Proceedings

A Hidden Markov Model-Based Acoustic Cicada Detector for Crowdsourced Smartphone Biodiversity Monitoring

Davide Zilli, Oliver Parson, Geoff V. Merrett, Alex Rogers

Download BibTeX Cite

Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 2945-2950.

Abstract

Automated acoustic recognition of species aims to provide a cost-effective method for biodiversity monitoring. This is particularly appealing for detecting endangered animals with a distinctive call, such as the New Forest cicada. To this end, we pursue a crowdsourcing approach, whereby the millions of visitors to the New Forest will help to monitor the presence of this cicada by means of a smartphone app that can detect its mating call. However, current systems for acoustic insect classification are aimed at batch processing and not suited to a real-time approach as required by this system, because they are too computationally expensive and not robust to environmental noise. To address this shortcoming we propose a novel insect detection algorithm based on a hidden Markov model to which we feed as a single feature vector the ratio of two key frequencies extracted through the Goertzel algorithm. Our results show that this novel approach, compared to the state of the art for batch insect classification, is much more robust to noise while also reducing the computational cost.

Davide Zilli, Oliver Parson, Geoff V. Merrett, and Alex Rogers. 2013. A Hidden Markov Model-Based Acoustic Cicada Detector for Crowdsourced Smartphone Biodiversity Monitoring. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13. IJCAI, 2945–2950. https://ijcai.org/proceedings/2013/432
Welcome Future Conferences AI Journal Division IJCAI Proceedings About IJCAI IJCAI Awards