Hanning Window Research Articles

Location Detection and Numerical Simulation of Guided Wave Defects in Steel Pipes

At present, researchers in the field of pipeline inspection focus on pipe wall defects while neglecting pipeline defects in special situations such as welds. This poses a threat to the safe operation of projects. In this paper, a multi-node fusion and modal projection algorithm of steel pipes based on guided wave technology is proposed. Through an ANSYS numerical simulation, research is conducted to achieve the identification, localization, and quantification of axial cracks on the surface of straight pipelines and internal cracks in circumferential welds. The propagation characteristics and vibration law of ultrasonic guided waves are theoretically solved by the semi-analytical finite element method in the pipeline. The model section is discretized in one-dimensional polar coordinates to obtain the dispersion curve of the steel pipe. The T(0,1) mode, which is modulated by the Hanning window, is selected to simulate the axial crack of the pipeline and the L(0,2) mode to simulate the crack in the weld, and the correctness of the dispersion curve is verified. The results show that the T(0,1) and L(0,2) modes are successfully excited, and they are sensitive to axial and circumferential cracks. The time–frequency diagram of wavelet transform and the time domain diagram of the crack signal of Hilbert transform are used to identify the echo signal. The first wave packet peak point and group velocity are used to locate the crack. The pure signal of the crack is extracted from the simulation data, and the variation law between the reflection coefficient and the circumferential and radial dimensions of the defect is calculated to evaluate the size of the defect. This provides a new and feasible method for steel pipe defect detection.

Evaluating the Impact of Windowing Techniques on Fourier Transform-Preprocessed Signals for Deep Learning-Based ECG Classification

(1) Background: Arrhythmias, or irregular heart rhythms, are a prevalent cardiovascular condition and are diagnosed using electrocardiogram (ECG) signals. Advances in deep learning have enabled automated analysis of these signals. However, the effectiveness of deep learning models depends greatly on the quality of signal preprocessing. This study evaluated the impact of different windowing techniques applied to Fourier transform-preprocessed ECG signals on the classification accuracy of deep learning models. (2) Methods: We applied three windowing techniques—Hamming, Hann, and Blackman—to transform ECG signals into the frequency domain. A one-dimensional convolutional neural network was employed to classify the ECG signals into five arrhythmia categories based on features extracted from each windowed signal. (3) Results: The Blackman window yielded the highest classification accuracy, with improved signal-to-noise ratio and reduced spectral leakage compared to the Hamming and Hann windows. (4) Conclusions: The choice of windowing technique significantly influences the effectiveness of deep learning models in ECG classification. Future studies should explore additional preprocessing methods and their clinical applications.

Comparative exploration on EEG signal filtering using window control methods

Electroencephalography (EEG) is used to monitor brain activity. The brain signals consist of different frequency band signals delta, theta, alpha, beta, and gamma waves. The signals are affected by external noise which reduces the quality of the EEG signal due to which it becomes difficult to do further processing of EEG signals like feature extraction or extraction of meaningful features from EEG signal. Therefore, it becomes important to filter the noise from the EEG signal before feature extraction or classification of the EEG signal. The research article presents an overview of different types of windowing filter techniques like Rectangular, Bartlett, Hamming, Hanning, and Kaiser windows applied for finite impulse response (FIR) behavior which is used for EEG signal processing for different brain waves processed in different frequency bands. The comparative analysis is carried out in terms of the response time of brain frequency bands for different windowing filter techniques using the MATLAB 2023 signal processing simulation tool. The novelty of the work lies in estimating minimum latency and appropriate filter selection for various typical EEG waves, since the EEG signals are pre-supposed in the hardware chip design, noise elimination is the first step in high-performance computing applications. The Bartlett window band stop has an optimal response time of 12.666 s for delta waves, a highpass filter with a response time of 16.187 s for theta waves, a bandpass with a response time of 13.122 s for alpha waves, a highpass filter with a response time of 17.866 s for beta waves, and a highpass filter with a response time of 13.797 s for gamma waves. The Barlett window FIR filter is well-suited for EEG applications.

Analyzing Business Cycles in Azerbaijan: Application of Various Filters and Spectral Analysis

This study investigates the cyclical behavior of Azerbaijan's economy and its non-oil-gas sector through the application of various filtering techniques, including the Hodrick-Prescott, Baxter-King, and Christiano-Fitzgerald filters. Utilizing quarterly GDP data from Q1 2001 to Q4 2023, we analyze the volatility, cycle duration, and significant periods of economic growth and contractions. Our analysis reveals that the Hodrick-Prescott filter shows more pronounced and volatile cyclical components, while the Baxter-King and Christiano-Fitzgerald filters provide smoother and more moderated views. Spectral analysis, employing periodograms smoothed with Bartlett, Hamming, Hann, and Parzen kernel windows, identifies cycle durations ranging from 4.1 to 19.4 quarters for total GDP and 4.3 to 19.4 quarters for non-oil-gas GDP. We find that Azerbaijan's economy exhibits higher volatility compared to advanced economies, likely due to its dependence on oil exports and susceptibility to external shocks. Key periods such as the 2008 financial crisis and the COVID-19 pandemic significantly impacted economic performance, with notable declines identified across all filtering methods. The HP filter indicates a sharp and more prolonged downturn during these crises, whereas the BK and CF filters capture less prolonged and less severe impacts. This research provides a comprehensive understanding of Azerbaijan's business cycles, highlighting the importance of robust methodologies in economic analysis. Our findings contribute to the limited literature on Azerbaijan’s economy and offer valuable insights for policymakers aiming to enhance economic stability and growth.

Miniaturized photoelectrochemical sensing system for reusable detection of macromolecules and its applications for unattended environmental monitoring

Photoelectrochemical (PEC) sensors are widely employed in biochemical detection due to their accuracy and speed, but most PEC measurement systems face challenges with bulky instruments and complex operations. Current miniaturized systems based on Potentiostat and constant light are limited in accuracy and application ranges. PEC systems based on mod/demod signal processing offer high SNR and accuracy but struggles with miniaturization due to complex algorithms of Lock-in amplifier. Herein, discrete Fourier transform (DFT) algorithm instead of Lock-in algorithm was used for the photocurrent demodulation in PEC system. After functional verification, coordinate rotation digital computer (CORDIC) algorithm was used to implement DFT demodulation to further reduce resource occupancy. Afterwards, the spectral leakage was reduced by applying Hanning window, which improves the SNR and accuracy of photocurrent demodulation. Subsequently, a miniaturized, low-cost, low-power and high-accuracy PEC measurement system was obtained by integrating designed Hanning-CORDIC-DFT module, Potentiostat, light source driver and other miniaturized modules. The performances of miniaturized PEC measurement system were compared with that of the commercial PEC measurement system based on Lock-in amplifier by using the same MIP-PEC sensor, validating the feasibility and accuracy of designed miniaturized PEC system. Then, the performance of MIP-PEC sensor was investigated in detail. The results showed that MIP-PEC sensor has good sensitivity, stability, selectivity and recyclability for detecting MC-LR. The reliability of MIP-PEC sensor was further confirmed by the recovery test of MC-LR in lake water. Finally, long-term, cyclic and unattended measurements of microcystin-LR were achieved by designed miniaturized PEC system and MIP-PEC sensor under different conditions, verifying the practicality of the system for field monitoring. The designed measurement system provides a new method for constructing miniaturized PEC sensing platform, and a new solution for continuous biochemical detection in complex environments.

Application of speech recognition algorithm based on interactive artificial intelligence system in English video teaching system

Social robots are intelligent programs that can autonomously run, automatically publish information, and interact on social media platforms. This article focuses on the speech recognition algorithm of interactive artificial intelligence systems, combined with the Hanning window function and the Vitit algorithm, and explores its application value in English video teaching systems through detailed experiments. Adopting speech feature extraction technology provides strong support for the subsequent recognition process. The application of Hanning window function has a positive impact on speech signal preprocessing. The most indispensable algorithm in speech recognition models is the Vitit algorithm, which successfully solves the problem of time variation and continuity of speech signals. This study successfully applied speech recognition algorithms based on interactive artificial intelligence systems to English video teaching systems. By combining speech feature extraction, Hanning window function, and Vitit algorithm, a system with strong recognition ability and interactivity was constructed. The English video teaching system aims to improve students’ learning process. By combining interactive artificial intelligence speech recognition technology with English video teaching, it can improve students’ experience in learning English and also meet their personalized learning habits.

PaIRS-UniNa: A Robust And Accurate Free Tool For Digital Particle Image Velocimetry And Optical Camera Calibration

This contribution presents PaIRS-UniNa (PArticle Image Reconstruction Software – University of Naples “Federico II”), a free software application designed to perform digital particle image velocimetry (PIV). PaIRS-UniNa relies on a powerful C library designed for speed, robustness and accuracy, and a user-friendly graphical interface (Figure 1) running in Python environment on all operating systems, including Windows, Linux and MacOS. At the current stage, it features modules to perform two-component (2C) PIV, stereoscopic PIV and optical camera calibration for multi-camera systems. The processing of PIV and stereoscopic PIV data is based on an iterative image deformation method, which can be customized by the user varying different parameters in such a way to find the best compromise between accuracy and computational speed. These parameters include the size, shape and number of interrogation windows, the number of process iterations and the type of interpolating functions for both image deformation and displacement interpolation (such as bilinear, bicubic, methods based on simplexes or the FFT shift theorem and B-splines of varying orders). Additionally, it is possible to select a range of weighting windows for cross-correlation estimation and velocity field filtering, including rectangular, Blackman, Gaussian, Harris, Hann windows, among others, and several vector validation strategies for outlier detection and replacement. Such a level of customization enables to effectively adjust the modulation transfer function of the PIV process to meet specific application requirements. For the stereoscopic PIV process two different approaches are possible: mapping, which involves applying PIV to de-warped images for 2D-2C displacement fields, and warping, which applies PIV to raw images and combine 3C reconstruction and image dewarping in a single subsequent step. In order to accurately estimate the laser sheet plane location and correct errors due to misalignment and inaccurate displacement vector positions, a disparity correction procedure is also available. The calibration module (CalVi) supports accurate calibration with the camera models mostly used in the PIV community: polynomials, rational functions and the pinhole camera model. Moreover, it supports the integration of the pinhole camera model with a refractive correction model for cylindrical geometries and camera calibration procedures working with unknown positions and orientations of the calibration target. In such a way, the target may be moved by hand without losing accuracy in comparison with a conventional calibration, in which the target is displaced with the aid of translational and rotational stages and knowledge of the target positions is strictly required. Already used in numerous experimental studies by the Experimental Thermo-Fluid Dynamics group of University of Naples “Federico II” and international collaborating groups, PaIRS-UniNa offers a rapid and effective free alternative to commercial PIV software for research and industrial use.

Reconstruction of Tomographic Size Using a Filtered Back Projection Algorithm

Due to their widespread use, image processors are among the most essential computer processors. FBP technique is one of the most widely used tomographic reconstruction methods. Images from a computed tomography (CT) scanner with 512x512 pixels of raw data for parallel beam projections are the metadata to which this approach is applied. This work is divided into three parts. Several projections are created from an input test picture termed Shepp-Logan phantom in the first section, utilizing radon transformation. The picture is rebuilt from the projection of a parallel beam in the second section to minimize reconstruction time. Finally, the proposed system filters the projections using the Ram Lak filter and the Hann window in the third section to refine the picture and then recombine the projections to generate the reconstructed image. The reconstruction procedure is performed on CT images using a filtered back-projection algorithm in this article for image optimization and then projection recombination to generate the rebuilt picture. Since CT can accurately depict anatomical elements like bones and organs and may pick slides in any orientation, it is frequently used in biological applications and diagnostics. It also offers a great spatial resolution and soft tissue contrast. We develop, create, and run computer programs when we work with MATLAB. After projecting the 2D image onto a CT scanner, a very good result in reconstructing the image is received.

Reconstruction of Tomographic Size Using a Filtered Back Projection Algorithm

Due to their widespread use, image processors are among the most essential computer processors. FBP technique is one of the most widely used tomographic reconstruction methods. Images from a computed tomography (CT) scanner with 512x512 pixels of raw data for parallel beam projections are the metadata to which this approach is applied. This work is divided into three parts. Several projections are created from an input test picture termed Shepp-Logan phantom in the first section, utilizing radon transformation. The picture is rebuilt from the projection of a parallel beam in the second section to minimize reconstruction time. Finally, the proposed system filters the projections using the Ram Lak filter and the Hann window in the third section to refine the picture and then recombine the projections to generate the reconstructed image. The reconstruction procedure is performed on CT images using a filtered back-projection algorithm in this article for image optimization and then projection recombination to generate the rebuilt picture. Since CT can accurately depict anatomical elements like bones and organs and may pick slides in any orientation, it is frequently used in biological applications and diagnostics. It also offers a great spatial resolution and soft tissue contrast. We develop, create, and run computer programs when we work with MATLAB. After projecting the 2D image onto a CT scanner, a very good result in reconstructing the image is received.

Hanning FIR window filtering analysis for EEG signals

The window function is a mathematical function in signal processing that is multiplied by a signal to lessen or eliminate the impact of undesired characteristics such as spectral leakage and edge effects that might arise while analyzing finite-duration signals. The Hanning window is just one of several window functionalities available. It is designed to gradually taper the endpoints of a signal to zero, reducing spectral leakage in the frequency domain. Filtering is commonly employed in Electroencephalography (EEG) signal processing to remove noise, artifacts, or undesired frequency components while retaining the desired information. Filtering techniques include band pass, low pass, and high pass and band reject filtering. Hanning window technique using low pass, high pass, band pass, and band reject is used for filtering of EEG signal. The response time analysis for the Hanning window is calculated using low, high, band pass, and band-reject filters for different types of EEG waves alpha, beta gamma, theta, and delta using the signal processing simulation tool MATLAB 2023. The response time for delta wave using a low pass filter has a low response time of 14.125 sec which is comparatively less than another filter. For theta wave high pass filter has a low response time of 11.965 seconds. For alpha wave low pass filter has a low response time of 16.706 seconds whereas for beta wave low pass filter has a low response time of 15.202 seconds. For gamma waves high pass filter has a low response time of 11.349 seconds. Hanning band-reject filter has proven low response time for delta, theta, and gamma waveforms of 16.475 seconds, 16.182 seconds, and 17.743 seconds respectively. The novelty of the work is that the Hanning window improves frequency resolution by balancing the main lobe width with side lobe attenuation Hanning band-reject filter is suited best to figure out the widest main-lobe peak and highest peak for different EEG waveforms.

Research on Fiber-Optic Optical Coherence Ranging System Based on Laser Frequency Scanning Interferometry.

In this paper, a system for absolute distance measurement is proposed based on laser frequency scanning interferometry (FSI). The system utilizes a digitally tunable laser as the light source and employs synchronized pulses to drive an analog-to-digital converter (ADC) for interference signal acquisition. The frequency domain demodulation for absolute distance measurement is achieved through a three-spectrum line interpolation method based on the Hanning window. The system takes advantage of the spatial filtering characteristics of a single-mode optical fiber and the diffuse reflection properties of light to achieve a high integration of the prism system that forms the interference optical path. The resulting integrated fiber-optic probe is capable of measuring the distance to a non-cooperative target even when oriented at a certain angle with the target. We designed and fabricated a portable prototype. Experimental validation demonstrated that the maximum measurement distance of the system is 73.51 mm with a standard deviation of less than 0.19 μm for optimal measurement results. Even when there is an offset angle, the system maintains good measurement repeatability.

Balancing ensemble averaging and signal stationarity when processing acoustic data recorded during a rocket launch

There are many important details to consider when collecting acoustical data during a rocket launch including system noise floor, dynamic range and transducer sensitivity. In this talk, the choice of signal processing parameters will also be shown to be important. Since the pressures are created by turbulence, ensemble averaging is key to reducing random variation. As such, the use of a Hanning window with 50% overlap would be customary for performing this type of processing. However, as the rocket lifts off, the source location moves, and the data can only be considered stationary for short blocks of time. Therefore, the ability to average is limited by non-stationarity. To balance these competing phenomena, an adjustable windowing function such as the Tukey window may be more appropriate than Hanning. Noise data collected at two different launches will be used to compare of the Hanning window with 50% overlap to that using of the Tukey window with a taper of α = 0.25, which filters 12.5% of the signal at each extremity, with 87.5% overlap. The use of both window types will be compared when estimating auto spectral densities and integrated overall levels. The effect of blocksize will also be determined.

Pre-processing data and window function testing on wave spectrum analysis

Pre-processing data in oceanographic data research is an important step in processing data into the required information. This phase often takes longer and more rigorous time to present the correct data. Data pre-processing involves the selection of data, and the modification of data so that it can be readable in a computational algorithm. The data selection process includes selection of sample numbers, correction and filling of data gaps.The data used in spectrum analysis is the recording of the water surface profile of the wave in the data resolution of 1 second. The data needs to be corrected by the process of detrending against the tidal height which become the still water level. The spectral analysis is processed using the pwelch function on MATLAB. The frequency spectrum curve of the measurement is obtained from the calculation of the power spectral density value. In order to obtain a stable power spectral density curve, the Hann window function is used with the number of window elements, nfft/16.

A Multi Representation Deep Learning Approach for Epileptic Seizure Detection

Epileptic seizures, unpredictable in nature and potentially dangerous during activities like driving, pose significant risks to individual and public safety. Traditional diagnostic methods, which involve labour-intensive manual feature extraction from Electroencephalography (EEG) data, are being supplanted by automated deep learning frameworks. This paper introduces an automated epileptic seizure detection framework utilizing deep learning to bypass manual feature extraction. Our framework incorporates detailed pre-processing techniques: normalization via L2 normalization, filtering with an 80 Hz and 0,5 Hz Butterworth low-pass and high-pass filter, and a 50 Hz IIR Notch filter, channel selection based on standard deviation calculations and Mutual Information algorithm, and frequency domain transformation using FFT or STFT with Hann windows and 50% hop. We evaluated on two datasets: the first comprising 4 canines and 8 patients with 2.299 ictal, 23.445 interictal, and 32.915 test data, 400-5000Hz sampling rate across 16-72 channels; the second dataset, intended for testing, 733 icatal, 4.314 interictal, and 1908 test data, each 10 minutes long, recorded at 400Hz across 16 channels. Three deep learning architectures were assessed: CNN, LSTM, and a hybrid CNN-LSTM model-stems from their demonstrated efficacy in handling the complex nature of EEG data. Each model offers unique strengths, with the CNN excelling in spatial feature extraction, LSTM in temporal dynamics, and the hybrid model combining these advantages. The CNN model, comprising 31 layers, yielded highest accuracy, achieving 91% on the first dataset (precision 92%, recall 91%, F1-score 91%) and 82% on the second dataset using a 30-second threshold. This threshold was chosen for its clinical relevance. The research advances epileptic seizure detection using deep learning, indicating a promising direction for future medical technology. Future work will focus on expanding dataset diversity and refining methodologies to build upon these foundational results.

Digitized Perception of Piano Accompaniment in Songs with the Involvement of Deep Learning Techniques

Abstract In this paper, we use the Hanning window function to add windows to the piano accompaniment human brain perception signal, and the noise and redundant features in the piano accompaniment human brain perception signal are downgraded by principal component analysis and Relief algorithm for dimensionality reduction processing and feature selection. Based on the basic structure of RCNN, the ReConv-EEG Net EEG identity recognition method is established, and the feature vectors in the recurrent network are classified and computed through the feed-forward layer, the normalization layer and the output layer to obtain the feature values of the EEG signals. Under different speed accompaniments, the subjects perceived the high-speed piano accompaniment with the lowest α -wave power spectral value of 47.85 on average, while the original speed accompaniment had the most significant α -wave power spectral value of 49.12 on average.Meanwhile, there were differences in the perception of piano accompaniment among subjects, and the mean EEG δ -wave synchronization indexes of the non-musicians and the musicians differed by an average of 0.2297 when they were listening to the same accompaniment.The method in this paper contributes to developing a new method for deep learning-assisted music perception research in the field. Also, it lays the data foundation for applying the technique to the field.

Two-dimensional Hanning self-convolution window for enhancing Moiré fringe alignment in lithography

The current image processing techniques heavily rely on classic windows for suppressing spectral leakage. However, the limited capability of these classic windows to suppress spectral leakage restricts their application in the field of high-precision measurement. This paper proposes a novel family of windows capable of suppressing spectral leakage associated with the Moiré fringes pattern, thereby enhancing the accuracy of lithography alignment. Specifically, a novel two-dimensional Hanning self-convolution window is introduced, which is derived from the Hanning window to inherit its advantage of ultra-rapid sidelobe decay. The behaviors of the main lobe and desirable sidelobes of the 1st- to 4th-order are examined. Additionally, a novel fringe phase extraction algorithm is established based on the proposed window, which demonstrates high accuracy and efficiency. Simulation and experimental results validate the feasibility and practicality of the proposed window. In particular, the proposed window outperforms classic windows in suppressing spectral leakage, thereby successfully enhancing the phase extraction accuracy to achieve high-precision misalignment measurement at the sub-2-nm level (1.47 nm @ 3σ criterion). The proposed method can be easily extended to other real engineering applications utilizing fringe to achieve high-precision measurements, such as fringe projection profilometry, damage detection, and visual mechanical vibration monitoring.

Accurate Analysis of Multi-Mode Interferometric Optical Fiber Sensor

In view of the problem that the sensing characteristics of the multi-mode interferometric fiber sensors cannot be accurately analyzed, an analysis method based on the fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT) is proposed and demonstrated theoretically and experimentally. The suitabilities of the rectangular window function with the narrow main lobe (high spectrum resolution) and low side lobe (high main mode energy leakage) and the Hanning window function with the wide main lobe (low spectrum resolution) and high side lobe (high energy concentration) in this kind of sensor analysis are discussed, respectively. This method can not only realize the sensing performance analysis of the various modes, but also overcome the inconsistency of the different interference wavelength (dip) sensing characteristics in the conventional analysis methods. At the same time, this method is also beneficial to solve the repetitive problem of such sensors.

Research on Ultra-wide Bandwidth Low-frequency Signal Channelization for Xinjiang 110 m Radio Telescope

Aiming at the subband division of ultra-wide bandwidth low-frequency (UWL) signal (frequency coverage range: 704–4032 MHz) of the Xinjiang 110 m QiTai radio Telescope (QTT), a scheme of ultra-wide bandwidth signal is designed. First, we analyze the effect of different window functions such as the Hanning window, Hamming window, and Kaiser window on the performance of finite impulse response (FIR) digital filters, and implement a critical sampling polyphase filter bank (CS-PFB) based on the Hamming window FIR digital filter. Second, we generate 3328 MHz simulation data of ultra-wideband pulsar baseband in the frequency range of 704–4032 MHz using the ultra-wide bandwidth pulsar baseband data generation algorithm based on the 400 MHz bandwidth pulsar baseband data obtained from Parkes CASPSR observations. Third, we obtain 26 subbands of 128 MHz based on CS-PFB and the simulation data, and the pulse profile of each subband by coherent dispersion, integration, and folding. Finally, the phase of each subband pulse profile is aligned by non-coherent dedispersion, and to generate a broadband pulse profile, which is basically the same as the pulse profile obtained from the original data using DSPSR. The experimental results show that the scheme for the QTT UWL receiving system is feasible, and the proposed channel algorithm in this paper is effective.

Multi-window approach enables two-fold improvement in OCT axial resolution with strong side-lobe suppression and improved phase sensitivity.

A common processing approach for optical coherence tomography (OCT) uses a window function (e.g., Hann or rectangular window) for spectral shaping prior to calculating the Fourier transform. Here we build on a multi-window approach [Opt. Express8, 5267 (2017)10.1364/BOE.8.005267] that enables improved resolution while still suppressing side-lobe intensity. The shape of the window function defines the trade-off between main-lobe width (resolution) and side-lobe intensity. We have extended the approach to include the interferometric phase for phase-sensitive applications like vibrometry and Doppler OCT. Using the Hann window as a reference, we show that 11 Taylor windows are sufficient to achieve 50% improvement in axial resolution, -31 dB side-lobe intensity, and 20% improvement in phase sensitivity with low computational cost.

A high performance-oriented AI-enabled IoT-based pest detection system using sound analytics in large agricultural field

The agricultural industry has enormous potential to meet the world's growing need for nutritious and healthful food. However, pests destroy a significant portion of the harvest and reduce quality; thus, farmers struggle to discover pests on their farms. Traditional pest identification methods require scientists with extensive field experience correctly identify pests based on their physical characteristics. Pesticides harm food and agriculture. IoT technology uses many affordable sensor devices to collect real-time data on pest-related agricultural development characteristics. The research paper aims to develop an AI-enabled real-time, IoT-based automatic pest detection system using sound pest analytics and IoT networks in the sizeable agricultural field. The proposed method used audio pre-processing techniques in sound analytics such as HPF, Hann window, hop window, FFT, DFT, STFT, and MFCC algorithm for denoising the pest sound, removing the spectral leakage, converting overlapping to non-overlapping frames, converting the time to the frequency domain, determining frequency spectrum, detecting sinusoidal frequency and internal component, extracting the MFCCs feature respectively. The characteristics and other statistical metrics were collected from 500 pest sounds and trained, validated, and evaluated using the CNN, LSTM, Bi-LSTM, and CNN-Bi-LSTM network models. The extracting features and different statistical measurements were compared during the testing process. The proposed system integrated the CNN and Bi-LSTM techniques called CNN-Bi-LSTM model for training, validating, and testing. From the experimental results, it has been observed that, the proposed CNN-Bi-LSTM model achieved 98.91 % accuracy, 96.8 % sensitivity, 97.96 % specificity, 98.54 % recall, 98.63 % precision, and 98.58 % F1 score, which is better compared to CNN, LSTM, Bi-LSTM and also existing ANN, DCNN, and VGG-16 state-of-the-art techniques.