Investigation of Unsupervised Models for Biodiversity Assessment
Significant animal species loss has been observed in recent decades due to habitat destruction, which puts at risk environmental integrity and biodiversity. Traditional ways of assessing biodiversity are limited in terms of both time and space, and have high cost. Since the presence of animals can be indicated by sound, recently acoustic recordings have been used to estimate species richness. Bioacoustic sounds are typically recorded in habitats for several weeks, so contain a large collection of different sounds. Birds are of particular interest due to their distinctive calls and because they are useful ecological indicators. To assess biodiversity, the task of manually determining how many different types of birds are present in such a lengthy audio is really cumbersome. Towards providing an automated support to this issue, in this paper we investigate and propose a clustering based approach to assist in automated assessment of biodiversity. Our approach first estimates the number of different species and their volumes which are used for deriving a biodiversity index. Experimental results with real data indicates that our proposed approach estimates the biodiversity index value close to the ground truth.
KeywordsBiodiversity Unsupervised model Bioacoustics
This research was supported by an Australian Government Research Training Program (RTP) Scholarship. We sincerely thank the Samford Ecological Research Facility (SERF), Queensland University of Technology, Australia for providing us the labelled dataset.
- 1.Bibby, C.J., Burgess, N.D., Hill, D.A., Mustoe, S.: Bird Census Techniques. Elsevier, Amsterdam (2000)Google Scholar
- 6.Riede, K.: Monitoring biodiversity: analysis of Amazonian rainforest sounds. Ambio 546–548 (1993)Google Scholar
- 9.BioDiversityGroup: Biodiversity Website (2017). http://www.bioacoustics.myspecies.info/en. Accessed May 2017
- 10.Cai, J., Ee, D., Pham, B., Roe, P., Zhang, J.: Sensor network for the monitoring of ecosystem: bird species recognition. In: 3rd International Conference on Intelligent Sensors, Sensor Networks and Information, ISSNIP 2007, pp. 293–298. IEEE (2007)Google Scholar
- 12.Hanson, B., Applebaum, T.: Robust speaker-independent word recognition using static, dynamic and acceleration features: experiments with Lombard and noisy speech. In: 1990 International Conference on Acoustics, Speech, and Signal Processing, ICASSP-90. IEEE (1990)Google Scholar
- 13.Akaike, H.: Information theory and an extension of the maximum likelihood principle. In: Proceedings of the 2nd International Symposium on Information Theory, Tsahkadsor, Armenia, 28 September 1971Google Scholar
- 16.Thakur, A., Rajan, P.: Model-based unsupervised segmentation of birdcalls from field recordings. In: 2016 10th International Conference on Signal Processing and Communication Systems (ICSPCS), pp. 1–6. IEEE, December 2016Google Scholar
- 17.Agranat, I.: Bat species identification from zero crossing and full spectrum echolocation calls using hidden Markov models, fisher scores, unsupervised clustering and balanced winnow pairwise classifiers. In: Proceedings of Meetings on Acoustics ICA2013, vol. 19, no. 1, p. 010016. ASA, June 2013Google Scholar
- 18.Salamon, J., Bello, J.P.: Unsupervised feature learning for urban sound classification. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 171–175. IEEE, April 2015Google Scholar
- 19.Somervuo, P., Härmä, A.: Analyzing bird song syllables on the self-organizing map. In: Workshop on Self-Organizing Maps (WSOM 2003), September 2003Google Scholar