Computational Bioacoustic Artificial Intelligence
Description
This book deep dives into the theoretical background of bioacoustics, signal processing techniques, feature extraction and pattern recognition algorithms, and technically advanced case studies in bioacoustics AI. By exploring the intricate characteristics of bioacoustic signals, this book offers a comprehensive understanding of the underlying principles and practical implementations. The fundamental chapters provide readers the basics by discussing the statistical and deterministic models of bioacoustic signals, including parametric and non-parametric approaches, time-frequency representations, and stochastic processes. Furthermore, this book delves into the complexities of bioacoustic signal generation and propagation, considering physiological factors, acoustic media, and signal degradation. The feature engineering methodology upon the complex and noisy sound data is understood and explored using advanced signal processing techniques, such as wavelet transforms, matching pursuit, higher-order statistics, and fractal analysis. The subsequent chapters focus on feature engineering and pattern recognition. The feature extraction methods under subject of discussion include time-domain, frequency-domain, and time-frequency features, as well as statistical and structural features. The advanced techniques related to deep learning such as convolutional neural networks and recurrent neural networks are also explored. Traditional classification techniques, including statistical pattern recognition and syntactic pattern recognition, are covered, followed by a deep dive into the application of deep learning for bioacoustic classification. The later chapters detail on the futuristic topics such as bioacoustic localization, source separation, change detection, and monitoring. The bioacoustic data collected with other sensor modalities are significant in the development of bioacoustic indices. This book leads the assessment techniques to determine the quality of ecosystem and its performance. Furthermore, the application of bioacoustic AI in man-machine interaction is examined.
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Dr. S. Karthika, Associate Professor, in the Department of Information Technology has over 16 years of teaching and 1.5 years of exclusive research experience. To her credit she has 80+ research articles and delivered 20+ keynote talks in her research domain of computational science using AI. She heads the AI for Social Good (AISG) Research Lab and Cyber Security Working Group (CSWG). She is currently executing DST funded CSRI project and has also completed projects from TNSCST and SSN Trust. She has executed 2 consultancies and has published a US Patent. She has organized multiple International conferences, ATAL FDP, ACM Summer School and 25+ other events/activities. She has played many vitals roles as an academician and researcher by serving as Board of Studies (BoS), Doctoral Committee member and reviewer for reputed International and National journals and conferences. She is a recipient of UGC BSR Fellowship, Teacher Par Excellence awards and Intel oneAPI Innovator. She is an active ACM and Senior IEEE member. She is an ardent admirer of Indian dance and music art forms.
Balusamy is currently working as Associate Dean Academics in Shiv Nadar University, Delhi-NCR. Prior to this assignment, he was Professor, School of Computing Sciences & Engineering and Director International Relations at Galgotias University, Greater Noida, India. His contributions focus on Engineering Education, Block chain, and Data Sciences. His Academic degrees and twelve years of experience working as Faculty in a global university like VIT University, Vellore, has made him more receptive and prominent in his domain. He does have 200 plus high impact factor papers in Springer, Elsevier, and IEEE. He has done more than 80 Edited and authored books and collaborated with eminent professors across the world from top QS ranked universities. Balusamy has served up to the position of Associate Professor in his stint of 12 years of experience with VIT University, Vellore.
Prof. Chithirai Pon Selvan brings over 25 years of expertise in engineering education and academic leadership, with 17 years dedicated to advancing higher education in the UAE. He is a Senior Fellow of the Higher Education Academy (SFHEA), UK, and a Fellow of the Curtin Academy, Australia. His research interests include machine design, optimization techniques, advanced manufacturing, and renewable energy, with a strong focus on engineering for sustainable development. He has published 150+ research articles in journals, authored several books, and presented at numerous conferences. He is an approved PhD supervisor at several universities, including Curtin University, Australia, and has successfully supervised many doctoral candidates. Throughout his career, he has been invited and honored as a keynote speaker, session chair, resource person, and technical committee member at conferences across the UAE, India, Thailand, Malaysia, Germany, Maldives, Italy, Australia, Oman, Pakistan, Singapore, Qatar, and the UK. Prof. Pon has demonstrated distinguished leadership in advancing engineering education in the UAE. As the founding faculty member responsible for launching the Mechanical Engineering programs at Manipal University Dubai, and Amity University Dubai, he played a pivotal role in lab development, accreditation, industry collaboration, student success, and academic leadership for over a decade. He is also the founding academic of the Mechanical Engineering program at Curtin University Dubai, where he currently serves. In addition, he is a member of leading professional societies such as SAE, ASHRAE, IMechE, ASME, IEEE, EI, ASQ, and ISTE.
Dr. Feslin Anish Mon, Researcher specializing in Trust-based Security in IoT Networks, holds a Ph.D. in Information and Communication Engineering. With good publications in reputed journals and a patented contribution to his name, Dr. Feslin has notable contribution in academia as well. Currently, he serves in the Department of Information Technology at the University of Technology and Applied Sciences, Ibri Branch, where he actively contributes to research and inspires students and colleagues alike. His work reflects a commitment to advancing knowledge and innovation in the dynamic field of information technology.
Dr. Chander Prabha is working as a Professor in the Department of Computer Science and Engineering at Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India. She had done her Ph.D. from M.M. University in Computer Science Engineering in 2017 and her master's in Engineering from Punjab Engineering College (2004), PEC, Chandigarh, India. A total of 21+ years of academic/research experience with more than 110+ publications in various National, International Conferences, and International Journals. She has published many book chapters in CRC press. She is the guest editor journal of Artificial Intelligence and Internet of Things, Near East University, Turkey. Also, she is a reviewer of many international conferences and journals of repute. Many online certifications are also there in her credit. She is a lifetime member of educational societies like ISTE, IAENG, SCIEI, IIRJC, IACSIT, etc.
Content
1. Advanced Signal Processing Techniques in Bioacoustics by Elevating Wildlife Monitoring.- 2.Leveraging Advanced Deep Learning Techniques for Enhanced Bioacoustic Classification.- 3. Deep Neural Networks and Wavelet Transforms for Bioacoustic Classification.- 4. Ethical Considerations and Societal Impact.- 5. OPTIMIZING BIOACOUSTIC SIGNAL CLASSIFICATION USING AI-ENHANCED TRADITIONAL ALGORITHMS.- 6. AI-POWERED BIOACOUSTIC METHODS FOR EARLY WARNING OF NATURAL DISASTERS THROUGH ANIMAL BEHAVIORAL CUES.- 7. Artificial Intelligence in Agriculture: Technology-Powered Strategies for a Sustainable Farming Future.- 8. Optimizing Bioacoustic Classification: Deep Metric Learning and Contrastive Embedding Techniques for Unlabeled Acoustic Data.- 9. Integrating Bioacoustic Sensors for Improved Human-Computer Interaction.- 10. Recent Advances in Bioacoustic Data Fusion and Multimodal Analysis.- 11. Acoustic Analysis: Traditional Classification Methods in Bioacoustics.- 12. Enhancing Human-Computer Interaction with Bioacoustics: Techniques and Applications.- 13. Bioacoustics Feature Engineering: Techniques and Applications for Ecological Insight.- 14. Detection of Ecosystem Changes Using Bioacoustics Techniques.- 15. Advanced Techniques for Ecological Research using Bioacoustic Signal Processing.- 16. Ethical and Privacy Considerations in AI-Driven Wildlife Monitoring.