FP estimation method based on the exact spectral moment is proposed. The main contribution of this paper is to establish the mapping relationship between the exact spectral moment and FP through the neural network. Firstly, the exact spectral moments, m0, m2 and m4, are derived via the differentiability of the series Weierstrass-Mandelbrot (WM) function in the finite interval. Secondly, a series of spectral moment parameter correspondence tables are generated according to the provided ideal fractal parameters. Then, the spectral moment is taken as the input layer and fractal parameters as the output layer, after which the BP neural network is optimized using the NSGA-II algorithm. Moreover, the mapping relationship between the FP and the spectral moment is established, thus obtaining the Fractal parameters estimation neural network (FPENN). The FP estimation model is trained by a relatively large amount of data and packaged to form a brand-new FP estimation method. Finally, the effectiveness of the proposed method is demonstrated by comparison with the existing methods. The results show that the relative error of D is <0.1 %, and the relative error of G is <25 %.

Noises in Double-Differenced GNSS Observations

Precise data processing from the Global Navigation Satellite Systems (GNSS) reference station network is mainly based on a combination of double-differenced carrier phase and code observations. This approach allows most of the measurement errors to be removed or reduced and is characterized as the most accurate method. However, creating observation differences between two receivers and two satellites increases the measurement noise of the observations by a factor of 2. As a result, it increases the impact of the incorrect definition of the noise characteristic on the results of the estimation of the unknowns in the positioning model. This is especially important in Multi-GNSS solutions, which integrate measurements from different systems, for which the stochastic parameters of observation may differ significantly. In this paper, the authors prepared a complex analysis of the noise type in double-differenced GNSS (GPS, GLONASS and Galileo) observations, both carrier phase and code ones, with a 1 s sampling interval. The Autocorrelation Function (ACF) method, the Lomb–Scargle (L-S) periodogram method, and the Allan variance (AVAR) method were used. The results that were obtained for the weekly set of measurement data showed that, depending on the system and type of observation, the noise level and its type are significantly different. Among the code measurements, the lowest noise levels were obtained for the GPS C5Q and Galileo C7Q/C8Q observations, with the standard deviations not exceeding ±10 cm, while the noisiest observations were for the GLONASS C1C and C2C signals, which had standard deviations of about ±90 cm and ±45 cm, respectively. For the carrier phase observations, each signal type was characterized by very similar noise levels of ±1.5–3.5 mm. The ACF analysis showed that 1 Hz double-differenced GNSS data can only be treated as being not correlated to time for carrier phase observations; for code observations, an irrelevant autocorrelation may be considered for measurement intervals greater than 20 s. Depending on the GNSS signals, the spectral index k varies in a range from −1.3 to −0.2 for code data and k = 0.0 in the case of phase data. Using the modified Allan deviation (MDEV) allows for specific noise types for each signal and GNSS system to be determined. All of the code observations were characterized by either flicker PM or white PM. In the case of the phase observations, they were all uniquely characterized by white PM (GPS and Galileo or by white PM and flicker PM (GLONASS).

Finite-temperature phonon dispersion and vibrational dynamics of BaTiO3 from first-principles molecular dynamics

A systematic investigation of the finite-temperature phonon dispersion, including its smearing and temperature effects, is carried out using autocorrelation function method together with first-principles molecular dynamics method along the [001] direction of $\mathrm{Ba}\mathrm{Ti}{\mathrm{O}}_{3}$, as an illustrating example of entropy-stabilized structures. A unique anharmonicity in the cubic phase mainly derived from the interactions between titanium and oxygen atoms is revealed, which provides extremely strong damping and smearing to longitudinal optical phonons but no discernible effect on the shift of phonon energies. The anharmonicity gives rise to a nearly constant density distribution in a cubic region around the equilibrium position of the relative motion of titanium atoms with respect to oxygen atoms. These results may help to gain a further insight into complex interactions in entropy-stabilized structures and provide an essential benchmark reference to the development of the promising machine-learning based molecular dynamics methods for the investigation of phonon properties.

Application of Artificial Intelligence Software based on Semantic Web Technology in English Learning and Teaching

&lt;p&gt;English teaching and learning in the information age need to be carried out with the help of intelligent system software to change the disadvantages of traditional English teaching as well as learning. According to the actual circumstances of teaching besides learning, this paper combines semantic Web technology and artificial intelligence technology to construct an English learning and teaching system. Moreover, this paper divides the entire speech data into frames in the speech processing, performs subsequent analysis in units of frames, and uses the autocorrelation function method in the time domain to extract the pitch of each frame of data corresponding to the English sentence. In addition, this paper combines the actual needs of English learning and teaching to construct system function modules, and designs experiments to analyze system performance and make statistics on user satisfaction. From the research results, it can be seen that the system constructed in this paper basically meets the actual needs of English autonomous learning and English teaching.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

The modern structurator: increased performance for calculating the structure function

The autocorrelation function is a statistical tool that is often combined with dynamic light scattering (DLS) techniques to investigate the dynamical behavior of the scattered light fluctuations in order to measure, for example, the diffusive behavior of transparent particles dispersed in a fluid. An alternative approach to the autocorrelation function for the analysis of DLS data has been proposed decades ago and consists of calculating the autocorrelation function starting from difference of the signal at different times by using the so-called structure function. The structure function approach has been proven to be more robust than the autocorrelation function method in terms of noise and drift rejection. Therefore, the structure function analysis has gained visibility, in particular in combination with imaging techniques such as dynamic shadowgraphy and differential dynamic microscopy. Here, we show how the calculation of the structure function over thousands of images, typical of such techniques, can be accelerated, with the aim of achieving real-time analysis. The acceleration is realized by taking advantage of the Wiener–Khinchin theorem, i.e., by calculating the difference of images through Fourier transform in time. The new algorithm was tested both on CPU and GPU hardware, showing that the acceleration is particularly large in the case of CPU.

Deformation Prediction of Foundation Pit Based on Exponential Power Product Model of Improved Algorithm

In order to improve the prediction accuracy of foundation pit deformation, an improved optimization algorithm of supply and demand-exponential power product foundation pit deformation prediction model (ISDO-EPP model) is proposed. Through six standard test functions and three application examples, the optimization ability of the ISDO algorithm is verified, and the optimization results are compared with those of basic supply demand optimization algorithm (SDO), whale optimization algorithm (WOA), grey wolf optimization algorithm (GWO), moth swarm algorithm (MSA), and particle swarm optimization algorithm (PSO). Taking the settlement prediction of three foundation pits as an example, the delay time and embedding dimension of each case are determined by autocorrelation function method and false nearest neighbor method, and input and output vectors are constructed to train and predict each model. The results show that the search ability of the ISDO algorithm is better than that of SDO and other five algorithms, and the ISDO algorithm has better search accuracy, global search ability, and robustness. The absolute values of average relative errors of the ISDO-EPP model for three cases are 0.73%, 3.36%, and 1.33%, respectively, which are better than ISDO-SVM and ISDO-BP models. It shows that the ISDO algorithm can effectively optimize the parameters of the EPP model, and the ISDO-EPP model is feasible and effective for deformation prediction.

Model identification of durability degradation process of concrete material and structure based on Wiener process

The degradation and damage of concrete material and structure during their service life are significantly characterized by randomness, so it is of great importance to establish a stochastic process model for the structural durability degradation. This study was based on Wiener process to identify the durability degradation of concrete structures. Firstly, two identification methods for Wiener process were introduced, including autocorrelation function method and probability statistics verification method. The former is a rough method for preliminary identification, and the latter is a strict method for accurate identification. According to the degradation processes and damage mechanisms of dynamic elastic modulus of concrete and section stiffness of reinforced concrete girder, and based on relevant data, autocorrelation function method and probability statistics verification method were used to identify whether the degradation processes of concrete dynamic elastic modulus and girder section stiffness conform to Wiener process. The results showed that the degradation process of concrete dynamic elastic modulus and the degradation process of section stiffness of reinforced concrete girder can be modeled with Wiener process. The conclusions contribute to the prediction of performance degradation and the maintenance for concrete structures in service.

Influence of characteristic parameters of signal on fault feature extraction of singular value method

The detection of mechanical fault signals by singular value decomposition is a commonly used method in fault diagnosis. The delay time of the fault signal time series and the rationality of the value of the phase space embedding dimension, as well as the fluctuation of the characteristic parameters of the fault signal, will cause the singular value decomposition method to have a greater impact on the accuracy of fault feature identification and diagnosis. In this article, the simulation model of the similarity signal is established by the combination of the autocorrelation function method and the Cao’s algorithm. Then, the delay time of the signal sequence and the optimal value of the embedded dimension are obtained through simulation. Next, using this method to study the fluctuation of the characteristic parameters such as the frequency, amplitude and initial phase of the signal, the relationship between the characteristic parameters of the signal and the singular value of the signal is obtained. Finally, through the experimental study of the pitting corrosion of the gear tooth surface, the vibration of the fault feature is obtained. The research shows that the combination of autocorrelation function method and Cao's algorithm can calculate the optimal characteristic parameters for the singular value decomposition method and improve the ability of the method to identify fault features.

Temporal continuity of visual attention for future gaze prediction in immersive virtual reality

BackgroundEye tracking technology is receiving increased attention in the field of virtual reality. Specifically, future gaze prediction is crucial in pre-computation for many applications such as gaze-contingent rendering, advertisement placement, and content-based design. To explore future gaze prediction, it is necessary to analyze the temporal continuity of visual attention in immersive virtual reality. MethodsIn this paper, the concept of temporal continuity of visual attention is presented. Subsequently, an autocorrelation function method is proposed to evaluate the temporal continuity. Thereafter, the temporal continuity is analyzed in both free-viewing and task-oriented conditions. ResultsSpecifically, in free-viewing conditions, the analysis of a free-viewing gaze dataset indicates that the temporal continuity performs well only within a short time interval. A task-oriented game scene condition was created and conducted to collect users' gaze data. An analysis of the collected gaze data finds the temporal continuity has a similar performance with that of the free-viewing conditions. Temporal continuity can be applied to future gaze prediction and if it is good, users' current gaze positions can be directly utilized to predict their gaze positions in the future. ConclusionsThe current gaze's future prediction performances are further evaluated in both free-viewing and task-oriented conditions and discover that the current gaze can be efficiently applied to the task of short-term future gaze prediction. The task of long-term gaze prediction still remains to be explored.

Time and frequency characterization of GLONASS and Galileo on-board clocks

As the core component of navigational satellites, the on-board atomic clock is of key importance for guaranteeing the stable operation of global satellite navigation systems (GNSSs). As GNSSs are modernized, the clocks of several satellites have been changed to support long-term in-orbit operation. Analysis of the performance of on-board clocks is therefore crucial for supporting the positioning, navigation and timing of the satellite navigation system. Most studies involving stability analysis have not paid much attention to performance variation as a consequence of clocks being changed. This motivated our study, in which we analyzed the time domain characterization of on-board clocks from January 2016 to October 2018 to detect the variation. With respect to frequency domain characterization, conventional methods for noise analysis are easily affected by frequent fluctuations and local discontinuities as the averaging time increases. Hence, we propose the lag 1 autocorrelation function method based on overlapping samples to identify the noise of satellite clocks. We find that the time domain stability of the Galileo clock is of the magnitude of 10–13, 10–14 and 10–15 at averaging times of 100 s, 1000 s and 1 d, respectively, which is one order of magnitude better than that of the Russian global navigation satellite system (GLONASS). Moreover, the stability of the passive hydrogen masers on board Galileo shows a certain improvement due to the frequency modulation. The overall performance of the GLONASS cesium atomic clock and the Galileo rubidium atomic frequency standard is stable without any obvious aging in recent years. In terms of the frequency domain characterization, GLONASS clocks are mainly affected by flicker phase modulation noise, white frequency modulation noise and flicker frequency modulation noise, whereas the Galileo clocks are mainly affected by three kinds of frequency modulation noise.

A review on epileptic foci localization using resting-state functional magnetic resonance imaging.

Epilepsy is a brain syndrome caused by synchronous abnormal discharge of brain neurons. As an effective treatment for epilepsy, successful surgical resection requires accurate localization of epileptic foci to avoid iatrogenic disability. Previous studies have demonstrated the potential of restingstate functional magnetic resonance imaging (rs-fMRI) technique to localize epileptic foci though clinical applications of rs-fMRI are still at an early stage of development. fMRI data analysis approaches seek pre-defined regressors modeling contributions to the voxel time series, including the BOLD response following neuronal activation. In present study, localization strategies of epileptic foci in rs-fMRI technology were classified and summarized. To begin with, data-driven approaches attempting to determine the intrinsic structure of the data were discussed in detail. Then, as novel fMRI data analysis methods, deconvolution algorithms such as total activation (TA) and blind deconvolution were discussed, which were applied to explore the underlying activity-inducing signal of the BOLD signal. Lastly, effective connectivity approaches such as autocorrelation function method and Pearson correlation coefficient have also been proposed to identify the brain regions driving the generation of seizures within the epileptic network. In the future, fMRI technology can be used as a supplement of intraoperative subdural electrode method or combined with traditional epileptic focus localization technologies, which is one of the most attractive aspect in clinic. It may also play an important role in providing diagnostic information for epilepsy patients.

Onset detection of ultrasonic signals for the testing of concrete foundation piles by coupled continuous wavelet transform and machine learning algorithms

The construction of ultra-high-rise and long-span structures requires higher requirements for the integrity detection of piles. The acoustic signal detection has been verified an efficient and accurate nondestructive testing method. In fact, the integrity of piles is closely related to the onset time of signals. The accuracy of onset time directly affects the integrity evaluation of a pile. To achieve high-precision onset detection, continuous wavelet transform (CWT) preprocessing and machine learning algorithms were integrated into the software of high-sampling rate testing equipment. The distortion of waveforms, which could interfere with the accuracy of detection, was eliminated by CWT preprocessing. To make full use of the collected waveform data, three types of machine learning algorithms were used for classifying whether the data points are ambient or ultrasonic signals. The models involve a commonly used classifier (ELM), an individual classification tree model (DTC), an ensemble tree model (RFC) and a deep learning model (DBN). The classification accuracy of the ambient and ultrasonic signals of these models was compared by 5-fold validation. Results indicate that RFC performance is better than DBN and DTC after training. It is more suitable for the classification of points in waveforms. Then, a detection method of onset time based on classification results was therefore proposed to minimize the interference of classification errors on detection. In addition to the three data mining methods, the autocorrelation function method was selected as the control method to compare the proposed data mining based methods with the traditional one. The accuracy and error analysis of 300 waveforms proved the feasibility and stability of the proposed method. The RFC-based detection method is recommended because of the highest accuracy, lowest errors, and the most favorable error distribution among four onset detection methods. Successful applications demonstrate that it could provide a new way for ensuring the accurate testing of pile foundation integrity.

Sensitivity of Pitch Frequency Estimation to Reverberation Effect

This paper investigates the effect of reverberation on pitch frequency estimation. The autocorrelation function (ACF) method is used for pitch frequency estimation with and without reverberation effect. This paper modeled the reverberation effect on speech signal using a comb filter. The estimation error percentage of a comb filter at the length mild 8, moderate 10, and severe 12 in these scenarios have been investigated. The accuracy of the pitch frequency estimation is evaluated for the different scenarios for further accurate speech processing and verification.