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Automatic speaker identification system based on MLP network and deep learning in the presence of severe interference

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Multimedia Tools and Applications 2025年 1-42页

作者： Shafik, Amira Sedik, Ahmed El-Shafai, Walid Khalaf, Ashraf A. M. El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Minia University Minia61519 Egypt Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafr Elsheikh University Kafr El‑Shaikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Automatic Speaker Identification (ASI) is so crucial for security. Current ASI systems perform well in quiet and clean surroundings. However, in noisy situations, the robustness of an ASI system against additive noise and interference is a crucial factor. An investigation of the impact of interference on ASI system performance is presented in this paper, which introduces algorithms for achieving high ASI system performance. The objective is to resist the interference of various forms. This paper presents two models for the ASI task in the presence of interference. The first one depends on Normalized Pitch Frequency (NPF) and Mel-Frequency Cepstral Coefficients (MFCCs) as extracted features and Multi-Layer Perceptron (MLP) as a classifier. In this model, we investigate the utilization of a Discrete Transform (DT), such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST), to increase the robustness of extracted features against different types of degradation through exploiting the sub-band decomposition characteristics of DWT and the energy compaction property of DCT and DST. This is achieved by extracting features directly from contaminated speech signals in addition to features extracted from discrete transformed signals to create hybrid feature vectors. The enhancement techniques, such as Spectral Subtraction (SS), Winer Filter, and adaptive Wiener filter, are used in a preprocessing stage to eliminate the effect of the interference on the ASI system. In the second model, we investigate the utilization of Deep Learning (DL) based on a Convolutional Neural Network (CNN) with speech signal spectrograms and their Radon transforms to increase the robustness of the ASI system against interference effects. One of this paper goals is to introduce a comparison between the two models and build a more robust ASI system against severe interference. The experimental results indicate that the two proposed models lead to satisfa

关键词： Additive noise

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Towards AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

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IEEE Internet of Things Journal 2025年第11期12卷 16990-16997页

作者： Ahmed, Imran Ahmad, Misbah Siddiqi, Muftooh Ur Rehman Chehri, Abdellah Jeon, Gwangill Anglia Ruskin University School of Computing and Information Science Cambridge United Kingdom University of West of England Centre for Machine Vision Bristol Robotics Laboratory Bristol United Kingdom Hartpury University Department of Animal and Agriculture Gloucester United Kingdom Aston University College of Engineering and Physical Sciences Birmingham United Kingdom Department of Mathematics and Computer Science Canada Incheon National University Department of Embedded Systems Engineering Incheon Korea Republic of

In the rapidly advancing field of intelligent transportation systems, integrating Artificial Intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model's effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model's capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision-making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step towards recognising the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology. © 2025 IEEE.

关键词： Artificial intelligence (AI) autonomous vehicles deep leaning edge computing Internet of Vehicles (IoV)

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Enhanced Soil Property Estimations from Earth Observation Data with Differential Evolution-Based Multi-Objective TSK Model 13

Enhanced Soil Property Estimations from Earth Observation Da...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Tsakiridis, Nikolaos L. Samarinas, Nikiforos Kalopesa, Eleni Theocharis, John B. Zalidis, George C. Aristotle University of Thessaloniki Spectra Lab Group Laboratory of Remote Sensing Spectroscopy and Gis Department of Agriculture Thessaloniki54124 Greece Aristotle University of Thessaloniki Automation and Robotics Laboratory Electrical and Computer Engineering Thessaloniki54124 Greece

ISBN: (纸本)9798350308365

This paper introduces a novel approach integrating Differential Evolution (DE) with multi-objective optimization techniques for enhancing Type-1 Takagi-Sugeno-Kang (TSK) fuzzy rule-based systems to attain both fair predictive performance and enhanced interpretability within the context of explainable artificial intelligence. The developed approach encodes the whole knowledge base into one chromosome and endeavors to concurrently optimize the prediction accuracy, the number of rules, and the selection of relevant input features. The methodology was tested on Earth observation data for the estimation of soil health parameters via infrared spectroscopy, and more concretely i) on the 2015 LUCAS topsoil dataset to predict three key soil properties (namely, soil organic carbon, clay content, and pH) from laboratory spectra, and ii) on a set of PRISMA hyperspectral images to predict soil organic carbon in a region of northern Greece. Compared to the classical Random Forest (RF) algorithm, our proposed learning algorithm attains a fair balance between accuracy and interpretability, and is statistically equivalent to RF in terms of accuracy. The findings underscore the potential of the proposed methodology in refining TSK models and its applicability in Earth observation-driven predictions, paving the way for both enhanced modeling accuracy and sparse feature selection. © 2024 IEEE.

关键词： Fuzzy rules

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TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud Analysis

TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cl...

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Pavlo Melnyk Andreas Robinson Michael Felsberg Mårten Wadenbäck Department of Electrical Engineering Computer Vision Laboratory Linköping University Sweden

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In many practical applications, 3D point cloud analy-sis requires rotation invariance. In this paper, we present a learnable descriptor invariant under 3D rotations and reflections, i.e., the O(3) actions, utilizing the recently intro-duced steerable 3D spherical neurons and vector neurons. Specifically, we propose an embedding of the 3D spherical neurons into 4D vector neurons, which leverages end-to-end training of the model. In our approach, we perform TetraTransform-an equivariant embedding of the 3D input into 4D, constructed from the steerable neurons-and ex-tract deeper O(3)-equivariant features using vector neurons. This integration of the TetraTransform into the VN-DGCNN framework, termed TetraSphere, negligibly increases the number of parameters by less than 0.0002%. TetraSphere sets a new state-of-the-art performance classifying randomly rotated real-world object scans of the challenging subsets of ScanObjectNN. Additionally, TetraSphere outperforms all equivariant methods on randomly rotated synthetic data: classifying objects from ModelNet40 and segmenting parts of the ShapeNet shapes. Thus, our results reveal the prac-tical value of steerable 3D spherical neurons for learning in 3D Euclidean space. The code is available at https: //***/pavlo-melnyk/tetrasphere.

关键词： Point cloud compression Training Solid modeling Three-dimensional displays Shape Neurons Vectors

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On learning deep O(n)-equivariant hyperspheres 24

On learning deep O(n)-equivariant hyperspheres

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Proceedings of the 41st International Conference on Machine Learning

作者： Pavlo Melnyk Michael Felsberg Mårten Wadenbäck Andreas Robinson Cuong Le Computer Vision Laboratory Department of Electrical Engineering Linköping University Sweden

In this paper, we utilize hyperspheres and regular n-simplexes and propose an approach to learning deep features equivariant under the transformations of nD reflections and rotations, encompassed by the powerful group of O(n). Namely, we propose O(n)-equivariant neurons with spherical decision surfaces that generalize to any dimension n, which we call Deep Equivariant Hyperspheres. We demonstrate how to combine them in a network that directly operates on the basis of the input points and propose an invariant operator based on the relation between two points and a sphere, which as we show, turns out to be a Gram matrix. Using synthetic and real-world data in nD, we experimentally verify our theoretical contributions and find that our approach is superior to the competing methods for O(n)-equivariant benchmark datasets (classification and regression), demonstrating a favorable speed/performance trade-off. The code is available on GitHub.

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An Interpretable CNN for the Segmentation of the Left Ventricle in Cardiac MRI by Real-Time Visualization

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computer Modeling in engineering & Sciences 2023年第5期135卷 1571-1587页

作者： Jun Liu Geng Yuan Changdi Yang Houbing Song Liang Luo Robotics Institute School of Computer ScienceCarnegie Mellon UniversityPittsburghPA15217USA Department of Electrical&Computer Engineering College of EngineeringNortheastern UniversityBostonMA02115USA Security and Optimization for Networked Globe Laboratory(SONG Lab) Embry-Riddle Aeronautical UniversityDaytona BeachFL32114USA School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu610054China

The interpretability of deep learning models has emerged as a compelling area in artificial intelligence *** safety criteria for medical imaging are highly stringent,and models are required for an ***,existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and ***,the interpretability of CNNs has come into the *** medical imaging data are limited,many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public Image Net datasets by the transfer learning ***,this generates many unreliable parameters and makes it difficult to generate plausible explanations from these *** this study,we trained from scratch rather than relying on transfer learning,creating a novel interpretable approach for autonomously segmenting the left ventricle with a cardiac *** enhanced GPU training system implemented interpretable global average pooling for graphics using deep *** deep learning tasks were *** included data management,neural network architecture,and *** system monitored and analyzed the gradient changes of different layers with dynamic visualizations in real-time and selected the optimal deployment *** results demonstrated that the proposed method was feasible and efficient:the Dice coefficient reached 94.48%,and the accuracy reached 99.7%.It was found that no current transfer learning models could perform comparably to the ImageNet transfer learning *** model is lightweight and more convenient to deploy on mobile devices than transfer learning models.

关键词： Interpretable graphics training visualization image segmentation left ventricle CNNs global average pooling

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Adaptive inter-intradomain alignment network with class-aware sampling strategy for rolling bearing fault diagnosis

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Science China(Technological Sciences) 2023年第10期66卷 2862-2870页

作者： GAO QinHe HUANG Tong ZHAO Ke SHAO HaiDong JIN Bo LIU ZhiHao WANG Dong National Key Discipline Laboratory of Armament Launch Theory&Technology Rocket Force University of EngineeringXi’an 710025China Key Laboratory of Road Construction Technology&Equipment Ministry of Education Chang’an UniversityXi’an 710054China College of Mechanical and Vehicle Engineering Hunan UniversityChangsha 410082China Institute of Systems and Robotics Department of Electrical and Computer EngineeringUniversity of CoimbraCoimbra 3030-290Portugal

Existing unsupervised domain adaptation approaches primarily focus on reducing the data distribution gap between the source and target domains,often neglecting the influence of class information,leading to inaccurate alignment *** by this observation,this paper proposes an adaptive inter-intra-domain discrepancy method to quantify the intra-class and inter-class discrepancies between the source and target ***,an adaptive factor is introduced to dynamically assess their relative *** upon the proposed adaptive inter-intradomain discrepancy approach,we develop an inter-intradomain alignment network with a class-aware sampling strategy(IDAN-CSS)to distill the feature *** classaware sampling strategy,integrated within IDAN-CSS,facilitates more efficient *** multiple transfer diagnosis cases,we comprehensively demonstrate the feasibility and effectiveness of the proposed IDAN-CSS model.

关键词： unsupervised domain adaptation inter-class domain discrepancy intra-class domain discrepancy class-aware sampling strategy

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Real-Time Imitation of Human Head Motions, Blinks and Emotions by Nao Robot: A Closed-Loop Approach 11

Real-Time Imitation of Human Head Motions, Blinks and Emotio...

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11th RSI International Conference on robotics and Mechatronics, ICRoM 2023

作者： Rayati, Keyhan Feizi, Amirhossein Beigy, Alireza Shahverdi, Pourya Masouleh, Mehdi Tale Kalhor, Ahmad Louie, Wing-Yue Geoffrey University of Tehran Human and Robot Interaction Lab Electrical and Computer Engineering Tehran Iran Shahrood University of Technology Department of Electrical Engineering Shahrood Iran Oakland University Intelligent Robotics Laboratory Michigan United States

ISBN: (纸本)9798350308105

This paper introduces a novel approach for enabling real-time imitation of human head motion by a Nao robot, with a primary focus on elevating human-robot interactions. By using the robust capabilities of the MediaPipe as a computer vision library and the DeepFace as an emotion recognition library, this research endeavors to capture the subtleties of human head motion, including blink actions and emotional expressions, and seamlessly incorporate these indicators into the robot's responses. The result is a comprehensive framework which facilitates precise head imitation within human-robot interactions, utilizing a closed-loop approach that involves gathering real-time feedback from the robot's imitation performance. This feedback loop ensures a high degree of accuracy in modeling head motion, as evidenced by an impressive R2 score of 96.3 for pitch and 98.9 for yaw. Notably, the proposed approach holds promise in improving communication for children with autism, offering them a valuable tool for more effective interaction. In essence, proposed work explores the integration of real-time head imitation and real-time emotion recognition to enhance human-robot interactions, with potential benefits for individuals with unique communication needs. © 2023 IEEE.

关键词： Emotion Recognition

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Dependency-aware Task Offloading and Resource Pricing in Vehicular Edge Computing: A Stackelberg Game Approach 22

Dependency-aware Task Offloading and Resource Pricing in Veh...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Zhao, Liang Huang, Shuai Cao, Yuxiang Zhou, Huan Leung, Victor C. M. China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information Technology Yichang China Northwestern Polytechnical University School of Computer Science Xi'an China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798331509712

Vehicular Edge Computing (VEC) allows vehicles to offload their delay-sensitive tasks to nearby Road Side Units (RSUs) for processing, which improves network quality of service (QoS). However, the self-interested SDN controller is unwilling to ask RSUs to provide free computing resources for vehicles. At the same time, complicated dependencies between vehicular subtasks may cause non-ideal task delay and energy consumption. In order to solve these problems, this paper proposes a Stackelberg game-based Dependency-aware task Offloading and resource Pricing framework (SDOP). Specifically, we first model a vehicular edge network that partially offloads dependency-aware tasks. Then, we depict the interaction between the SDN controller and vehicles as a Stackelberg game, with the goal of maximizing the utility of both parties. Next, we present a Gradient Ascent Plus Genetic algorithm (GAPG) to solve the problem. Finally, numerous simulations are performed, and the results show that compared with other baseline schemes, the proposed GAPG can significantly improve the utility of both the SDN controller and vehicles under various scenarios. © 2024 IEEE.

关键词： Quality of service

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Sequential Transfer with Multi-Objective Genetic Algorithm for Feature Selection of Small, High-Dimensional Datasets

Sequential Transfer with Multi-Objective Genetic Algorithm f...

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Congress on Evolutionary Computation

作者： Parth C. Upadhyay G. N. DeSouza John A. Lory Department of Electrical Engineering and Computer Science (EECS) Vision-Guided and Intelligent Robotics (ViGIR) Laboratory Division of Plant Sciences University of Missouri Columbia MO

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Performing feature selection on a small number of instances with high-dimensional datasets poses a needed challenge in preventing over-fitting. To address this issue, this paper proposes a sequential transfer-learning approach combined with a multi-objective genetic algorithm (STMO-GA) for feature selection. Firstly, for the multi-objective component of our method, we employ a Non-dominated Sorting Genetic Algorithm (NSGA-II) to generate a Pareto front. Then, features are ranked based on their number of appearances in the same Pareto front. Next, during the sequential knowledge transfer process, the ranked features are iteratively selected until a predetermined $n$ number of features remains. This feature subspace is further refined by a k-fold cross-validation operation, starting from the rank-one feature, to determine the cut-off of the $n$ features that will remain. Comparative evaluations against both GA-based as well as traditional feature selection methods demonstrate that the proposed method achieves superior classification accuracy, while retaining the smallest number or a comparable number of features.

关键词： Accuracy Evolutionary computation Feature extraction Genetics Linear programming Robustness Optimization

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