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Exploring Variational Auto-encoder Architectures, Configurations, and datasets for Generative Music Explainable AI

Machine Intelligence Research 2024年第1期21卷 29-45页

作者： Nick Bryan-Kinns Bingyuan Zhang Songyan Zhao Berker Banar School of Electronic Engineering and Computer Science Queen Mary University of LondonLondon E14NSUK Computer Science Department Carleton CollegeNorthfield MN 55057USA

Generative AI models for music and the arts in general are increasingly complex and hard to *** field of ex-plainable AI(XAI)seeks to make complex and opaque AI models such as neural networks more understandable to *** ap-proach to making generative AI models more understandable is to impose a small number of semantically meaningful attributes on gen-erative AI *** paper contributes a systematic examination of the impact that different combinations of variational auto-en-coder models(measureVAE and adversarialVAE),configurations of latent space in the AI model(from 4 to 256 latent dimensions),and training datasets(Irish folk,Turkish folk,classical,and pop)have on music generation performance when 2 or 4 meaningful musical at-tributes are imposed on the generative *** date,there have been no systematic comparisons of such models at this level of com-binatorial *** findings show that measureVAE has better reconstruction performance than adversarialVAE which has better musical attribute *** demonstrate that measureVAE was able to generate music across music genres with inter-pretable musical dimensions of control,and performs best with low complexity music such as pop and *** recommend that a 32 or 64 latent dimensional space is optimal for 4 regularised dimensions when using measureVAE to generate music across *** res-ults are the first detailed comparisons of configurations of state-of-the-art generative AI models for music and can be used to help select and configure AI models,musical features,and datasets for more understandable generation of music.

关键词： Variational auto-encoder explainable AI(XAI) generative music musical features datasets

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Attention Contours: A Comparative Study on 2D and Virtual Reality Attention Profile Assessment Tests 5

Attention Contours: A Comparative Study on 2D and Virtual Re...

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5th International Conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Rajarajeswari, P. Madhubala, R. Gomathi, V. Ramco Institute of Technology Department of Computer Science and Engineering Rajapalayam India National Engineering College Department of Computer Science and Engineering Kovilpatti India

ISBN: (纸本)9798350389609

Virtual Reality (VR) technology in health-care has emerged as a valuable tool for advancing diagnostic techniques and enhancing patient care. This research explores the application of VR in attention profile assessments, with a particular focus on diagnosing Attention Deficit/Hyperactivity Disorder (ADHD). ADHD poses challenges in maintaining attention, and traditional diagnostic approaches often lack real-world relevance. This study examines the efficacy of Attention Contours - a VR-based Continuous Performance Task (CPT) - in comparison to its conventional 2D (two-dimensional) counterpart for assessing attention profiles. Attention Contours VR incorporates varied virtual settings and language-neutral stimuli to improve ecological validity. User trials reveal greater engagement and fewer distractions in the VR environment, highlighting its potential for more accurate attention assessment. Participants showed a strong preference for the VR format, praising its immersive experience and enhanced usability. The findings emphasize the impact of VR in advancing attention assessment tools and suggest its promising role in ADHD assessment. © 2024 IEEE.

关键词： Virtual environments

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Innovative Approaches in Cataract Detection: Exploring Transfer Learning and Image Segmentation Techniques

Innovative Approaches in Cataract Detection: Exploring Trans...

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2024 International Conference on Smart Systems for Applications in Electrical sciences, ICSSES 2024

作者： Vijay, K. Vishnu, S. Sankar, Shreeram Manohar, Eashaan Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350364040

The natural lens of the eye gets foggy in cataracts, a common eye ailment that seriously harms vision and overall eye health. As the disease gets worse, it may block light from reaching the retina, developing clouded vision and possibly blindness if treatment has not been given. Effective medications for preserving the eye's clarity rely on early detection of cataracts. Our research aims to use innovative methods, especially deep learning and convolutional neural networks (CNNs), to deal with this difficulty. The creation of a computer-based system that may detect cataracts in eye pictures is made feasible by these precise algorithms. Through the use of CNNs, the system is able to identify complex patterns in these images and identify between eyes that are healthy and those that have cataracts. Drawing up an unbiased and accurate way for the timely identification of cataracts is the core objective of our research. An aid like this would improve overall eye care in addition to immediate medical care for this problem. By incorporating techniques from deep learning, we expect to discover a sustainable solution that can help medical professionals treat cataracts as quickly as possible, limiting the detrimental impact on their vision and brightening the lives of those who are affected. © 2024 IEEE.

关键词： Image segmentation

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The Role of Metaverse Technology in Education: A Framework for Implementation and Future Research

The Role of Metaverse Technology in Education: A Framework f...

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2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Sineka, P. Jothikumar, Karthikeyan Nivetha, V.M. Luckshika, M.K. Priyadharshini, P. Janani Ratthna, S.G. National Engineering College Department of Computer Science and Engineering TamilNadu India

ISBN: (纸本)9798350374711

In this article, our study aims to pioneer a transformative approach to online education by creating a Metaverse college environment. In this immersive 3D space, avatars fluidly represent both teachers and students, fundamentally altering the dynamics of virtual learning. Lectures transcend traditional boundaries through VR presentations, enriching engagement and understanding. Interactive quizzes inject a gamified element into assessments, fostering a dynamic and enjoyable learning journey. One of the standout features is the use of VR glasses for attendance tracking, ensuring precise reflection of real-world participation. This innovative fusion of cutting-edge technology and traditional teaching methodologies heralds a paradigm shift in remote education. Our platform is designed to provide students with an authentic college experience within the digital realm, fundamentally reshaping the landscape of virtual learning. By seamlessly integrating advanced technologies with tried-and-true educational practices, we aim to create an environment where students thrive and actively engage with course materials. This Metaverse college experience promises not only to enhance learning outcomes but also to redefine the possibilities of online education. Join us on this journey as we revolutionize the way we learn and teach in the digital age. © 2024 IEEE.

关键词： Virtual environments

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ARtViz: Enhancing Art Books with Marker-based Augmented Reality for 3D Visualization using Vuforia and Unity 5

ARtViz: Enhancing Art Books with Marker-based Augmented Real...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Abisha, D. Navedha Evanjalin, R. Ida Christy, J. Syed Rabiya, M. Bala Dinesh, S. National Engineering College Department of Computer Science and Engineering Kovilpatti India

ISBN: (纸本)9798350367171

ARtViz, a novel approach to augmenting traditional art books with marker-based augmented reality (AR) technology for immersive 3D visualization experiences. Leveraging Vuforia, a leading AR platform, in conjunction with Unity, a versatile game development engine, ARtViz offers an innovative way to engage readers with art books. By integrating 3D models and interactive elements, ARtViz transforms static images and text into dynamic, interactive exhibits that bring artworks to life. The process begins with the creation of marker images or symbols specific to each artwork or section within the art book. These markers act as triggers for the AR content and are uploaded to the Vuforia Image Target database. In Unity, the AR scene is set up with an ARCamera and GameObjects representing the 3D models corresponding to the artworks. When users scan the markers with a mobile device equipped with the ARtViz app, the ARCamera detects the markers and overlays the 3D models onto the pages of the art book. The benefits of ARtViz are manifold. Users can explore artworks from various angles, zoom in to see intricate details, and interact with the pieces through touch gestures. For educational purposes, ARtViz provides contextual information, artist biographies, and audio guides, enriching the learning experience. Moreover, ARtViz promotes accessibility by offering an inclusive platform for individuals with visual impairments, who can now experience art through tactile and auditory feedback. ARtViz represents a significant advancement in art book technology, bridging the gap between traditional printed media and digital interactivity. This project opens new avenues for art enthusiasts, educators, and museum visitors to engage with art in a dynamic and immersive manner. Future enhancements could include crowd-sourced content, user-generated AR experiences, and collaborations with art institutions for curated AR exhibitions. ARtViz stands at the forefront of the convergence of art, tec

关键词： Augmented reality

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A Survey on Collaborative Portal for Academic and Research Activities using Process Automation 4

A Survey on Collaborative Portal for Academic and Research A...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Revathy, P. Bhuvaneshwar, B. Sujitha, U. Vamsha Metilda, M.J. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307757

The current education system emphasizes research work and publications, urging students and faculty to actively engage in academic activities like workshops, seminars, symposium, and conferences. However, the lack of a centralized platform to access information about such events results in time and effort wastage, leading to missed opportunities for presentations and collaboration. To address this issue, a comprehensive portal is proposed which consolidates information about all academic activities across the country, universities. With the help of this portal, researchers, teachers, and students can easily plan ahead and engage meaningfullyin current events. The portal features multiple dashboards, each displaying information about forthcoming conferences, workshops, and symposiums that are organized by the government and non-government organizations. The websites of AICTE, Anna University, Smart India Hackathon, and IIT are used to scrape information about governmental events;Accenture Innovation and TCS Launchpad are used for non-governmental events. These events are filtered based on the user preferences and an email is sent. Thereby, the portal aims to increase the academic productivity. © 2024 IEEE.

关键词： Students

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A Novel Approach for Classification of Crops and Weeds using Deep Learning

A Novel Approach for Classification of Crops and Weeds using...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Ram Prakash, L. Pravinesh, H. Ariyamala, V. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

Weed detection is very important in modern agriculture, and timely and fast identification of weeds helps to reduce crop losses that have a positive effect on the yield. The classical methods of weed management involve manual weeding or broadcast spraying with herbicide, and both have laborious, expensive, and dismaying effects on the environment. The demand has never been higher for automatic weed detection systems in agriculture. Abstract: This project aims to build an automated weed detection system with YOLOv8, one of the most state-of-the-art object detection algorithms that excels in real-time accuracy. The system utilizes high-resolution drone and camera images of fields to pinpoint the specific plants growing in a crop, enabling more selective herbicide application and minimizing the need for manual labor from cropping operators. The design of YOLOv8 ensures high-speed and high-precision performance, making it ideal for use as a real-time detection system in the field. We train our model on a home-grown dataset of approximately 8k crop and weed images (gm), incorporating additional data augmentation for enhanced robustness. We will assess performance using evaluation metrics like precision, recall, F1-score, and mean average precision (mAP). © 2024 IEEE.

关键词： Deep learning

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Optimizing Customer Segmentation through Machine Learning 5

Optimizing Customer Segmentation through Machine Learning

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Julian, Anitha Hariprasath, S.R. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

In today's highly competitive business environment, the rise of new competitors and entrepreneurial ventures has intensified the race to attract new customers and retain existing ones. There has never been a greater need for outstanding customer service, regardless of the business volume. To meet customer expectations, businesses need to understand each and every one of their customers' needs. Achieving this goal requires tailoring customer support and developing personalized service plans. Methodical customer service techniques can facilitate this knowledge. Customer segmentation is a key strategy in this case. Through segmenting their customer base based on common market attributes, companies can develop marketing and service plans that are specific to each group. However, big data concepts and machine learning approaches have gained importance because traditional market analysis is frequently insufficient for a vast client base. These technologies improve the efficiency and accuracy of client segmentation by enabling its automation. K-Means clustering, a well-liked technique for unsupervised machine learning is employed. The Scikit-learn (Sklearn) library makes the K-Means algorithm easier to implement. Sklearn is a versatile Python library that is used extensively for data analysis and machine learning activities. It can be applied to a wide range of jobs. Utilizing a retail data set, the suggested work probably includes customer activity data like average number of purchases and monthly customer count. © 2024 IEEE.

关键词： classification algorithms cluster analysis clustering analysis customer profiling data preprocessing unsupervised learning

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Voice Assisted Facial Emotion Recognition System For Blind Peoples With Tensorflow Model

Voice Assisted Facial Emotion Recognition System For Blind P...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Ashmad Ahemed, S. Jayakumar, D. Ifet College of Engineering Department of Computer Science and Engineering Villupuram India

ISBN: (纸本)9798350348460

This research project presents the development of a novel Voice-Assisted Facial Emotion Recognition System tailored to address the unique needs of blind individuals. Leveraging the power of deep learning and the TensorFlow framework, the system employs computer vision techniques to detect and interpret facial emotions, enabling blind users to comprehend the emotional cues of those they interact with. The project encompasses data collection, preprocessing, model training, and evaluation, resulting in a robust TensorFlow model capable of real-time emotion recognition from facial images. A voice assistant integrates seamlessly into the system, offering spoken feedback to users, thus enhancing their understanding of emotional contexts during social interactions. The system prioritizes user accessibility and privacy, ensuring a user-friendly interface and adherence to data security standards. Extensive testing and user feedback are essential components of the project, allowing for continuous improvement and optimization to meet the specific needs of blind individuals. This research offers a promising solution to empower the visually impaired community by providing them with a valuable tool for enhanced social interaction and emotional understanding. Real-World Application, Emphasize the practicality and real-world applicability of the system, showcasing how it can make a meaningful impact on the daily lives of blind individuals by helping them navigate social and emotional contexts more effectively. Multimodal Interaction, Highlight the multimodal nature of the system, which combines visual facial emotion recognition with voice-based interaction to provide a comprehensive and accessible user experience. © 2024 IEEE.

关键词： Convolutional Neural Network Emotion identification Tensorflow

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Voice-Based Common Cold Detection 2

Voice-Based Common Cold Detection

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Premnath Babu, Hariesh Ramiya Arsha Fahima, T. Kavithadevi, M.K. Thiagarajar College of Engineering Computer Science and Engineering Department Madurai India

ISBN: (纸本)9798350328202

The identification of health-related issues without invasive testing constitutes a pivotal facet of medical research and technological advancement. This paper attempts to establish a very efficient and precise means of detecting cold symptoms via voice recordings, by comparing various machine learning methodologies and deep learning methodologies such as Convolutional Neural Networks (CNNs) by analysing alterations in one's vocal patterns. The method involves collecting voice samples from people with and without cold symptoms. During the pre-processing stage, the vibrations and various characteristics in the voice are measured. These characteristics capture the unique traits associated with changes in voice quality caused by cold-related effects which serve as input to the models. Time-tested metrics such as Accuracy, the Confusion Matrix, and the F1-score were used for evaluations. Beyond the quantitative, this study embodies our aspiration to tap into the unique intricacies of voice for proactive cold detection. This approach can lay the foundation for unobtrusive and economically viable health-monitoring tools. To conclude, this paper helps us to detect cold symptoms by a voice recording of a human using machine learning and neural network techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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