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Disentangling Prosody Representations With Unsupervised Speech Reconstruction

IEEE/ACM Transactions on Audio Speech and Language Processing 2024年 32卷 39-54页

作者： Qu, Leyuan Li, Taihao Weber, Cornelius Pekarek-Rosin, Theresa Ren, Fuji Wermter, Stefan Institute of Artificial Intelligence Zhejiang Lab Hangzhou311121 China Department of Informatics University of Hamburg Hamburg20148 Germany School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

Human speech can be characterized by different components, including semantic content, speaker identity and prosodic information. Significant progress has been made in disentangling representations for semantic content and speaker identity in speech recognition and speaker verification tasks respectively. However, it is still an open challenging question to extract prosodic information because of the intrinsic association of different attributes, such as timbre and rhythm, and because of the need for supervised training schemes to achieve robust speech recognition. The aim of this article is to address the disentanglement of emotional prosody based on unsupervised reconstruction. Specifically, we identify, design, implement and integrate three crucial components in our proposed model Prosody2Vec: (1) a unit encoder that transforms speech signals into discrete units for semantic content, (2) a pretrained speaker verification model to generate speaker identity embeddings, and (3) a trainable prosody encoder to learn prosody representations. We first pretrain Prosody2Vec on unlabelled emotional speech corpora, then fine-tune the model on specific datasets to perform Speech Emotion Recognition (SER) and Emotional Voice Conversion (EVC) tasks. Both objective and subjective evaluations on the EVC task suggest that Prosody2Vec effectively captures general prosodic features that can be smoothly transferred to other emotional speech. In addition, our SER experiments on the IEMOCAP dataset reveal that the prosody features learned by Prosody2Vec are complementary and beneficial for the performance of widely used speech pretraining models and surpass the state-of-the-art methods when combining Prosody2Vec with HuBERT representations. Audio samples can be found on our demo website. © 2023 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

关键词： Speech recognition

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Recommending Public Transit Route: Ant-Colony Optimization or Dijkstra?

Recommending Public Transit Route: Ant-Colony Optimization o...

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2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Jabbar, Abdul Pinandito, Aryo Universitas Brawijaya Faculty of Computer Science Informatics Engineering Department Malang Indonesia Universitas Brawijaya Faculty of Computer Science Information System Department Malang Indonesia

ISBN: (纸本)9798350369359

Advances in algorithmic studies have been made recently, especially in the area of finding the shortest path. Despite taking a greedy approach, the Dijkstra algorithm is well recognized for its efficacy and has established itself as the industry standard for identifying the shortest path in a graph. Conversely, scientists have embraced the Ant-Colony Optimization (ACO) approach, which draws inspiration from the behavior of ants. This study examines the two methods and determines if, given equivalent resources, ACO can generate Dijkstra-like recommendations. Research data is subjected to both algorithms, and the outcomes are presented visually. In data measurement, Dijkstra is processed first to determine the best path, and then the ACO method is applied to produce findings that are similar to Dijkstra. In data measurement, Dijkstra is processed first to determine the best path, and then the ACO method is applied to produce findings that are similar to Dijkstra. Memory consumption, computation time, initial memory allocation, and final memory allocation are evaluation parameters. ACO is more efficient than Dijkstra in terms of execution time and number of pathways traversed, according to the study's repeated evaluation methodology. This iterative assessment procedure is carried out to address the possibility of single measurements resulting from the JavaScript Garbage Collector. The study's findings imply that a larger memory may be necessary for ACO to deliver the desired outcomes. These results could also be impacted by each algorithm's distinct qualities. © 2023 IEEE.

关键词： Ant colony optimization

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Adaptive 360° Video Streaming Over Wireless Communication Channels 9

Adaptive 360° Video Streaming Over Wireless Communication C...

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9th International Conference on Frontiers of Signal Processing, ICFSP 2024

作者： Valiandi, Ioanna Pattichis, Marios S. Kyriacou, Efthyvoulos Panayides, Andreas S. VIDEOMICS Group CYENS Centre of Excellence Nicosia Cyprus University of New Mexico Department of Electrical and Computer Engineering AlbuquerqueNM United States Cyprus University of Technology Department of Electrical Eng. Computer Engineering and Informatics Limassol Cyprus

ISBN: (纸本)9798350353235

360° video streaming is one of the prevalent communication technologies for enhancing user experience and has thus seen widespread adoption in virtual and mixed reality applications. However, delivering content at scale while securing the quality of wirelessly communicated 360° videos in real-time poses significant challenges. 360° videos come in ultra-high definition, necessitate unprecedented bitrate demands and involve high encoding complexity. The time-varying nature of underlying wireless channels further introduces a destabilizing factor, calling for video systems to seamlessly adjust to varying bandwidth throughput to maintain adequate quality of service and experience. To address this issue, in this study, we have developed a multi-objective optimization framework for real-time video encoding adaptation. The objective is to optimize both video quality and encoding efficiency while minimizing the required bitrate, subject to real-time application constraints. To achieve this, we relied on generating (offline) precise forward prediction models of video quality, bitrate demands, and encoding time, that can be used to select the optimum encoding configuration in real-time. To validate our methods, we implemented an adaptive video encoding controller, and ran emulations employing actual network traces from 5G mobile video streaming scenarios, using the popular open-source x264 and x265 codecs for video encoding. A dataset of 4K omnidirectional videos at 30 frames per second was used. © 2024 IEEE.

关键词： Video streaming

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Augmenting Internet of Medical Things Security:Deep Ensemble Integration and Methodological Fusion

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computer Modeling in engineering & Sciences 2024年第12期141卷 2185-2223页

作者： Hamad Naeem Amjad Alsirhani Faeiz MAlserhani Farhan Ullah Ondrej Krejcar Faculty of Informatics and Management Center for Basic and Applied ResearchUniversity of Hradec KraloveHradec Kralove50003Czech Republic Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Engineering&Networks College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Cybersecurity Center Prince Mohammad Bin Fahd UniversityAl JawharahKhobarDhahran34754Saudi Arabia

When it comes to smart healthcare business systems,network-based intrusion detection systems are crucial for protecting the system and its networks from malicious network *** protect IoMT devices and networks in healthcare and medical settings,our proposed model serves as a powerful tool for monitoring IoMT *** study presents a robust methodology for intrusion detection in Internet of Medical Things(IoMT)environments,integrating data augmentation,feature selection,and ensemble learning to effectively handle IoMT data *** rigorous preprocessing,including feature extraction,correlation removal,and Recursive Feature Elimi-nation(RFE),selected features are standardized and reshaped for deep learning *** using the BAT algorithm enhances dataset *** deep learning models,Transformer-based neural networks,self-attention Deep Convolutional Neural Networks(DCNNs),and Long Short-Term Memory(LSTM)networks,are trained to capture diverse data *** predictions form a meta-feature set for a subsequent meta-learner,which combines model *** classifiers validate meta-learner features for broad algorithm *** comprehensive method demonstrates high accuracy and robustness in IoMT intrusion *** were conducted using two datasets:the publicly available WUSTL-EHMS-2020 dataset,which contains two distinct categories,and the CICIoMT2024 dataset,encompassing sixteen *** results showcase the method’s exceptional performance,achieving optimal scores of 100%on the WUSTL-EHMS-2020 dataset and 99%on the CICIoMT2024.

关键词： Cyberattack ensemble learning feature selection intrusion detection smart cities machine learning BAT augmentation

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Implementation of the SMART Method and Location Based Service Technology in a Sewing Service Search Application 9

Implementation of the SMART Method and Location Based Servic...

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9th IEEE Information Technology International Seminar, ITIS 2023

作者： Mumpuni, Retno Santoso, Bagus Jati Wahanani, Henni Endah Rahajoe, Ani Dijah Maulana, Hendra Maghfiro, Feronika Nur Universitas Pembangunan Nasional 'Veteran' Jawa Timur Faculty of Computer Science Department of Informatics Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia

ISBN: (纸本)9798350306835

The tailoring business involves services where tailors create or repair clothing. Currently, the number of tailors with various skill sets is on the rise. However, customers often struggle to find tailors who meet their specific needs. They have to spend time visiting tailor shops, just to assess whether a tailor is a suitable match or to place an order, even when the distance and location are uncertain. This inefficiency hampers the process. In this rapidly advancing technological era, many businesses leverage technology to promote their services, including the tailoring industry. To address this issue, the author aims to develop an Android-based Tailor Locator Application that utilizes Location Based Services (LBS) and the SMART Method. This application will utilize LBS technology to display the distance and location between customers and tailors, while continuously updating the location information of both customers and tailors. The application will also implement the SMART method to rank tailors based on ratings and provide tailor recommendations. © 2023 IEEE.

关键词： Location based services

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Evaluating Non-Expert Stakeholder Interaction with Artificial Intelligence on Energy Urban Domain Using VIRTSI: The Case of ChatGPT 15

Evaluating Non-Expert Stakeholder Interaction with Artificia...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Tsihrintzis, George A. Sarmas, Elissaios Marinakis, Vangelis Panagoulias, Dimitrios Tsihrintzi, Evangelia-Aikaterini Virvou, Maria University of Piraeus Department of Informatics Piraeus Greece School of Electrical & Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Greece National and Kapodistrian University of Athens Department of Informatics and Telecommunicatios Athens Greece

ISBN: (纸本)9798350368833

This paper investigates the interaction of non-expert stakeholders with Artificial Intelligence (AI) in the energy urban domain, using the VIRTSI model and focusing on the capabilities of ChatGPT. VIRTSI (Variability and Impact of Reciprocal Trust States towards Intelligent systems), is a rigorous computational model for human-AI Interaction that simulates human trust states, spanning from overtrust to distrust, through user modelling and quantifies the efficiency of the interaction in VIRTSI-adapted confusion matrices. The research employed an 16-question survey, evaluating the accuracy and usefulness of ChatGPT's responses regarding energy consumption, cost-effective solutions, and renewable energy production for residential buildings in Greece. Each answer was assessed by human stakeholders who were non-expert in the energy Urban Domain (e.g house owners, building managers, etc.), who either accepted or rejected the responses based on validation processes. The analysis highlighted key aspects such as repetition, specification, and objections in the interaction with ChatGPT, offering insights into the effectiveness of AI in supporting energy-related decisions. The findings reveal that while AI can provide valuable information, user validation and expert consultation are critical for practical implementation. This highlights both the potential and limitations of integrating AI tools like ChatGPT in enhancing non-expert stakeholder engagement and decision-making in urban energy management, emphasizing the ongoing need for human expert involvement. © 2024 IEEE.

关键词： Decision making

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Fingerprint Matching for Noisy and Distorted Patterns using a Siamese Network with ResNet50 and Multihead Attention

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IEEE Access 2025年 13卷 88047-88064页

作者： Sawhney, Tinny Sharma, Ashu Abrol, Pawanesh Lehana, Parveen Kumar Chaahat Yadav, Vaishali Scherer, Rafal Kulakov, Oleksii AlZubi, Ahmad Ali University of Jammu DSP Lab Department of Electronics India University of Jammu Department of Computer Science and IT India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India Manipal University Jaipur Jaipur India Czestochowa University of Technology Department of Artificial Intelligence Częstochowa Poland National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Faculty of Informatics and Software Engineering Kyiv Ukraine King Saud University Community College Department of Computer Science Riyadh Saudi Arabia

Dermatoglyphics, the study of unique ridge patterns on fingertips, plays a crucial role in fingerprint-based identification. However, skin conditions such as psoriasis, eczema, and verruca vulgaris can distort these patterns. This distortion creates challenges for fingerprint matching systems in forensic science and biometric authentication. This paper proposes a Siamese network with a modified ResNet50 architecture and multihead attention mechanisms to improve fingerprint matching under such distortions. The Siamese network enables the system to compare fingerprint pairs effectively, even under distortions. The modified ResNet50 architecture captures intricate ridge patterns, while the multihead attention mechanisms focus on critical fingerprint regions. Overall, the proposed approach enhances the system’s ability to learn discriminative features. As a result, it can effectively differentiate between matched and unmatched fingerprint pairs, even in the presence of moderate to extreme noise. The proposed system was trained on the Sokoto coventry fingerprint dataset (SOCOFing) and a custom dataset. Experimental results demonstrate high accuracy, with the system achieving 99.47% accuracy under low noise conditions and 90% under moderate noise. However, performance declined to 55.56% under extreme distortions. A comparative analysis highlighted the superiority of the proposed system over widely used minutiae-based methods. The latter exhibited a significant drop in matching scores, falling below 15% for highly distorted fingerprints. These findings underscore the limitations of traditional fingerprint recognition techniques and highlight the effectiveness of proposed approach in handling dermatoglyphic distortions. © 2013 IEEE.

关键词： Feature Selection

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Implementation of Convolutional Neural Network for COVID19 Screening using X-Rays Images

Implementation of Convolutional Neural Network for COVID19 S...

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2023 International Conference on computer Science, Information Technology and engineering, ICCoSITE 2023

作者： Delimayanti, Mera Kartika Mardiyono, Anggi Warsuta, Bambang Puspitaningrum, Eka Suci Naryanto, Rizqi Fitri Naryaningsih, Agustien Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Indonesia Universitas Negeri Semarang Faculty of Engineering Department of Mechanical Engineering Indonesia Fisheries Research Center Indonesia

ISBN: (纸本)9798350320954

Various unwelcome conditions have existed since the introduction of the novel coronavirus illness (COVID-19). COVID-19 can cause fever, muscle soreness, shortness of breath, cough, headache, and other symptoms. Diagnosis at an early stage is a crucial aspect of successful treatment. Therefore, it is necessary to seek out alternate methods for COVID-19 detection. Among the existing imaging resources, X-ray images are generally accessible and inexpensive. Consequently, an alternate diagnostic tool for detecting COVID-19 instances is provided using available resources. In the first stages of COVID-19, X-rays detected the disease before it spread to the lungs and caused more damage. Machine learning models can help clinicians accomplish jobs more quickly and accurately. In addition to chest X-ray pictures and fundus images, deep learning algorithms have been used to diagnose illnesses. This research was conducted to classify the X-ray chest images in COVID-19 and normal cases based on the public datasets which were used. This analysis uses 5600 images from the accessible resources, and a Convolutional Neural Network (CNN) architecture with the VGG16 algorithm was employed to diagnose COVID19. VGG16 is object identification and classification method that can classify with greater precision than most other deep learning algorithms. Transfer learning and fine-tuning were employed to help for improving the performance. The results showed that the VGG16 network had an accuracy of 98.13%. This research has implications for the early detection of COVID-19 by using X-ray images. The experiment and analysis reveal our suggested method's promising and stable performance compared to the current standard. © 2023 IEEE.

关键词： COVID-19

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Overview of Load Disaggregation Models, Methods and Hardware 59

Overview of Load Disaggregation Models, Methods and Hardware

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59th International Universities Power engineering Conference, UPEC 2024

作者： Lancranjan, Mircea N. Muresan, Alexandru Jurj, Dacian I. Berciu, Alexandru Polycarpou, Alexis Czumbil, Levente Micu, Dan D. Technical University of Cluj-Napoca Department Electrical Engineering Cluj-Napoca Romania Technical University of Cluj-Napoca Faculty of Automation and Computer Science Cluj-Napoca Romania Department of Electrical and Computer Engineering and Informatics Frederick University Nicosia Cyprus

ISBN: (纸本)9798350379730

In the dynamic landscape of energy efficiency, a domain garnering ever increasing attention and urgency, the cornerstone of informed decision-making resides in the acquisition of reliable data. This paper summarizes the main approaches, methods and hardware used to gather, process and disaggregate such data on an appliance-by-appliance basis. The paper explores a critical analysis of the various models and techniques that support the gathering of energy data at the appliance level. A range of approaches, including hardware-based solutions such as smart meters, submeters, and sensor arrays, placing a special focus on intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM) algorithms that employ advanced signal processing and machine learning techniques, are examined for their effectiveness, scalability, and suitability for both industrial and commercial contexts. Furthermore, the challenges of data acquisition capabilities, reconciling scalability, cost-effectiveness, and user-friendliness - prerequisites for widespread acceptance and uptake are also investigated. © 2024 IEEE.

关键词： AI Data Cleaning Energy Efficiency Energy Signature Load Disaggregation Load Monitoring NILM

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Characterizing land deformations in Western Greece with satellite remote sensing observations and in situ ground monitoring 14

Characterizing land deformations in Western Greece with sate...

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Earth Resources and Environmental Remote Sensing/GIS Applications XIV 2023

作者： Fasoulis, Konstantinos Triantafyllidis, Panagiotis Hadjidoukas, Panagiotis Pappas, Christoforos Department of Civil Engineering University of Patras Patras265 04 Greece Department of Computer Engineering and Informatics University of Patras Patras265 04 Greece

ISBN: (纸本)9781510666979

The area of Western Greece and the Peloponnese region show land deformations that can be triggered, for example, by seismic activity (e.g., Corinthian Gulf faults) and environmental factors (e.g., heavy rainfall). Land subsidence observed during the recent years within the Campus of the University of Patras in Northwestern Peloponnese confirms these spatiotemporal dynamics, with pronounced effects on the existing infrastructure and facilities. Here, we combined satellite remote sensing techniques and in situ geodetic monitoring to better understand these patterns and possible driving mechanisms. Regional-scale land deformations were quantified with active microwave remote sensing techniques (Persistent Scatterer Interferometric Synthetic Aperture Radar, PSInSAR) based on SAR data from the European Space Agency Sentinel-1 constellation of satellites. Land cover dynamics were characterized with high-resolution optical remote sensing observations (Normalized Difference Vegetation Index, NDVI;Planet Labs). The spatiotemporal Sentinel-1 SAR data were processed using the SNAP-StaMPS (SentiNel Application Platform-Stanford Method of Persistent Scatterer) integrated workflow for Persistent Scatterers Interferometry. The PSInSAR analysis resulted in regional-scale displacements of up to a few tens of mm per year. These displacements patterns were explored in tandem with possible land cover changes as detected with the NDVI spatiotemporal dynamics and further refined at the local scale with repeated field surveys during 2022 and 2023 at selected locations within the Campus of the University of Patras that were affected by land subsidence. The combination of remote sensing techniques, together with in situ ground monitoring, provides unique opportunities for monitoring ground deformation in almost real-Time, facilitating thus the assessment of the response of infrastructures to such spatiotemporal changes. © 2023 SPIE. All rights reserved.

关键词： Interferometry

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