An Architecture for Animal Sound Identification based on Multiple Feature Extraction and Classification Algorithms

• Leandro Tacioli UNICAMP
• Luíz Toledo UNICAMP
• Claudia Medeiros UNICAMP

DOI: https://doi.org/10.5753/bresci.2017.9919

Resumo

Automatic identification of animals is extremely useful for scientists, providing ways to monitor species and changes in ecological communities. The choice of effective audio features and classification techniques is a challenge on any audio recognition system, especially in bioacoustics that commonly uses several algorithms. This paper presents a novel software architecture that supports multiple feature extraction and classification algorithms to help on the identification of animal species from their recorded sounds. This architecture was implemented by the WASIS software, freely available on the Web.

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