Acoustic Bird Activity Detection on Real-Field Data
- 1.4k Downloads
We report on a research effort aiming at the development of an acoustic bird activity detector (ABAD), which plays an important role for automating traditional biodiversity assessment studies – presently performed by human experts. The proposed on-line ABAD is considered an integral part of an automated system for acoustic identification of bird species, which is currently under development. In particular, taking advantage of real-field audio recordings collected in the Hymettus Mountains east of Athens, we investigate the applicability of various machine learning techniques for the needs of our ABAD, which is intended to run on a mobile device. Performance is reported in terms of recognition accuracy on audio-frame level, due to the restrictions imposed by the requirement of run-time decision making with limited memory and energy resources. We report recognition accuracy of approximately 86% on a frame level, which is quite promising and encourages further research efforts in that direction.
Keywordsacoustic bird activity detection bioacoustics biodiversity surveys real-field data
Unable to display preview. Download preview PDF.
- 2.Loizou, P.: Speech Enhancement: Theory and Practice. CRC Press (2007)Google Scholar
- 3.Jančovič, P., Köküer, M.: Automatic detection and recognition of tonal bird sounds in noisy environments. EURASIP Journal on Advances in Signal Processing 2011, Article ID 982936, 10 (2011), doi:10.1155/2011/982936Google Scholar
- 4.Eyben, F., Wöllmer, M., Schuller, B.: OpenEAR - introducing the Munich open-source emotion and affect recognition toolkit. In: Proc. of the 4th International HUMAINE Association Conference on Affective Computing and Intelligent Interaction, ACII 2009 (2009)Google Scholar
- 5.Young, S., Evermann, G., Gales, M., Hain, T., Kershaw, D., Liu, X., Moore, G., Odell, J., Ollason, D., Povey, D., Valtchev, V., Woodland, P.: The HTK book (for HTK Version 3.4), Cambridge University Engineering Department (2006)Google Scholar
- 6.Aha, D., Kibler, D.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)Google Scholar
- 7.Bouckaert, R.R.: Bayesian networks in Weka. Technical Report 14/2004. Computer Science Department. University of Waikato (2004)Google Scholar
- 8.Chester, D.L.: Why two hidden layers are better than one. In: Proc. of the International Joint Conference on Neural Networks, vol. 1, pp. 265–268 (1990)Google Scholar
- 9.Quinlan, R.: C4.5: Programs for machine learning. Morgan Kaufmann Publishers, San Mateo (1993)Google Scholar
- 11.Scholkopf, B., Smola, A.J.: Learning with Kernels. MIT Press (2002)Google Scholar
- 12.Witten, H.I., Frank, E.: Data Mining: practical machine learning tools and techniques. Morgan Kaufmann Publishing (2005)Google Scholar