Recognition of bird species based on spike model using bird dataset

Area of Science:

• Ornithology
• Bioacoustics
• Artificial Intelligence

Background:

• Bird species recognition is crucial for monitoring biodiversity and detecting environmental changes.
• Existing methods for bird sound recognition are often region-specific.
• Birds serve as sensitive indicators of climate change and ecosystem health.

Purpose of the Study:

• To develop and present a spike-based bird species recognition model.
• To identify bird species accurately based on their vocalizations.
• To contribute to early warning systems for environmental health monitoring.

Main Methods:

• A dataset of 14 bird species vocalizations was collected in their natural habitat.
• Recordings were made using a digital recorder and unidirectional microphone at CPDO, Bhubaneswar, India.
• A novel spike-based model was employed for bird call analysis and species identification.

Main Results:

• The study successfully demonstrates a spike-based approach for bird species recognition.
• The model processes bird vocalizations to identify specific species.
• The research provides a foundation for automatic bird species recognition systems.

Conclusions:

• The developed spike-based model offers a promising method for accurate bird species recognition.
• This technology can enhance biodiversity monitoring and climate change detection.
• The system has potential applications in ecological research and conservation efforts.