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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Wei Zhao, Siyi Qian, Yanmin Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350368741

Recent advancements in speech enhancement (SE) techniques have greatly improved speech clarity and intelligibility in challenging acoustic environments. However, integrating SE into automatic speech recognition (ASR) systems often results in performance degradation due to artifacts introduced during the enhancement process. While various methods have enhanced recognition accuracy in SE-ASR systems, they often require fine-tuning or re-training of SE or ASR models, which is impractical in many real-world applications. In this paper, we propose a lightweight distortion suppression (DS) network that addresses these artifacts without modifying the SE or ASR models, treating them as fixed black boxes. The DS module operates on the time-frequency (T-F) bands of the original and enhanced complex spectrograms, efficiently compensating for SE distortions using the original T-F information. We validate our approach through experiments on both Mandarin and English ASR tasks using monaural and multi-channel SE frontends, across various ASR backends. Results show that the DS module significantly improves the performance of SE-ASR systems, even when used with robust commercial ASR backends. © 2025 IEEE.

关键词： non-intrusive robust speech recognition speech distortion speech enhancement

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Advanced Zero-Shot Text-to-Speech for Background Removal and Preservation with Controllable Masked Speech Prediction

Advanced Zero-Shot Text-to-Speech for Background Removal and...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Leying Zhang, Wangyou Chen, Zhengyang Qian, Yanmin Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350368741

The acoustic background plays a crucial role in natural conversation. It provides context and helps listeners understand the environment, but a strong background makes it difficult for listeners to understand spoken words. The appropriate handling of these backgrounds is situation-dependent: Although it may be necessary to remove background to ensure speech clarity, preserving the background is sometimes crucial to maintaining the contextual integrity of the speech. Despite recent advancements in zero-shot Text-to-Speech technologies, current systems often struggle with speech prompts containing backgrounds. To address these challenges, we propose a Controllable Masked Speech Prediction strategy coupled with a dual-speaker encoder, utilizing a task-related control signal to guide the prediction of dual background removal and preservation targets. Experimental results demonstrate that our approach enables precise control over the removal or preservation of background across various acoustic conditions and exhibits strong generalization capabilities in unseen scenarios. © 2025 IEEE.

关键词： background preservation background removal flow-matching text-to-speech

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Pose Measurement of the EndoWrist Round Tip Scissor Instrument with Optical Coherence Tomography

Pose Measurement of the EndoWrist Round Tip Scissor Instrume...

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作者： Schöne, Sandra Pol, Nirmal Kahrs, Lueder A. Medical Computer Vision and Robotics Lab University of Toronto Toronto Canada University of Stuttgart Stuttgart Germany Medical Computer Vision and Robotics Lab Institute of Biomedical Engineering University of Toronto Toronto Canada Department of Mathematical and Computational Sciences University of Toronto Mississauga Mississauga Canada

To automate surgical (sub-)tasks in robotic surgery, the knowledge of the exact pose of the instrument is mandatory. The application of Optical Coherence Tomography (OCT) to the problem of pose measurement appears promising due to its advantages of 3D imaging and micron-scale resolution. To investigate this, 175 image sequences of the EndoWrist Round Tip Scissor Tool were acquired with an OCT system. The images differ in the opening angles of the scissor blades and the rotation angles of the entire instrument about its central axis. These image sequences were further processed through computer vision methods of the individual images followed by point cloud generation. For pose estimation, an Iterative Closest Point algorithm was implemented to register the acquired point clouds to reference point clouds created from the instrument CAD file. The implemented algorithm was able to determine the opening angle with an overall error of 2 ± 1.3 and the rotation angle with a standard deviation between several runs of 0.6 ±2.8. However, the overall processing time of (39 ± 17)s on a standard PC leaves room for further investigations. © 2024 by Walter de Gruyter Berlin/Boston.

关键词： Optical coherence tomography

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Face Detection in Video Live Stream Using Deep Learning 2

Face Detection in Video Live Stream Using Deep Learning

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Amrita Kumar, Rajiv Joshi, Shivani Raj, Daksh Centre for Cyber Security and Cryptology Computer Science & Engineering Sharda School of Engineering & Technology Sharda University U.P. Greater Noida India G L Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Nvidia Ai Learning & Research Center U.P. Greater Noida India G L Bajaj Institute of Technology and Management Department of Computer Science and Engineering U.P. Greater Noida India Galgotias College of Engineering and Technology Department of Computer Science and Engineering U.P. Greater Noida India

ISBN: (纸本)9798350371055

With the increasing popularity of live streaming platforms, there is an imperative need for robust methods for identifying individuals in real-time. The proposed system utilizes Convolutional Neural Networks (CNN) computer vision algorithms to identify faces in live streams and match them against a pre-existing database of known individuals. Additionally, the system employs deep learning and supervised learning techniques along with various types of CNN to achieve robust facial recognition in diverse lighting and environmental conditions. The system's accuracy is evaluated using various performance metrics, including precision, recall, and F1-score. The results demonstrate that the proposed system achieves high accuracy in person identification during real-time live streams. This system holds potential applications in a variety of domains, including security, entertainment, and social media. © 2024 IEEE.

关键词： computer vision Convolutional neural networks (CNN) deep learning F1-score Live streaming precision recall supervised learning

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Wireless Network Security Using Load Balanced Mobile Sink Technique

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Intelligent Automation & Soft Computing 2023年第2期35卷 2135-2149页

作者： Reem Alkanhel Mohamed Abouhawwash S.N.Sangeethaa K.Venkatachalam Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast Lansing48824MIUSA Department of Computer Science and Engineering Bannari Amman Institute of TechnologySathyamangalam638401India Department of Computer Science and Engineering CHRIST(Deemed to be University)Bengaluru560074India Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia

Real-time applications based on Wireless Sensor Network(WSN)tech-nologies are quickly increasing due to intelligent *** the most signiﬁcant resources in the WSN are battery power and *** stra-tegies improve the power factor and secure the WSN *** takes more electricity to forward data in a *** numerous clustering methods have been developed to provide energy consumption,there is indeed a risk of unequal load balancing,resulting in a decrease in the network’s lifetime due to network inequalities and less *** possibilities arise due to the cluster head’s limited life *** cluster heads(CH)are in charge of all activities and con-trol intra-cluster and inter-cluster *** proposed method uses Lifetime centric load balancing mechanisms(LCLBM)and Cluster-based energy optimiza-tion using a mobile sink algorithm(CEOMS).LCLBM emphasizes the selection of CH,system architectures,and optimal distribution of *** addition,the LCLBM was added with an assistant cluster head(ACH)for load *** consumption,communications latency,the frequency of failing nodes,high security,and one-way delay are essential variables to consider while evaluating *** will choose a cluster leader based on the inﬂuence of the fol-lowing parameters on the energy balance of *** to simulatedﬁnd-ings,the suggested LCLBM-CEOMS method increases cluster head selection self-adaptability,improves the network’s lifetime,decreases data latency,and bal-ances network capacity.

关键词： Wireless sensor network load balancing mechanism optimization power consumption network’s lifetime

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Building Consensus in Group Decision-Making with Intuitionistic Reciprocal Preference Relations: An Analysis of Various Protocols of Information Granularity Distribution

Building Consensus in Group Decision-Making with Intuitionis...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Gonzalez-Quesada, Juan Carlos Cabrerizo, Francisco Javier Herrera-Viedma, Enrique Pedrycz, Witold Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Department of Computer Science and Artificial Intelligence Granada18071 Spain University of Alberta Department of Electrical & Computer Engineering EdmontonABT6R 2V4 Canada

ISBN: (纸本)9781665410205

On the one hand, to model experts' preferences in group decision-making, intuitionistic reciprocal preference relations have widely been used because they allow for accommodating hesitation degrees, which are inherent to all decision-making processes. On the other hand, an optimization of information granularity distribution has recently been applied to establish consensus during group decision-making processes. Concretely, a symmetric and uniform distribution of information granularity has been considered for intuitionistic reciprocal preference relations. However, there exist other protocols of information granularity distribution that could be used. Therefore, we aim to analyze all the information granularity distribution protocols and determine their effectiveness in building consensus through intuitionistic reciprocal preference relations. The performance of the different protocols is discussed by conducting some numerical experiments that help provide insights into the effectiveness of the protocols to build consensus. © 2024 IEEE.

关键词： Information granules

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Multimodal Pain Assessment Βased on Physiological Biosignals: The Impact of Demographic Factors on Perception and Sensitivity 11

Multimodal Pain Assessment Βased on Physiological Biosignal...

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11th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2025

作者： Pavlidou, Elisavet Tsiknakis, Manolis Department of Electrical and Computer Engineering Hellenic Mediterranean University Estavromenos Heraklion71410 Greece Computational BioMedicine Laboratory Institute of Computer Science Foundation for Research & Technology-Hellas Vassilika Vouton Heraklion70013 Greece

ISBN: (纸本)9789897587436

Pain is a multidimensional and highly personalized sensation that affects individuals’ physical and emotional state. Visual analog scales, numeric rate indicators, and various questionnaires, all relying on patient-reported outcome measurements, are considered the "gold" standard methods for assessing the severity of pain. Nevertheless, self-report tools require cognitive, linguistic, and social abilities, which may manifest variations in certain populations such as neonates, individuals with intellectual disabilities, and those affected by dementia. The purpose of this study is to automate the process through multimodal physiological-data-driven machine-learning models in order to gain deeper insights into pain sensation. We developed a pipeline using electrocardiogram (ECG), galvanic skin response (GSR), and electromyogram (EMG), along with demographic information from the BioVid dataset. The Pan & Tompkins algorithm was applied for ECG signal processing, while statistical analysis was used for feature extraction across all signals. Our study achieved 82.83% accuracy in the SVM classification task of baseline (BL) vs the highest level of pain (PA4) for females aged 20-35. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Sensory perception

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Integrating Instruction-based Learning with Bidirectional LSTM for Autonomous Vehicle Pedestrian Detection

Integrating Instruction-based Learning with Bidirectional LS...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Shoba, B. Raghunathan, T. Peter, S.Sam Karthick, K. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak Psn College of Engineering and Technology Department of Electronics and Communication Engineering Tirunelveli India Dr.NG.p.Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India Sona College of Technology Department of Information Technology Salem India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

Pedestrian detection is a crucial aspect of autonomous vehicle (A V) safety, as it enables accurate identification and tracking of individuals in an A V environment. Effective pedestrian detection supports essential A V functions like braking, steering, and speed control to prevent accidents in urban settings. Traditional computer vision methods, such as Support Vector Machines and Random Forests, rely on pre-designed features like the Histogram of Oriented Gradients. While the existing techniques offer some performance, it face significant challenges in dynamic environments, especially under varying lighting conditions, occlusions, and diverse pedestrian poses. Traditional models also require extensive manual feature engineering and tuning, which limits scalability and adaptability. Recent advances in deep learning, particularly with Convolutional Neural Networks (CNNs), have allowed for automatic feature learning, yielding practical improvements over classical methods. However, CNNs can struggle in low light and when facing adversarial attacks, as they mainly capture spatial features. To address these limitations, this research proposes a new methodology combining CNNs for spatial feature extraction with Bidirectional Long Short-Term Memory (BiLSTM) networks to capture temporal dependencies. This hybrid approach enhances detection capabilities by learning both static and dynamic patterns. Our proposed CNN-BiLSTM model achieves a 92 % detection accuracy and an F1 score of 88.5% across diverse conditions, outperforming existing models by up to 15%. By integrating temporal features, the model reduces missed detections and false positives by up to 20%, providing a more reliable and adaptable solution for pedestrian detection in autonomous vehicles. © 2025 IEEE.

关键词： Convolutional neural networks

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Pandemic state’s impact on agriculture

Pandemic state’s impact on agriculture

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Sheeja Kumari, V. Wilfred Blessing, N.R. Rohith Bhat, C. Sudha, I. Michael, G. Manikandan, T. Department of Computational Intelligence – Institute of AI & ML SIMATS School of Engineering SIMATS University Chennai India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri Oman Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India

ISBN: (纸本)9781032669663

The COVID-19 outbreak has significantly disrupted the agricultural system in India. In spite of this, the most recent quarterly GDP forecasts post-COVID scenario demonstrate the tenacity and durability of the Indian agricultural sector, which is anticipated to be the only sector to record a positive growth rate of 4% for fiscal years (FY) 2020 and 2021. Similarly, growth for the most recent quarter is projected to have been 5.89%, representing a reduction of 2.4% point. In this context, our goal is to compile a summary of the early evidence on the impact of COVID-19 on the agricultural sector in India, including production, marketing, and consumption, and then to design a set of pragmatic strategies to recover and thrive after the pandemic. According to the survey’s findings, the pandemic has disrupted production and marketing through labour and logistical restrictions, while the negative income shock has restricted access to markets and increased food commodity prices, altering consumption patterns. The pandemic caused great emotional, social, economic, and bodily harm to everyone involved in India’s agriculture industry. The state has responded to the crisis by seizing the opportunity it presents and launching a variety of programmes and necessary changes. We suggest a ten-point plan that includes social safety nets, family farming, the trading of buffer stock, staggered procurement, and secondary agriculture in order to revitalise and prosper after the epidemic. © 2024, CRC Press/Balkema. All rights reserved.

关键词： COVID-19

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Predicting the friction angle of clays using a multi-layer perceptron neural network enhanced by yeo-johnson transformation and coral reefs optimization

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Journal of Rock Mechanics and Geotechnical engineering 2024年第10期16卷 3982-4003页

作者： Libing Yang Trung Nguyen-Thoi Trung-Tin Tran College of Civil Engineering and Architecture Hunan Institute of Science and TechnologyYueyang414006China Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh CityViet Nam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh CityViet Nam Department of Information Technology Greenwich VietnamFPT UniversityDa Nang550000Viet Nam

The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering *** study addresses the importance of estimating the friction angle and presents the development of four soft computing models:YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** of all,the Yeo-Johnson(YJ)transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal *** technique is expected to improve the accuracy of friction angle prediction *** friction angle prediction models then utilized multi-layer perceptron neural networks(MLPnet)and metaheuristic optimization algorithms to further enhance performance,including flower pollination algorithm(FPA),coral reefs optimization(CRO),ant colony optimization continuous(ACOC),and cuckoo search algorithm(CSA).The prediction models without the YJ technique,***-MLPnet,CRO-MLPnet,ACOC-MLPnet,and CSA-MLPnet,were then compared to those with the YJ technique,***-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** these,the YJ-CRO-MLPnet model demonstrated superior reliability,achieving an accuracy of up to 83%in predicting the friction angle of clay in practical engineering *** improvement is significant,as it represents an increase from 1.3%to approximately 20%compared to the models that did not utilize the YJ transformation technique.

关键词： Natural hazards Slope stability Friction angle Clay Soft computing models Geotechnical engineering

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