Abstract
Estimating koala populations is challenging, making surveys costly and often imprecise. This study examines the feasibility of recognising koala calls from audio recordings using a bio-inspired acoustic approach. In the work, we propose to use the Cascade of Asymmetric Resonators with Fast-Acting Compression (CAR-FAC) cochlea model to pre-process koala recordings and a support vector machine (SVM) for classification. The frequency-dependent gain and bandwidth properties of the CAR-FAC model in pre-processing noisy data are discussed and investigated. To evaluate the effectiveness of the proposed method, it is compared with an FFT-based system as a baseline method. The baseline methodology uses a Mel-spectrogram pre-processing and the same linear classifier as the proposed method as the back-end. In summary, we achieved 96.7% accuracy for the proposed system.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
