Eco-Acoustic Intelligence
Editat de Canute Sherwin, Chandra Singh, Navaneeth Bhaskar, Shwetha N.en Limba Engleză Hardback – 12 oct 2026
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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Specificații
Notă biografică
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, more than 20 research articles, and holds 11 patents. His research interests include optical networking and communication, wireless communication, intelligent systems, IoT, and robotics.
Navaneeth Bhaskar, 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 numerous technical talks, published extensively in 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 earned her doctorate from Visveswaraya Technological University. 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 more than 14 peer-reviewed research articles and two book chapters. His research focuses on metal coatings, electrochemistry, grain refinement, alloy modification, sustainable technologies, and mechatronic systems.
Navaneeth Bhaskar, 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 numerous technical talks, published extensively in 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 earned her doctorate from Visveswaraya Technological University. 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 more than 14 peer-reviewed research articles and two book chapters. His research focuses on metal coatings, electrochemistry, grain refinement, alloy modification, sustainable technologies, and mechatronic systems.
Cuprins
Preface xxi
Part I: Foundations of Eco-Acoustic Intelligence 1
1 Sonic Biometrics and Pattern Recognition in Eco-Acoustic Intelligence 3
Roopesh Ramesh, Priyanka S. and Kalyan N.
1.1 Introduction 4
1.2 Mathematical Foundations of Sonic Biometrics 10
1.3 Acoustic Sensing and Signal Processing Pipeline 15
1.4 Pattern Recognition Algorithms in Eco-Acoustic Tasks 17
1.5 End-to-End Biometric System Architecture 23
1.6 Case Studies and Applications 29
1.7 Evaluation Metrics and Benchmarking in Eco-Acoustic Pattern Recognition 32
1.8 Datasets, Tools, and Resources in Eco-Acoustic Pattern Recognition 34
1.9 Future Directions 37
1.10 Conclusion 40
2 Artificial Intelligence and Machine Learning in Eco-Acoustics 45
Sai Venkatramana Prasada G. S. and Rashmi P. C.
2.1 Introduction 46
2.2 Types of Eco-Acoustic Data 47
2.3 AI and ML Overview 48
2.4 ML Techniques in Eco-Acoustics 49
2.5 Applications 50
2.6 Advanced Techniques 53
2.7 Integration with IoT and Edge AI 55
2.8 Challenges and Solutions 57
2.9 Future Directions 60
2.10 Conclusion 62
3 Exploration of Landslide Prediction Using Machine and Deep Learning Techniques 65
Niveditha M., Sowmiya Sri B., Poornashree H., Rakshitha M.D., Sanjeevakumar M. Hatture, Rashmi P. Karchi and Usha Desai
3.1 Introduction 66
3.2 Literature Survey 67
3.3 System Analysis 68
3.4 Methodology 70
3.5 Implementation 72
3.6 Experimentation 76viii Contents
3.7 Conclusion 78
4 AI and ML in Ecoacoustics: A New Era of Environmental Monitoring 81
Jyoti Omprakash Gautam, Parvez Khan and Homen Lahan
4.1 Introduction 82
4.2 The Evolution and Promise of Eco-Acoustics 83
4.3 Tools, Techniques, and Strategies: Capturing Environmental Soundscapes 85
4.4 From Raw Audio to Usable Data: Preprocessing and Standardization 86
4.5 Machine Learning Approaches in Eco-Acoustics 86
4.6 Ecological Applications and Insights from AI-Driven Eco-Acoustics 88
4.7 Acoustic Indices: Simplifying Complex Soundscapes 91
4.8 Current Challenges and Ethical Considerations 92
4.9 Future Directions in Eco-Acoustic Monitoring 93Contents ix
4.10 Conclusion 94
5 AI-Enhanced Eco-Acoustic Systems Integrating IoT for Efficient Ecosystem Monitoring 97
Dankan Gowda V., Srinivas D., K.D.V. Prasad, T. Kavitha and Franklin Jino R. E.
5.1 Introduction 98
5.2 Literature Survey 99
5.3 Fundamentals of Eco-Acoustic Systems 101
5.4 Integration of IoT in Eco-Acoustic Systems 104
5.5 Role of AI in Enhancing Eco-Acoustic Systems 107
5.6 Case Studies and Applications 110
5.7 Results of Discussion 112
5.8 Conclusion 117
6 Bio-Inspired Machine Learning Models for Eco-Acoustic Signal Processing 121
Dankan Gowda V., Galiveeti Poornima, Srinivas D., K.D.V. Prasad and D. Vengaimarbhan
6.1 Introduction 122
6.2 Literature Survey 124
6.3 Machine Learning in Eco-Acoustics 125
6.4 Bio-Inspired Machine Learning Models 126
6.5 Methodology 128
6.6 Results and Discussion 131
6.7 Conclusion 137
Part II: Enabling Technologies and Smart Systems 141
7 Noise Pollution and Its Impact with IoT-Based Monitoring and Machine Learning Solutions 143
Dankan Gowda V., Puja Roshani, K.D.V. Prasad, M. Sathyanarayanan and Manojkumar S. B.
7.1 Introduction 144
7.2 Literature Survey 146
7.3 IoT-Based Noise Monitoring Systems 147x Contents
7.4 Machine Learning Techniques in Noise Analysis 149
7.5 Results Discussion 152
7.6 Conclusion 157
8 Smart Eco-Acoustic Networks Using IoT and Machine Learning for Environmental Sustainability 161
Dankan Gowda V., S.V. Ramanan, K.D.V. Prasad, Sagar Choudhary and K. Sivakumar
8.1 Introduction 162
8.2 Integration of Machine Learning 163
8.3 Background and Motivation 165
8.4 Literature Survey 168
8.5 System Architecture 172
8.6 Applications and Use Cases 176
8.7 Challenges in Implementation 180
8.8 Results and Discussion 183
8.9 Conclusion 189
9 Speech Enhancement through U-Net Architecture for Noise Suppression 193
Saumya Y. M., Vinay P., Abner Stan Fernandez, Alden Crist Rego, B. Ashish Shenoy and Eyan Leroy Sequeira
9.1 Introduction 193
9.2 Literature Survey 194
9.3 Methodology 195
9.4 Results and Discussion 199
9.5 Conclusion 202
10 Hybrid Deep Learning Models for Biodiversity Monitoring in Rainforest Environments Using Eco Acoustic 205
Manjula Gururaj Rao, Ashwini B., Vaikunta Pai, Nagana Chetty, Rashmi P. Shetty, Priyanka H., Deepa Shetty and Chinmai Shetty
10.1 Introduction 206
10.2 Literature Survey 207
10.3 Methodology 212
10.4 Results and Discussion 216
10.5 Conclusion 221
Part III: Applications in Environmental Monitoring 225
11 SmartGuard: AI-Driven Real-Time Detection and Acoustic Alerts for Enhanced Forest Protection 227
K.V.N.D. Sushma, Nithya Madhasu, Praveen Abhi Vamsi Kodali, K. G. Suma and Usha Desai
11.1 Introduction 228
11.2 Methodology 230
11.3 Conclusion 235
12 Artificial Intelligence and Machine Learning in Ecoacoustics 239
Umashankar K.S., Babitha and Rekha M.B.
12.1 Introduction 240
12.2 Characteristics of Sound in Nature 240
12.3 Ecoacoustics Parameters 241
12.4 AI & ML in Ecoacoustics 242
12.5 ML Approaches 242
12.6 Case Studies 245
12.7 Conclusion 258
13 Wingbeat Sounds for Eco-Friendly Pest Control Using Smart Acoustic Traps 263
Navaneeth Bhaskar, Priyanka Tupe Waghmare, Ashritha K. P., Sanjana Shenoy and Shridevi Bhat
13.1 Introduction 264
13.2 Wingbeat Sound Characteristics of Insects 267
13.3 Design of Smart Acoustic Pest Trap 269
13.4 AI Techniques for Pest Detection and Classification 273
13.5 Results and Performance Evaluation 276
13.6 Conclusions 280
14 Acoustic Intelligence from Honey Bee Sounds for Smart Farming Applications 285
Navaneeth Bhaskar, Chetan Nimba Aher, Tanisha Sanjaykumar Londhe and Vinayak Bairagi
14.1 Introduction 286
14.2 Honey Bee Sound: Origin and Characteristics 288
14.3 Acoustic Signal Acquisition and Processing 290
14.4 AI-Based Sound Analysis and App Integration 293
14.5 Results and Validation 295
14.6 Conclusions 299
15 Acoustic Data for Marine Ecotourism: Enhancing Customer Experience and Conservation through Sound 303
E. Kamatchi Muthulakshmi, Dhanush K., Dhilipkumar R., Divya K., Barani R. and Alphonsa S.
15.1 Introduction 303
15.2 Integrating Acoustic Sensing in Marine Ecotourism Experiences 305
15.3 Enhancing Customer Experience through Sound 307
15.4 Marketing Acoustic Ecotourism 310
15.5 Case Studies 313
15.6 Conservation and Monitoring Impacts 315xiv Contents
15.7 Technological Frameworks and Tools 318
15.8 Ethical and Regulatory Considerations 321
15.9 Policy Recommendations 323
15.10 Conclusion 325
16 Harnessing Sound for Scalable Biodiversity Monitoring and Conversion: The Future of Ecoacoustic Intelligence 329
Karthika Pichaimuthu
16.1 Introduction 330
16.2 Technological Foundations for the Future 332
16.3 Key Analytical Components in Ecoacoustics 334
16.4 Challenges in Scaling Ecoacoustic Monitoring 337
16.5 The Sonosphere-Soundscapes as Ecological Indicators 340
16.6 Integrating Ecoacoustics into Conservation Practice 343
16.7 Future Directions and Innovations 346
16.8 Conclusion: Toward an Ecoacoustic Future 352
Part IV: Human, Health, and Cultural Dimensions 361
17 Interpreting Acoustics of Indian Classical Raag Music Using Machine Learning 363
Shreya Sudhir Aigalikar, Anuradha C. Phadke and Jyoti Lele
17.1 Introduction 364
17.2 Literature Review 364
17.3 Methodology 368
17.4 Result and Conclusion 375
17.5 Limitations & Future Scope 378
17.6 Acknowledgment 379
18 Human Health Risk Assessment via Acoustic Sensing of Traffic Noise 381
Anitha R., Felciya Suson S.M., Divyadharshini V., Dinesh Kumaran M.R., Dhasarathan S. and Gnanasanjay G.
18.1 Introduction 381
18.2 Fundamentals of Acoustic Sensing 384
18.3 Traffic Noise and Human Health 387
18.4 Risk Assessment Framework 390
18.5 Technologies and Innovations 392
18.6 Case Studies 395
18.7 Public Policy and Mitigation Strategies 397
18.8 Policy Implications and Recommendations 400
18.9 Conclusion 401
19 Nature's Playlist: Integrating Eco-Acoustic Therapy into Corporate Wellness Programs 405
J. Nirubarani, Mehaasre R., Nahul H., Abirami P., Aakash S. and Aravind M.
19.1 Introduction 405
19.2 Theoretical Framework 408
19.3 Eco-Acoustic Therapy: Mechanisms and Modalities 409
19.4 Designing the Eco-Acoustic Corporate Wellness Program (EACWP) 412Contents xvii
19.5 Case Studies and Evidence-Based Outcomes 415
19.6 Health Outcome Metrics 418
19.7 Implementation Strategy 421
19.8 Challenges and Ethical Considerations 424
19.9 Future Directions 426
19.10 Conclusion 429
20 Green Acoustics in Corporate Sustainability Reporting (CSR): From Monitoring to Disclosure 435
Kanimozhi T., Lingeshwari V., Logadheepan S., Kousalya N., Aravinthan K. and Arun Y.
20.1 Introduction 436
20.2 Theoretical Background: Eco-Acoustics and Sustainability Science 436
20.3 Corporate Sustainability and ESG Reporting Landscape 437
20.4 Eco-Acoustic Monitoring: Methods and Technologies 437
20.5 Integrating Eco-Acoustics into CSR and ESG Frameworks 440
20.6 Case Studies of Eco-Acoustic Applications in Corporate Reporting 442
20.7 Regulatory and Voluntary Disclosure Standards 444
20.8 Challenges and Limitations 446
20.9 Strategic Implications, Suggestions and Future Directions 447xviii Contents
20.10 Conclusion 455
21 Bioacoustics Intelligence: Machine Learning for Wildlife Monitoring 459
Nandini S. B., Mamatha A. and Veena G. S.
21.1 Introduction 460
21.2 Theoretical Foundations and Historical Context 465
21.3 How it is Seen without Upsetting the Fauna 468
21.4 Crucial Parts of a Bioacoustics Intelligence System 473
21.5 Intelligent Applications of Bioacoustics in the Real World 474
21.6 Future Direction 482
Part V: Cross-Functional Perspectives and Emerging Directions 485
22 Ecoacoustic Intelligence as a Cross-Functional Asset: Bridging Science, Policy, and Management 487
Manoj Govindaraj, G.M. Shaju, Parvez Khan, Jenifer Lawrence and Tilahun Haile Filatie
22.1 Introduction 488
References 511
Index 515
Part I: Foundations of Eco-Acoustic Intelligence 1
1 Sonic Biometrics and Pattern Recognition in Eco-Acoustic Intelligence 3
Roopesh Ramesh, Priyanka S. and Kalyan N.
1.1 Introduction 4
1.2 Mathematical Foundations of Sonic Biometrics 10
1.3 Acoustic Sensing and Signal Processing Pipeline 15
1.4 Pattern Recognition Algorithms in Eco-Acoustic Tasks 17
1.5 End-to-End Biometric System Architecture 23
1.6 Case Studies and Applications 29
1.7 Evaluation Metrics and Benchmarking in Eco-Acoustic Pattern Recognition 32
1.8 Datasets, Tools, and Resources in Eco-Acoustic Pattern Recognition 34
1.9 Future Directions 37
1.10 Conclusion 40
2 Artificial Intelligence and Machine Learning in Eco-Acoustics 45
Sai Venkatramana Prasada G. S. and Rashmi P. C.
2.1 Introduction 46
2.2 Types of Eco-Acoustic Data 47
2.3 AI and ML Overview 48
2.4 ML Techniques in Eco-Acoustics 49
2.5 Applications 50
2.6 Advanced Techniques 53
2.7 Integration with IoT and Edge AI 55
2.8 Challenges and Solutions 57
2.9 Future Directions 60
2.10 Conclusion 62
3 Exploration of Landslide Prediction Using Machine and Deep Learning Techniques 65
Niveditha M., Sowmiya Sri B., Poornashree H., Rakshitha M.D., Sanjeevakumar M. Hatture, Rashmi P. Karchi and Usha Desai
3.1 Introduction 66
3.2 Literature Survey 67
3.3 System Analysis 68
3.4 Methodology 70
3.5 Implementation 72
3.6 Experimentation 76viii Contents
3.7 Conclusion 78
4 AI and ML in Ecoacoustics: A New Era of Environmental Monitoring 81
Jyoti Omprakash Gautam, Parvez Khan and Homen Lahan
4.1 Introduction 82
4.2 The Evolution and Promise of Eco-Acoustics 83
4.3 Tools, Techniques, and Strategies: Capturing Environmental Soundscapes 85
4.4 From Raw Audio to Usable Data: Preprocessing and Standardization 86
4.5 Machine Learning Approaches in Eco-Acoustics 86
4.6 Ecological Applications and Insights from AI-Driven Eco-Acoustics 88
4.7 Acoustic Indices: Simplifying Complex Soundscapes 91
4.8 Current Challenges and Ethical Considerations 92
4.9 Future Directions in Eco-Acoustic Monitoring 93Contents ix
4.10 Conclusion 94
5 AI-Enhanced Eco-Acoustic Systems Integrating IoT for Efficient Ecosystem Monitoring 97
Dankan Gowda V., Srinivas D., K.D.V. Prasad, T. Kavitha and Franklin Jino R. E.
5.1 Introduction 98
5.2 Literature Survey 99
5.3 Fundamentals of Eco-Acoustic Systems 101
5.4 Integration of IoT in Eco-Acoustic Systems 104
5.5 Role of AI in Enhancing Eco-Acoustic Systems 107
5.6 Case Studies and Applications 110
5.7 Results of Discussion 112
5.8 Conclusion 117
6 Bio-Inspired Machine Learning Models for Eco-Acoustic Signal Processing 121
Dankan Gowda V., Galiveeti Poornima, Srinivas D., K.D.V. Prasad and D. Vengaimarbhan
6.1 Introduction 122
6.2 Literature Survey 124
6.3 Machine Learning in Eco-Acoustics 125
6.4 Bio-Inspired Machine Learning Models 126
6.5 Methodology 128
6.6 Results and Discussion 131
6.7 Conclusion 137
Part II: Enabling Technologies and Smart Systems 141
7 Noise Pollution and Its Impact with IoT-Based Monitoring and Machine Learning Solutions 143
Dankan Gowda V., Puja Roshani, K.D.V. Prasad, M. Sathyanarayanan and Manojkumar S. B.
7.1 Introduction 144
7.2 Literature Survey 146
7.3 IoT-Based Noise Monitoring Systems 147x Contents
7.4 Machine Learning Techniques in Noise Analysis 149
7.5 Results Discussion 152
7.6 Conclusion 157
8 Smart Eco-Acoustic Networks Using IoT and Machine Learning for Environmental Sustainability 161
Dankan Gowda V., S.V. Ramanan, K.D.V. Prasad, Sagar Choudhary and K. Sivakumar
8.1 Introduction 162
8.2 Integration of Machine Learning 163
8.3 Background and Motivation 165
8.4 Literature Survey 168
8.5 System Architecture 172
8.6 Applications and Use Cases 176
8.7 Challenges in Implementation 180
8.8 Results and Discussion 183
8.9 Conclusion 189
9 Speech Enhancement through U-Net Architecture for Noise Suppression 193
Saumya Y. M., Vinay P., Abner Stan Fernandez, Alden Crist Rego, B. Ashish Shenoy and Eyan Leroy Sequeira
9.1 Introduction 193
9.2 Literature Survey 194
9.3 Methodology 195
9.4 Results and Discussion 199
9.5 Conclusion 202
10 Hybrid Deep Learning Models for Biodiversity Monitoring in Rainforest Environments Using Eco Acoustic 205
Manjula Gururaj Rao, Ashwini B., Vaikunta Pai, Nagana Chetty, Rashmi P. Shetty, Priyanka H., Deepa Shetty and Chinmai Shetty
10.1 Introduction 206
10.2 Literature Survey 207
10.3 Methodology 212
10.4 Results and Discussion 216
10.5 Conclusion 221
Part III: Applications in Environmental Monitoring 225
11 SmartGuard: AI-Driven Real-Time Detection and Acoustic Alerts for Enhanced Forest Protection 227
K.V.N.D. Sushma, Nithya Madhasu, Praveen Abhi Vamsi Kodali, K. G. Suma and Usha Desai
11.1 Introduction 228
11.2 Methodology 230
11.3 Conclusion 235
12 Artificial Intelligence and Machine Learning in Ecoacoustics 239
Umashankar K.S., Babitha and Rekha M.B.
12.1 Introduction 240
12.2 Characteristics of Sound in Nature 240
12.3 Ecoacoustics Parameters 241
12.4 AI & ML in Ecoacoustics 242
12.5 ML Approaches 242
12.6 Case Studies 245
12.7 Conclusion 258
13 Wingbeat Sounds for Eco-Friendly Pest Control Using Smart Acoustic Traps 263
Navaneeth Bhaskar, Priyanka Tupe Waghmare, Ashritha K. P., Sanjana Shenoy and Shridevi Bhat
13.1 Introduction 264
13.2 Wingbeat Sound Characteristics of Insects 267
13.3 Design of Smart Acoustic Pest Trap 269
13.4 AI Techniques for Pest Detection and Classification 273
13.5 Results and Performance Evaluation 276
13.6 Conclusions 280
14 Acoustic Intelligence from Honey Bee Sounds for Smart Farming Applications 285
Navaneeth Bhaskar, Chetan Nimba Aher, Tanisha Sanjaykumar Londhe and Vinayak Bairagi
14.1 Introduction 286
14.2 Honey Bee Sound: Origin and Characteristics 288
14.3 Acoustic Signal Acquisition and Processing 290
14.4 AI-Based Sound Analysis and App Integration 293
14.5 Results and Validation 295
14.6 Conclusions 299
15 Acoustic Data for Marine Ecotourism: Enhancing Customer Experience and Conservation through Sound 303
E. Kamatchi Muthulakshmi, Dhanush K., Dhilipkumar R., Divya K., Barani R. and Alphonsa S.
15.1 Introduction 303
15.2 Integrating Acoustic Sensing in Marine Ecotourism Experiences 305
15.3 Enhancing Customer Experience through Sound 307
15.4 Marketing Acoustic Ecotourism 310
15.5 Case Studies 313
15.6 Conservation and Monitoring Impacts 315xiv Contents
15.7 Technological Frameworks and Tools 318
15.8 Ethical and Regulatory Considerations 321
15.9 Policy Recommendations 323
15.10 Conclusion 325
16 Harnessing Sound for Scalable Biodiversity Monitoring and Conversion: The Future of Ecoacoustic Intelligence 329
Karthika Pichaimuthu
16.1 Introduction 330
16.2 Technological Foundations for the Future 332
16.3 Key Analytical Components in Ecoacoustics 334
16.4 Challenges in Scaling Ecoacoustic Monitoring 337
16.5 The Sonosphere-Soundscapes as Ecological Indicators 340
16.6 Integrating Ecoacoustics into Conservation Practice 343
16.7 Future Directions and Innovations 346
16.8 Conclusion: Toward an Ecoacoustic Future 352
Part IV: Human, Health, and Cultural Dimensions 361
17 Interpreting Acoustics of Indian Classical Raag Music Using Machine Learning 363
Shreya Sudhir Aigalikar, Anuradha C. Phadke and Jyoti Lele
17.1 Introduction 364
17.2 Literature Review 364
17.3 Methodology 368
17.4 Result and Conclusion 375
17.5 Limitations & Future Scope 378
17.6 Acknowledgment 379
18 Human Health Risk Assessment via Acoustic Sensing of Traffic Noise 381
Anitha R., Felciya Suson S.M., Divyadharshini V., Dinesh Kumaran M.R., Dhasarathan S. and Gnanasanjay G.
18.1 Introduction 381
18.2 Fundamentals of Acoustic Sensing 384
18.3 Traffic Noise and Human Health 387
18.4 Risk Assessment Framework 390
18.5 Technologies and Innovations 392
18.6 Case Studies 395
18.7 Public Policy and Mitigation Strategies 397
18.8 Policy Implications and Recommendations 400
18.9 Conclusion 401
19 Nature's Playlist: Integrating Eco-Acoustic Therapy into Corporate Wellness Programs 405
J. Nirubarani, Mehaasre R., Nahul H., Abirami P., Aakash S. and Aravind M.
19.1 Introduction 405
19.2 Theoretical Framework 408
19.3 Eco-Acoustic Therapy: Mechanisms and Modalities 409
19.4 Designing the Eco-Acoustic Corporate Wellness Program (EACWP) 412Contents xvii
19.5 Case Studies and Evidence-Based Outcomes 415
19.6 Health Outcome Metrics 418
19.7 Implementation Strategy 421
19.8 Challenges and Ethical Considerations 424
19.9 Future Directions 426
19.10 Conclusion 429
20 Green Acoustics in Corporate Sustainability Reporting (CSR): From Monitoring to Disclosure 435
Kanimozhi T., Lingeshwari V., Logadheepan S., Kousalya N., Aravinthan K. and Arun Y.
20.1 Introduction 436
20.2 Theoretical Background: Eco-Acoustics and Sustainability Science 436
20.3 Corporate Sustainability and ESG Reporting Landscape 437
20.4 Eco-Acoustic Monitoring: Methods and Technologies 437
20.5 Integrating Eco-Acoustics into CSR and ESG Frameworks 440
20.6 Case Studies of Eco-Acoustic Applications in Corporate Reporting 442
20.7 Regulatory and Voluntary Disclosure Standards 444
20.8 Challenges and Limitations 446
20.9 Strategic Implications, Suggestions and Future Directions 447xviii Contents
20.10 Conclusion 455
21 Bioacoustics Intelligence: Machine Learning for Wildlife Monitoring 459
Nandini S. B., Mamatha A. and Veena G. S.
21.1 Introduction 460
21.2 Theoretical Foundations and Historical Context 465
21.3 How it is Seen without Upsetting the Fauna 468
21.4 Crucial Parts of a Bioacoustics Intelligence System 473
21.5 Intelligent Applications of Bioacoustics in the Real World 474
21.6 Future Direction 482
Part V: Cross-Functional Perspectives and Emerging Directions 485
22 Ecoacoustic Intelligence as a Cross-Functional Asset: Bridging Science, Policy, and Management 487
Manoj Govindaraj, G.M. Shaju, Parvez Khan, Jenifer Lawrence and Tilahun Haile Filatie
22.1 Introduction 488
References 511
Index 515