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Eco-Acoustic Intelligence

Innovations, Trends and Applications
Edited by Chandra Singh, Navaneeth Bhaskar, Shwetha N., and Canute Sherwin
Copyright: 2026 | Expected Pub Date: 2026
ISBN: 9781394403516 | Hardcover |
550 pages
Price: $225 USD

One Line Description
Discover how listening to the planets hidden soundscapes can revolutionize conservation with this groundbreaking guide showing how acoustic data is driving the next wave of environmental technology and sustainability.

Description
As the human population grows, it is becoming increasingly enmeshed with the environment. The emerging field of eco-acoustics analyzes the relationship between human-made and natural soundscapes from an ecological perspective. This book delves into how these soundscapes interact, influencing biodiversity, environmental monitoring, and sustainability efforts. It presents an interdisciplinary approach, combining principles from ecology, bioacoustics, artificial intelligence, and environmental science to offer a comprehensive understanding of how sound can be harnessed for conservation and technological innovation. The book introduces foundational concepts before advancing into emerging trends such as machine learning-driven sound analysis, real-time acoustic monitoring, and bio-inspired auditory technologies. Case studies and real-world applications highlight how eco-acoustic intelligence is being used to track species populations, detect environmental changes, and develop smart conservation strategies. The book also discusses ethical considerations, technological challenges, and future directions in the field, ensuring a well-rounded exploration of its subject matter. By integrating cutting-edge research with practical applications, the book provides insights into the role of acoustic data in assessing ecosystem health, mitigating noise pollution, and shaping policies for a more sustainable future.

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Author / Editor Details
Chandra Singh is an Assistant Professor in the Department of Electronics and Communication at the Nitte Mahalinga Adyantaya Memorial Institute of Technology, Nitte, India with more than six years of experience. He has published nine books, 15 book chapters, and more than 20 research articles in reputed peer-reviewed national and international journals, as well as 11 patents. His areas of interest are optical networking and communication, wireless communication, intelligent systems, IoT, and robotics.

Navaneeth Baskar, PhD is a faculty member in the Department of Artificial Intelligence and Data Science at Nitte Mahalinga Adyantaya Memorial Institute of Technology, Nitte, India. He has delivered multiple technical talks on AI and machine learning, published extensively in reputed journals and conferences, and filed several patents in India and abroad. His research interests include artificial intelligence, machine learning, data analytics, IoT, and signal processing.

Shwetha N., PhD is an Assistant Professor in the Department of Electronics and Communication Engineering at the Dr. Ambedkar Institute of Technology, Bangalore, Karnataka, India. She completed her doctoral degree at Visveswaraya Technological University and holds a post-graduate degree in signal processing from the Reva Institute of Technology and Management. Her research interests include signal processing, computational intelligence, communication, artificial neural networks, and wireless sensor networks.

Canute Sherwin, PhD is an Assistant Professor in the Department of E-Mobility at Atria University, Bengaluru, India with more than 12 years of experience. He has authored and co-authored more than 14 peer-reviewed research articles in reputed journals and two book chapters. He has made a significant contributions to advancing the areas of metal coatings, electrochemistry, grain refinement, modification of alloys, sustainable technologies, and mechatronic systems.

Table of Contents
Preface
Part I: Foundations of Eco-Acoustic Intelligence
1. Sonic Biometrics and Pattern Recognition in Eco-Acoustic Intelligence
Roopesh Ramesh, Priyanka S. and Kalyan N.
1.1 Introduction
1.1.1 Motivation and Background
1.1.2 Technical Challenges in Sonic Pattern Recognition
1.1.3 Comparison Table: Techniques in Sonic Pattern Recognition
1.1.4 Contribution of this Chapter
1.2 Mathematical Foundations of Sonic Biometrics
1.2.1 Eco-Acoustic Signal Modeling
1.2.2 Time-Frequency Analysis (STFT, Wavelet)
1.2.3 Feature Extraction Techniques (MFCC, Entropy, ZCR)
1.2.4 Mathematical Representation of Biometrics
1.3 Acoustic Sensing and Signal Processing Pipeline
1.3.1 Hardware and Sensor Technologies
1.3.2 Signal Preprocessing Steps
1.3.3 Flowchart of the Acoustic Pipeline
1.3.4 Sensor Comparison Table
1.4 Pattern Recognition Algorithms in Eco-Acoustic Tasks
1.4.1 Traditional Models (HMM, GMM, SVM)
1.4.2 Deep Learning Models (CNN, RNN, Transformers)
1.4.3 Equations and Architectures of Each Model
1.4.4 Comparative Performance Table
1.5 End-to-End Biometric System Architecture
1.5.1 System Design and Flow
1.5.2 Real-Time vs Offline Processing
1.5.3 Eco-ID Algorithm: Flowchart and Pseudocode
1.5.4 Case Studies
1.6 Case Studies and Applications
1.6.1 Rainforest Biodiversity Indexing
1.6.2 Marine Mammal Detection
1.6.3 Urban Ecoacoustic Monitoring
1.6.4 Results and Metrics Table
1.7 Evaluation Metrics and Benchmarking in Eco-Acoustic Pattern Recognition
1.7.1 ROC Curve and Confusion Matrix
1.7.2 Precision, Recall, and F1-Score
1.7.3 Equal Error Rate (EER) and Signal-to-Noise Ratio (SNR)
1.8 Datasets, Tools, and Resources in Eco-Acoustic Pattern Recognition
1.8.1 Open Eco-Acoustic Datasets
1.8.2 Dataset Characteristics Table
1.8.3 Tools and Libraries (Python, MATLAB, Etc.)
1.8.4 Data Acquisition and Training Flowchart
1.9 Future Directions
1.9.1 Federated Learning in Eco-Acoustics
1.9.2 Acoustic Scene Synthesis
1.9.3 Multi-Modal Data Fusion
1.9.4 Ethical Considerations
1.10 Conclusion
1.10.1 Summary of Contributions
1.10.2 Role in Sustainability and Conservation
1.10.3 Final Thoughts
References
2. Artificial Intelligence and Machine Learning in Eco-Acoustics
Sai Venkatramana Prasada G. S. and Rashmi P. C.
2.1 Introduction
2.2 Types of Eco-Acoustic Data
2.2.1 Importance of Soundscape Analysis
2.3 AI and ML Overview
2.3.1 Relevance to Acoustic Data
2.4 ML Techniques in Eco-Acoustics
2.4.1 Classical ML Algorithms
2.5 Applications
2.5.1 Species Identification
2.5.2 Biodiversity Assessment
2.5.3 Habitat Quality Monitoring
2.5.4 Anomaly Detection
2.5.5 Temporal Activity Patterns
2.6 Advanced Techniques
2.6.1 Transfer Learning
2.6.2 Data Augmentation
2.6.3 Semi-Supervised Learning
2.6.4 Generative Models
2.7 Integration with IoT and Edge AI
2.7.1 Acoustic Sensor Networks
2.7.2 Edge Computing
2.7.3 Cloud Integration
2.8 Challenges and Solutions
2.8.1 Data Scarcity and Label Noise
2.8.2 Environmental Variability
2.8.3 Interpretability
2.8.4 Ethical and Privacy Concerns
2.9 Future Directions
2.9.1 Federated Learning: Collaborative Model Training without Centralizing Data
2.9.2 Citizen Science: Engaging the Public in Data Collection via Mobile Apps
2.9.3 Edge AI Optimization: Designing Energy-Efficient Models for On-Device Inference
2.9.4 Real-Time Eco-Feedback: Automated Alerts for Conservation Interventions
2.10 Conclusion
Bibliography
3. Exploration of Landslide Prediction Using Machine and Deep Learning Techniques
Niveditha M., Sowmiya Sri B., Poornashree H., Rakshitha M.D., Sanjeevakumar M. Hatture, Rashmi P. Karchi and Usha Desai
3.1 Introduction
3.2 Literature Survey
3.3 System Analysis
3.4 Methodology
3.5 Implementation
3.6 Experimentation
3.7 Conclusion
References
4. AI and ML in Ecoacoustics: A New Era of Environmental Monitoring
Jyoti Omprakash Gautam, Parvez Khan and Homen Lahan
4.1 Introduction
4.1.1 Defining Eco-Acoustics: An Expanding Lens on Ecosystems
4.1.2 Why Acoustic Monitoring Matters: Beyond Human Perception
4.2 The Evolution and Promise of Eco-Acoustics
4.2.1 Technological Journey: From Tape to Terabytes
4.2.2 The AI/ML Revolution in Response to Data Overload
4.3 Tools, Techniques, and Strategies: Capturing Environmental Soundscapes
4.3.1 Diverse Acoustic Recorders Tailored to Ecological Contexts
4.3.2 Deployment Strategies for Optimized Monitoring
4.4 From Raw Audio to Usable Data: Preprocessing and Standardization
4.4.1 The Challenge of Environmental Noise
4.4.2 Pre-Processing Pipelines
4.5 Machine Learning Approaches in Eco-Acoustics
4.5.1 Overview of Algorithms, Applications, and Performance
4.5.2 Model Training, Validation, and Transferability
4.6 Ecological Applications and Insights from AI-Driven Eco-Acoustics
4.6.1 Measuring Ecosystem Biocomplexity and Connectivity
4.6.2 Automated Taxonomic Monitoring Across Taxa
4.6.3 Case Studies: BirdNET and Rainforest Connection
4.6.4 AI across Ecosystems: Terrestrial, Marine, and Urban Soundscapes
4.7 Acoustic Indices: Simplifying Complex Soundscapes
4.7.1 Role and Types of Acoustic Indices
4.7.2 Integration with Machine Learning (ML)
4.8 Current Challenges and Ethical Considerations
4.8.1 Scientific and Technical Hurdles
4.8.2 Ethical Issues in Acoustic Monitoring
4.9 Future Directions in Eco-Acoustic Monitoring
4.9.1 Integration with Internet of Things and Remote Sensing
4.9.2 Advances in AI Algorithms and Computing
4.10 Conclusion
Bibliography
5. AI-Enhanced Eco-Acoustic Systems Integrating IoT for Efficient Ecosystem Monitoring
Dankan Gowda V., Srinivas D., K.D.V. Prasad, T. Kavitha and Franklin Jino R. E.
5.1 Introduction
5.2 Literature Survey
5.3 Fundamentals of Eco-Acoustic Systems
5.4 Integration of IoT in Eco-Acoustic Systems
5.5 Role of AI in Enhancing Eco-Acoustic Systems
5.6 Case Studies and Applications
5.7 Results of Discussion
5.8 Conclusion
References
6. Bio-Inspired Machine Learning Models for Eco-Acoustic
Signal Processing
Dankan Gowda V., Galiveeti Poornima, Srinivas D., K.D.V. Prasad and D. Vengaimarbhan
6.1 Introduction
6.2 Literature Survey
6.3 Machine Learning in Eco-Acoustics
6.4 Bio-Inspired Machine Learning Models
6.5 Methodology
6.6 Results and Discussion
6.7 Conclusion
References
Part II: Enabling Technologies and Smart Systems
7. Noise Pollution and Its Impact with IoT-Based Monitoring and Machine Learning Solutions
Dankan Gowda V., Puja Roshani, K.D.V. Prasad, M. Sathyanarayanan and Manojkumar S. B.
7.1 Introduction
7.2 Literature Survey
7.3 IoT-Based Noise Monitoring Systems
7.4 Machine Learning Techniques in Noise Analysis
7.5 Results Discussion
7.6 Conclusion
References
8. Smart Eco-Acoustic Networks Using IoT and Machine Learning for Environmental Sustainability
Dankan Gowda V., S.V. Ramanan, K.D.V. Prasad, Sagar Choudhary and K. Sivakumar
8.1 Introduction
8.2 Integration of Machine Learning
8.3 Background and Motivation
8.4 Literature Survey
8.5 System Architecture
8.6 Applications and Use Cases
8.7 Challenges in Implementation
8.8 Results and Discussion
8.9 Conclusion
References
9. Speech Enhancement through U-Net Architecture for Noise Suppression
Saumya Y. M., Vinay P., Abner Stan Fernandez, Alden Crist Rego, B. Ashish Shenoy and Eyan Leroy Sequeira
9.1 Introduction
9.2 Literature Survey
9.3 Methodology
9.4 Results and Discussion
9.5 Conclusion
References
10. Hybrid Deep Learning Models for Biodiversity Monitoring
in Rainforest Environments Using Eco Acoustic
Manjula Gururaj Rao, Ashwini B., Vaikunta Pai, Nagana Chetty, Rashmi P. Shetty, Priyanka H., Deepa Shetty and Chinmai Shetty
10.1 Introduction
10.2 Literature Survey
10.3 Methodology
10.4 Results and Discussion
10.5 Conclusion
References
Part III: Applications in Environmental Monitoring
11. SmartGuard: AI-Driven Real-Time Detection and Acoustic Alerts for Enhanced Forest Protection
K.V.N.D. Sushma, Nithya Madhasu, Praveen Abhi Vamsi Kodali, K. G. Suma and Usha Desai
11.1 Introduction
11.1.1 Contributions
11.2 Methodology
11.2.1 Dataset Description
11.2.2 Pre-Processing
11.2.3 Yolo v11
11.2.4 VITS Algorithm
11.3 Conclusion
References
12. Artificial Intelligence and Machine Learning in Ecoacoustics
Umashankar K.S., Babitha and Rekha M.B.
12.1 Introduction
12.2 Characteristics of Sound in Nature
12.3 Ecoacoustics Parameters
12.4 AI & ML in Ecoacoustics
12.5 ML Approaches
12.5.1 Supervised Approach
12.5.2 Unsupervised Approach
12.6 Case Studies
12.6.1 Artificial Neural Networks (ANN)
12.6.2 Decision Trees
12.6.3 Support Vector Machines (SVM)
12.6.4 Random Forest
12.6.5 Fuzzy Classifiers
12.6.6 K Nearest Neighbors (KNN)
12.6.7 Convolutional Neural Networks (CNN)
12.6.8 Recurrent Neural Networks (RNNs)
12.6.9 Long Short-Term Memory (LSTM)
12.6.10 Gradient Boosting Methods
12.6.11 Clustering Algorithms
12.6.12 Comparison of Some of the Available Algorithms
12.7 Conclusion
References
13. Wingbeat Sounds for Eco-Friendly Pest Control Using Smart Acoustic Traps
Navaneeth Bhaskar, Priyanka Tupe Waghmare, Ashritha K. P., Sanjana Shenoy and Shridevi Bhat
13.1 Introduction
13.1.1 Early Pest Detection and Smarter Control
13.1.2 Current Pest Monitoring Systems
13.1.3 Need for an Automated and Intelligent System
13.1.4 Importance of Wingbeat Sound
13.1.5 Role of AI and Machine Learning in Pest Detection
13.2 Wingbeat Sound Characteristics of Insects
13.2.1 Understanding Insect Wingbeat Patterns
13.2.2 Applying Wingbeat Sounds in Pest Attraction
13.2.3 Recording Wingbeat Frequencies
13.2.4 Integrating Sound with AI-Based Monitoring
13.3 Design of Smart Acoustic Pest Trap
13.3.1 Purpose and Design Principles
13.3.2 Structural Layout and Components
13.3.3 Camera and Image Processing System
13.3.4 Energy Source and Smart Power Control
13.3.5 Wireless Communication and Alerts
13.4 AI Techniques for Pest Detection and Classification
13.4.1 Preprocessing of Image Data
13.4.2 Model Selection and Training Process
13.4.3 Dataset Preparation and Augmentation
13.4.4 Real-Time Inference and Edge Deployment
13.5 Results and Performance Evaluation
13.5.1 Model Performance Comparison
13.5.2 Inference and Hardware Deployment Performance
13.5.3 Additional Evaluation Metrics
13.6 Conclusions
References
14. Acoustic Intelligence from Honey Bee Sounds for Smart
Farming Applications
Navaneeth Bhaskar, Chetan Nimba Aher, Tanisha Sanjaykumar Londhe and Vinayak Bairagi
14.1 Introduction
14.1.1 Importance of Pollinators in Agriculture
14.1.2 Honey Bees in Crop Yield and Pollination
14.1.3 Challenges Faced by Farmers
14.1.4 Potential of Acoustic Monitoring in Beehive Intelligence
14.2 Honey Bee Sound: Origin and Characteristics
14.2.1 Anatomy of Sound Production in Honey Bees
14.2.2 Types of Hive Sounds
14.2.3 Acoustic Patterns during Bee Activities
14.3 Acoustic Signal Acquisition and Processing
14.3.1 Hive Monitoring Setup
14.3.2 Preprocessing of Hive Audio
14.3.3 Feature Extraction Techniques
14.3.4 Sound Pattern Clustering and Classification
14.3.5 Real-Time Data Collection and Edge Processing
14.4 AI-Based Sound Analysis and App Integration
14.4.1 Deep Learning Models for Bee Sound Classification
14.4.2 Transfer Learning and Lightweight AI Deployment
14.4.3 Real-Time Hive Health Interpretation
14.4.4 Mobile App Interface and User Experience
14.4.5 Integration with IoT and Cloud Services
14.5 Results and Validation
14.5.1 Dataset Description and Ground Truth Labeling
14.5.2 Model Training and Evaluation Strategy
14.5.3 Inference Time and Real-Time Capability
14.5.4 Confusion Matrix and Audio Visualization
14.5.5 Field Testing and Live Deployment
14.5.6 Farmer Feedback and Usability Evaluation
14.6 Conclusions
References
15. Acoustic Data for Marine Ecotourism: Enhancing Customer Experience and Conservation through Sound
E. Kamatchi Muthulakshmi, Dhanush K., Dhilipkumar R., Divya K., Barani R. and Alphonsa S.
15.1 Introduction
15.1.1 Objective
15.2 Integrating Acoustic Sensing in Marine Ecotourism Experiences
15.3 Enhancing Customer Experience through Sound
15.4 Marketing Acoustic Ecotourism
15.5 Case Studies
15.6 Conservation and Monitoring Impacts
15.7 Technological Frameworks and Tools
15.8 Ethical and Regulatory Considerations
15.9 Policy Recommendations
15.10 Conclusion
References
16. Harnessing Sound for Scalable Biodiversity Monitoring and Conversion: The Future of Ecoacoustic Intelligence
Karthika Pichaimuthu
16.1 Introduction
16.1.1 Overview of Ecoacoustics and Its Evolution
16.1.2 The Growing Importance of Acoustic Ecology in Conversion
16.2 Technological Foundations for the Future
16.2.1 Passive Acoustic Monitoring (PAM): Progress and Promise
16.2.2 Advances in Digital Records, Cloud Platforms, and Acoustic Sensors
16.2.3 Role of AI and Machine Learning in Ecoacoustic Intelligence
16.3 Key Analytical Components in Ecoacoustics
16.3.1 Pre-Processing and Noise Reduction in Large-Scale Acoustic Data
16.3.2 Visualization and Sonogram Analysis Techniques
16.3.3 Data Annotation, Sound Event Detection, and Classification Methods
16.4 Challenges in Scaling Ecoacoustic Monitoring
16.4.1 Data Volume, Heterogeneity, and Standardization Issues
16.4.2 Balancing Automation with Ecological Accuracy
16.4.3 Limitations in Taxonomic Resolution and Ground-Truthing
16.5 The Sonosphere-Soundscapes as Ecological Indicators
16.5.1 Conceptualizing the Sonosphere
16.5.2 Acoustic Signals as Behavioral and Ecological Proxies
16.5.3 Implications for Biodiversity and Habitat Health Assessment
16.6 Integrating Ecoacoustics into Conservation Practice
16.6.1 Real-World Applications and Case Studies
16.6.2 Informing Policy and Impact Assessments through Sound 3
16.6.3 Opportunities for Citizen Science and Community-Based Monitoring
16.7 Future Directions and Innovations
16.7.1 Emerging Tools: Real-Time Monitoring, Autonomous Systems, and Edge Al
16.7.2 Ecoacoustic Indices and Predictive Ecosystem Modeling
16.7.3 The Potential of Cross-Disciplinary Partnerships
16.8 Conclusion: Toward an Ecoacoustic Future
16.8.1 Summary of Potential and Ongoing Challenges
16.8.2 A Call for Deeper Integration of Sound in Ecological Thinking
16.8.3 Vision for the Future of Ecoacoustic Intelligence
16.8.4 Final Outlook
References
Part IV: Human, Health, and Cultural Dimensions
17. Interpreting Acoustics of Indian Classical Raag Music Using Machine Learning
Shreya Sudhir Aigalikar, Anuradha C. Phadke and Jyoti Lele
17.1 Introduction
17.2 Literature Review
17.3 Methodology
17.3.1 Audio Input from User
17.3.2 Pre-Processing
17.3.3 CNN Architecture
17.3.4 Testing
17.4 Result and Conclusion
17.5 Limitations & Future Scope
17.6 Acknowledgment
References
18. Human Health Risk Assessment via Acoustic Sensing of Traffic Noise
Anitha R., Felciya Suson S.M., Divyadharshini V., Dinesh Kumaran M.R., Dhasarathan S. and Gnanasanjay G.
18.1 Introduction
18.1.1 Background
18.1.2 Objectives
18.2 Fundamentals of Acoustic Sensing
18.2.1 Data Acquisition and Processing
18.3 Traffic Noise and Human Health
18.3.1 Sources and Characteristics of Traffic Noise
18.3.2 Impacts of Traffic Noise on Health
18.4 Risk Assessment Framework
18.4.1 Noise Metrics
18.4.2 Exposure-Assessment
18.4.3 Relationships between Dose and Response
18.5 Technologies and Innovations
18.5.1 IoT Integration into Smart Cities
18.5.2 AI and Machine Learning
18.5.3 Edge Computing
18.6 Case Studies
18.6.1 Urban Centers in Europe
18.6.2 Metro Cities in India
18.6.3 U. S Transportation Corridors
18.7 Public Policy and Mitigation Strategies
18.7.1 Regulation Standards
18.7.2 Urban Architecture
18.8 Policy Implications and Recommendations
18.9 Conclusion
References
19. Nature’s Playlist: Integrating Eco-Acoustic Therapy into Corporate Wellness Programs
J. Nirubarani, Mehaasre R., Nahul H., Abirami P., Aakash S. and Aravind M.
19.1 Introduction
19.1.1 Background
19.1.2 Objectives
19.2 Theoretical Framework
19.2.1 Hypothesis of Biophilia
19.2.2 Attention Restoration Theory (ART)
19.2.3 Stress Recovery Theory (SRT)
19.3 Eco-Acoustic Therapy: Mechanisms and Modalities
19.3.1 Categories of Natural Soundscapes
19.3.2 Modalities of Delivery
19.3.3 Combining with Other Practices
19.4 Designing the Eco-Acoustic Corporate Wellness Program (EACWP)
19.4.1 Components of the Program
19.4.2 Sample Plan of Weekly Schedule
19.5 Case Studies and Evidence-Based Outcomes
19.5.1 Case Study 1: Bangalore, India Based IT Company
19.5.2 Case Study 2: The Culture of Remote Work within Canada
19.5.3 Meta-Analysis Evidence
19.6 Health Outcome Metrics
19.6.1 Physiological Outcomes
19.6.2 Psychological Results
19.7 Implementation Strategy
19.7.1 Organization Preparation
19.7.2 Infrastructure and Investment
19.7.3 Engagement and Communication
19.7.4 Integration of Policies
19.8 Challenges and Ethical Considerations
19.8.1 Accessibility
19.8.2 Relevance of Culture
19.8.3 Confidentiality and Autonomy
19.9 Future Directions
19.9.1 AI-Individualized Sound Landscapes
19.9.2 Eco-Acoustic Workstations
19.9.3 Health Cross-Sectional Studies
19.10 Conclusion
References
20. Green Acoustics in Corporate Sustainability Reporting (CSR): From Monitoring to Disclosure
Kanimozhi T., Lingeshwari V., Logadheepan S., Kousalya N.,
Aravinthan K. and Arun Y.
20.1 Introduction
20.2 Theoretical Background: Eco-Acoustics and Sustainability Science
20.3 Corporate Sustainability and ESG Reporting Landscape
20.4 Eco-Acoustic Monitoring: Methods and Technologies
20.5 Integrating Eco-Acoustics into CSR and ESG Frameworks
20.6 Case Studies of Eco-Acoustic Applications in Corporate Reporting
20.7 Regulatory and Voluntary Disclosure Standards
20.8 Challenges and Limitations
20.9 Strategic Implications, Suggestions and Future Directions
20.10 Conclusion
References
21. Bioacoustics Intelligence: Machine Learning for Wildlife Monitoring
Nandini S. B., Mamatha A. and Veena G. S.
21.1 Introduction
21.1.1 What Role Does Bioacoustics Play in Wildlife Monitoring
21.2 Theoretical Foundations and Historical Context
21.2.1 Source-Filter Theory of Sound Production
21.2.2 Acoustic Niche Hypothesis
21.3 How it is Seen without Upsetting the Fauna
21.3.1 Non-Invasive Monitoring Setup
21.3.2 Performance
21.4 Crucial Parts of a Bioacoustics Intelligence System
21.5 Intelligent Applications of Bioacoustics in the Real World
21.5.1 BirdNET was Created
21.5.2 Rainforest Connection
21.5.3 Xeno-Canto
21.5.4 Elephant Listening Project (ELP)
21.5.5 Marine Case to Demonstrate Ecosystem Diversity
21.5.6 Deep Squeak
21.5.7 Tools
21.6 Future Direction
References
Part V: Cross-Functional Perspectives and Emerging Directions
22. Ecoacoustic Intelligence as a Cross-Functional Asset:
Bridging Science, Policy, and Management
Manoj Govindaraj, G.M. Shaju, Parvez Khan, Jenifer Lawrence and Tilahun Haile Filatie
22.1 Introduction
Background of the Study
Objectives
Methodology
Integrating Ecoacoustic Data into Environmental Policy and Conservation Planning
Challenges in Scaling Ecoacoustic Monitoring: Technological, Ethical, and Institutional Dimensions
Enhancing Ecoacoustic Intelligence through Machine Learning and Data-Driven Soundscape Analysis
Optimizing Ecoacoustic Tool Utilization through Interdisciplinary Collaboration
Discussion
Main Findings
Suggestions
Conclusion
Future Scope
References
Index

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Eco-Acoustic Intelligence