Feature extraction based on bandpass filtering for frog call classification

Xie, Jie, Towsey, Michael, Zhang, Liang, Zhang, Jinglan, & Roe, Paul (2016) Feature extraction based on bandpass filtering for frog call classification. In Mansouri, Alamin, Nouboud, Fathallah, Chalifour, Alain, Mammass, Driss, Meunier, Jean, & Elmoataz, Abderrahim (Eds.) Image and Signal Processing: 7th International Conference, ICISP 2016, Trois-Rivières, QC, Canada, May 30 - June 1, 2016, Proceedings, Springer International Publishing, Québec, Canada, pp. 231-239.

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Abstract

In this paper, we propose an adaptive frequency scale filter bank to perform frog call classification. After preprocessing, the acoustic signal is segmented into individual syllables from which spectral peak track is extracted. Then, syllable features including track duration, dominant frequency, and oscillation rate are calculated. Next, a k-means clustering technique is applied to the dominant frequency of syllables for all frog species, whose centroids are used to construct a frequency scale. Furthermore, one novel feature named bandpass filter bank cepstral coefficients is extracted by applying a bandpass filter bank to the spectral of each syllable, where the filter bank is designed based on the generated frequency scale. Finally, a k-nearest neighbour classifier is adopted to classify frog calls based on extracted features. The experiment results show that our proposed feature can achieve an average classification accuracy of 94.3 % which outperforms Mel-frequency cepstral coefficients features (81.4 %) and syllable features (88.1 %).

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ID Code: 93805
Item Type: Conference Paper
Refereed: Yes
Additional Information: Volume 9680 of the series Lecture Notes in Computer Science
Additional URLs:
Keywords: Frog call classification, Spectral peak track, k-means clustering, Filter bank, k-nearest neighbour
DOI: 10.1007/978-3-319-33618-3_24
ISBN: 9783319336176
ISSN: 0302-9743
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2016 Springer International Publishing Switzerland
Copyright Statement: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-33618-3_24
Deposited On: 19 Jun 2016 23:13
Last Modified: 10 Jul 2017 14:00

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