Acoustic-Based Applications for Vertebrate Vocalization (SpringerBriefs in Applied Sciences and Technology)

✍ Scribed by Ramashini Murugaiya, Manisha Milani Mahagammulle Gamage, Krishani Murugiah, Madhumathy Perumal

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 69
• Edition: 1st ed. 2022
• Category: Library

✦ Synopsis

​Acoustic-Based Applications for Vertebrate Vocalization is designed to help researchers improve their findings and knowledge of vertebrate vocalization by focusing on the integration of acoustic features with new technologies, such as the Internet of Things (IoT), cloud computing, and virtual and cognitive reality. The book addresses the most common challenges in vertebrate vocalization-based research via suitable audio signal processing techniques, data collection, data pre-processing, acoustic feature engineering, extraction, and selection for multidisciplinary applications, i.e. feature classification, vertebrate communication, behavioral analysis, and signal pattern analysis. The book is an important reference for scholars, researchers, industry practitioners, teachers, and students across a number of disciplines, including bioengineering, audio engineering, systems engineering, biotechnology, signal processing, biology, zoology, and animal sciences.

✦ Table of Contents

Preface
Contents
Chapter 1: Introduction to Applications on Vertebrate Vocalisation
1.1 Acoustics of Ecosystem
1.2 Significance of Vocalisation
1.2.1 Vocalisation: An Innate Existence of Vertebrates
1.3 Vocal Mechanism in Different Vertebrates
1.4 Primary Factors in Vertebrate Vocalisation-Based Applications
1.5 Acoustic-Based Applications of Vertebrate
1.5.1 Amphibians
1.5.2 Fishes
1.5.3 Reptiles
1.5.4 Birds
1.5.5 Mammalians
1.6 Summary
References
Chapter 2: Significance of Acoustic Features in Vertebrate Vocalisations Related Applications
2.1 Introduction to Acoustic Features
2.2 Related Works on Acoustic Features
2.3 Homomorphic Signal Processing
2.4 Cepstral Features for Acoustics Analysis
2.4.1 Linear Prediction Cepstrum Coefficients (LPCC)
2.4.2 Perceptual Linear Prediction (PLP) Cepstral Coefficients
2.4.3 Mel Frequency Cepstral Coefficients (MFCC)
2.4.4 Greenwood Function Cepstral Coefficients (GFCC)
2.4.5 Gammatone Cepstral Coefficients (GTCC)
2.4.6 Power Normalised Cepstral Coefficient (PNCC)
2.4.7 Relative Spectral PLP (RASTA-PLP) Feature
2.5 Acoustic Feature Engineering and Feature Selection
2.6 Acoustic Feature Optimisation of Vertebrate Vocalisation Applications
2.6.1 Vocal Signal Input
2.6.2 Noise Reduction
2.6.3 Feature Extraction
2.6.4 Vocalisation Applications
2.7 Feature Optimisation Results and Discussions
2.7.1 Cepstral Feature Analysis
2.7.2 Time-Domain and Cepstral Feature Analysis
2.7.3 Frequency-Domain and Cepstral Feature Analysis
2.8 Summary
References
Chapter 3: Trending Technologies in Vertebrate Vocalisation Applications
3.1 Introduction
3.2 Significant of Technology Integration for Vertebrate Vocalisation Related Applications
3.3 Citizen Science Projects for Vertebrate Vocalisation
3.4 Potential Future Directions on Development of Vertebrate Vocalisation Applications
3.4.1 Artificial Intelligence (AI)
3.4.2 Internet of Things (IoT)
3.4.3 Augmented Reality (AR)
3.4.4 Virtual Reality (VR)
3.4.5 Cognitive Technologies
3.4.6 Robotic Process Automation (RPA)
3.5 Conclusion Final Remarks
References
Index