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A Speech Enhancement Algorithm Combining wavelet Transform and Adaptive Filters 6

A Speech Enhancement Algorithm Combining Wavelet Transform a...

引用

6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Shrestha, Aayam Ghorshi, Seyed Joorabchi, Marjan Panahi, Issa Firouzeh, Fereshteh Fakhar The University of Texas at Tyler Department of Electrical and Computer Engineering TX United States Sharif University of Technology School of Science and Engineering Iran The University of Texas at Dallas Department of Electrical and Computer Engineering TX United States

ISBN: (纸本)9798331506520

Noise as an unwanted interference can significantly degrade speech signals, especially those recorded by many microphones. This interference is modeled as additive noise that originates from a range of sources including White Gaussian Noise (WGN), babble, crowd, large city, and traffic noises. These disturbances can alter the characteristics of speech signals reducing both their quality and intelligibility. This paper introduces a novel approach designed to reduce noise and enhance the quality and intelligibility of speech signals. The proposed method combines wavelet Transform with Adaptive Filters, specifically the Wiener filter and RLS filter. The evaluation process involves testing noisy speech signals under realistic conditions with different signal-to-noise ratios (SNRs) and different types of additive noise. The objective measure is used for evaluation, including the perceptual evaluation of speech quality (PESQ). Results show that combining Wiener or RLS filtering with wavelet Transform significantly improves noise reduction, outperforming the use of wavelet Transform alone. © 2025 IEEE.

关键词： Adaptive filtering

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Heart signal processing Using wavelet Analysis and Deep Learning Algorithms Based on Artificial Intelligence

Heart Signal Processing Using Wavelet Analysis and Deep Lear...

引用

International conference on applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2024

作者： Zilaie, Morteza Mohammadkhani, Zohreh Asrari, Keyvan Azimi Talebiyan, Seyed Reza Islamic Azad University Science and Research Branch Khorasan Razavi Mashhad Iran The University of Pavia Pavia Italy Imam Reza International University Khorasan Razavi Mashhad Iran

ISBN: (纸本)9783031840999

The electrocardiogram signal of the heart is used to monitor the health status and function of the human heart and to a doctor in diagnosing the type of disease. For this purpose, first, the scalogram of the different heart failures produces different types of this signal. In this research, the cardiac signals of the patients are analyzed by an artificial intelligence neural network, and the type of the person’s disease is diagnosed. The artificial intelligence in question can be learned and can be used as an assistant ECG signal is calculated using continuous wavelet transform and then it is subjected to learning and evaluation using a deep convolutional neural network. In the architecture of artificial intelligence, two well-known neural networks GoogLeNet and SqueezeNet are used, which have been sufficiently trained in similar applications such as image processing and machine vision. In the end, the type of heart failure is diagnosed and classified. The cardiac signals of the patients used in the simulation were extracted from the PhysioNet medical engineering standard dataset of MIT University. In the end, the simulation results of the research are given. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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An Improved Digital signal processing for signal Enhancement and Interference Mitigation 3

An Improved Digital Signal Processing for Signal Enhancement...

引用

3rd IEEE International conference on Automation and Computation, AUTOCOM 2025

作者： Alam, Intekhab Baxi, Paramjit Sahoo, Barnali Garg, Mohan Roopashree, R. Thaj Mary Delsy, T. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331542375

Digital signal processing has become a fundamental tool in signal processing applications, such as speech and image processing, due to its ability to manipulate and examine signals in a digital form skillfully. A primary challenge in signal processing involves the presence of unwanted noise and interference, which can negatively impact the precision and quality of the processed signal. The proposed advanced DSP approach aims to improve signal enhancement and interference mitigation techniques using sophisticated algorithms and methods. The proposed state-of-the-art DSP approach includes noise reduction algorithms, such as adaptive filtering and spectral subtraction, to eliminate unwanted noise from the signal. Additionally, cutting-edge signal processing techniques, such as wavelet transforms and time-frequency analysis, are used for signal enhancement and demising. The proposed approach employs adaptive equalization techniques to mitigate interference from other signals and substantially improve the overall signal quality. The enhanced DSP approach is evaluated through simulation and real-world experiments, demonstrating its effectiveness in improving signal quality and reducing interference considerably. The proposed approach has the potential to significantly improve the accuracy and reliability of various signal processing applications considerably, making it a valuable tool in fields such as telecommunications, audio and video processing, and medical signal analysis. © 2025 IEEE.

关键词： image enhancement

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wavelet-based Filtering of Noisy images by Amplification of Low-Frequency Components in High-Frequency Subbands

Wavelet-based Filtering of Noisy Images by Amplification of ...

引用

Digital signal processing and its applications (DSPA), International conference on

作者： Y. S. Bekhtin V. T. Trinh Department of Automatics and Information Technologies in Control Ryazan State Radio Engineering University named after V.F. Utkin Ryazan Russian Federation

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

The paper describes a new method of noisy image restoration based on amplification of low-frequency components in high-frequency subbands, or details of fast wavelet transform (FWT). It makes a difference with respect to well-known wavelet filtering methods which are typically based on soft thresholding of wavelet coefficients belonging to high-frequency subbands of a noisy image. At the same time, the low-frequency component, or approximation can be used to extract the low-frequency components corresponding the details. It allows modifying the details of the FWT by adding the extracted low-frequency components that leads to increasing the signal-to-noise ratios in subbands. The paper contains the experimental results of modeling proving the effectiveness of the suggested method comparing to some widely used filtering methods, too. The results of modeling have shown that the proposed method allows improving quality of restored image by increasing both the signal-to-noise ratio and significantly reducing the number of ringing artifacts.

关键词： Wiener filters Filtering image edge detection Digital signal processing image restoration Noise measurement signal to noise ratio wavelet coefficients

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wavelet Analysis of Cardiograms in EDF Format

Wavelet Analysis of Cardiograms in EDF Format

引用

Digital signal processing and its applications (DSPA), International conference on

作者： Sinan V. Kurbanov Denis A. Andrikov Department of Mechanics and Control Processes Peoples' Friendship University of Russia (PFUR) Moscow Russian Federation

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

The correlation of the wavelet shape and the characteristic ECG section has been confirmed, and wavelets for the identification of P-, R-, and T waves have been experimentally established. An algorithm for identifying R waves in an ECG has been developed and its operability has been verified. Using the algorithm developed, a graph of the dynamics of the heartbeat period is plotted as a function of time (period number). To identify the P and T waves, the wavelet image of the real ECG is constructed, and the presence of distortion of the shape of this image due to the influence of QRS complex and neighboring waves is established. Computer removal (filtering) of QRS complexes from the ECG is carried out, wavelet image of the remaining part is calculated. The possibility of determining the positions of the maximum P- and T-waves has been established from the wavelet image of the filtered ECG. The theoretical and practical value of this study consists in a new direction of wavelet analysis of cardiac signals, by successively applying several wavelets of a special form, to identify characteristic sections of the ECG in order to improve diagnostics. The proposed method also assumes intermediate filtering of the identified sections when moving from wavelet to wavelet. The development was carried out within the framework of computer generation of arrays of digital functions of the state of the heart, suitable for training artificial intelligence systems, and diagnostics using such systems. The practical results obtained can be applied in the development of a digital expert diagnostic system or a specific technical device.

关键词： Training Correlation Shape Heart beat Filtering Heuristic algorithms signal processing algorithms Electrocardiography wavelet analysis Distortion

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3rd International conference on Machine Vision and Augmented Intelligence, MAI 2023

3rd International Conference on Machine Vision and Augmented...

引用

3rd International conference on Machine Vision and Augmented Intelligence, MAI 2023

ISBN: (纸本)9789819743582

The proceedings contain 76 papers. The special focus in this conference is on Machine Vision and Augmented Intelligence. The topics include: Survey on Robustness of Deep Learning Techniques on Adversarial Attacks in WBAN;synergizing Collaborative and Content-Based Filtering for Enhanced Movie Recommendations;exploring Transformer-Based Approaches for Hyperspectral image Classification: A Comparative Analysis;deep Learning for Cognitive Task and Seizure Classification with Hilbert–Huang Transform and Variational Mode Decomposition;tracking of Ship and Plane in Satellite Videos Using a Convolutional Regression Network with Deep Features;Tumor Detection and Analysis from Brain MRI images Using Deep Learning;software Maintenance Prediction Using Stack Ensemble Deep Learning Algorithms;resource Allocation in 6G Network for High-Speed Train Using D2D Outband Communication;controlling the Band-to-Band Tunneling Effect in Charge Plasma Based Dopingless Transistor;Comparison of Different CIC Filter Architectures on the Basis of a Novel Parameter Called Noise Factor for Sigma-Delta Based ADCs;the Scientific Analysis on Effective Yoga Posture Recognition Techniques;impact of Gamma Rays on Emerging Devices for Photonic applications;shaft Rotation Monitoring Using Radar signal processing and wavelet Transform;gysel Power Divider Miniaturization Using an Inter-Digital Capacitor-Based Slow-Wave Structure;noise Estimation and Removal in Fundus images Using Pyramid Real image Denoising Network;evaluation of Hybrid Encryption Method to Secure Healthcare Data;multimodal Face Recognition System Using Hybrid Deep Learning Feature;Classification of Copy and Move image by Using HELM-FSK Method: An Efficient Approach;analysis of Energy Efficient Smart Home Based on IoT System;role of Explainable Artificial Intelligence Approaches in Cybersecurity.

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An Improved Digital signal processing for signal Enhancement and Interference Mitigation

An Improved Digital Signal Processing for Signal Enhancement...

引用

Automation and Computation (AUTOCOM), International conference on

作者： Intekhab Alam Paramjit Baxi Barnali Sahoo Mohan Garg Roopashree R Thaj Mary Delsy T Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Sciences Chemistry and Biochemistry JAIN (Deemed to be University) Bangalore Karnataka India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

关键词： Wireless communication wavelet transforms Time-frequency analysis Accuracy Prevention and mitigation signal processing algorithms Interference Digital signal processing wavelet analysis Telecommunications

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A Speech Enhancement Algorithm Combining wavelet Transform and Adaptive Filters

A Speech Enhancement Algorithm Combining Wavelet Transform a...

引用

International image processing, applications and Systems conference (IPAS)

作者： Aayam Shrestha Seyed Ghorshi Marjan Joorabchi Issa Panahi Fereshteh Fakhar Firouzeh Department of Electrical and Computer Engineering The University of Texas at Tyler Texas USA School of Science and Engineering Sharif University of Technology Iran Department of Electrical and Computer Engineering The University of Texas at Dallas Texas USA

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

关键词： wavelet transforms Additive noise Wiener filters Filtering Noise Urban areas Adaptive filters Interference Speech enhancement wavelet analysis

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Comparison of Widely-used image Restoration Methods for Additive and Multiplicative Noises

Comparison of Widely-used Image Restoration Methods for Addi...

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Digital signal processing and its applications (DSPA), International conference on

作者： Mohammad Zedan Yury S. Bekhtin dept. Diagnostic Information Technologies National Research University “MPEI” Moscow Russian Federation dept. Automatics and Information Technologies in Control Ryazan State Radio Engieneering University named after V.F. Utkin Ryazan Russian Federation

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

This paper evaluates and compares the performance of various image denoising techniques, including wavelet transform, Wiener filtering, non-local means (NLM), denoising autoencoders (DAEs), and feedforward autoencoders. There are cases of additive and multiplicative noises considered where the Gaussian distribution with zero mean stands for the first case and an exponential distribution with unity mean describes the second case (speckle). The experiments have been conducted on the given dataset which contained both still test images and synthetic aperture radar (SAR) images with varying noise levels to analyze the effectiveness of these denoising approaches. The performance of each method is assessed using key image quality metrics: Peak signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Mean Squared Error (MSE), and Normalized Absolute Error (NAK). The results demonstrate that denoising autoencoders DAEs provide superior performance in terms of structural preservation and noise reduction, particularly in low-noise conditions, while Wiener filtering proves to be computationally efficient and robust under high-noise conditions. These findings offer insights into the selection of effective denoising techniques for noisy images based on specific noise characteristics and application requirements.

关键词： wavelet transforms Wiener filters Additives PSNR Autoencoders Speckle Radar polarimetry Computational efficiency image restoration Noise level

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wavelet-based contrast computation and application to digital image watermarking

Wavelet-based contrast computation and application to digita...

引用

conference on wavelet applications in signal and image processing viii

作者： Vandergheynst, P Kutter, M Winkler, S Swiss Fed Inst Technol Signal Proc Lab CH-1015 Lausanne Switzerland

ISBN: (纸本)0819437646

We introduce an isotropic measure of local contrast for natural images that is based on analytic filters and present the design of directional wavelet frames suitable for its computation. We show how this contrast measure can be used within a masking model to facilitate the insertion of a watermark in an image while minimizing visual distortion.

关键词： Digital watermarking wavelets Visual process modeling Transducers image filtering Visualization Bandpass filters Curium image quality Optical filters

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