A Feasible Approach for Automatic Detection and Recognition of the Bengalese Finch Songnotes and Their Sequences

Khan Md. Mahfuzus Salam, Tetsuro Nishino, Kazutoshi Sasahara, Miki Takahasi, Kazuo Okanoya

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DOI: 10.4236/jilsa.2010.24025                

Abstract

The Bengalese finch song has been widely studied for its unique features and similarity to human language. For com-putational analysis the songs must be represented in songnote sequences. An automated approach for this purpose is highly desired since manual processing makes human annotation cumbersome, and human annotation is very heu-ristic and easily lacks objectivity. In this paper, we propose a new approach for automatic detection and recognition of the songnote sequences via image processing. The proposed method is based on human recognition process to visually identify the patterns in a sonogram image. The songnotes of the Bengalese finch are dependent on the birds and similar pattern does not exist in two different birds. Considering this constraint, our experiments on real birdsong data of different Bengalese finch show high accuracy rates for automatic detection and recognition of the songnotes. These results indicate that the proposed approach is feasible and generalized for any Bengalese finch songs.

Keywords

Birdsong Analysis, Bengalese Finch Song, Songnote Detection and Recognition, Pattern Recognition

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	E. Honda and K. Okanoya, “Acoustical and Syntactical Com-parisons between Songs of the White-Backed Munia (Lonchura striata) and Its Domesticated Strain, the Bengalese Finch (Lonchura Striata Var. Domestica),” Zoological Science, Vol. 16, 1999, pp. 319-326.
[2]	K. Okanoya, “Song Syntax in Bengalese Finches: Proximate and Ultimate Analyses,” Ad-vances in the Study of Behavior, Vol. 34, 2004, pp. 297-346.
[3]	Y. Kakishita, K. Sasahara, T. Nishino, M. Ta-kahasi and K. Okanoya, “Ethological Data Mining: an Auto-mata-Based Approach to Extract Behavioral Units and Rules,” Data Mining and Knowledge Discovery, Vol. 18, No. 3, 2009, pp. 446-471.
[4]	C. K. Catchpole and P. J. B. Slater, “Bird Song: Biological Themes and Variations,” 2nd Edition, Cam-bridge University Press, UK, 2003.
[5]	J. Nishikawa and K. Okanoya, Ornithological Science, 2006, pp. 95-103.
[6]	J. Doupe and P. K. Kuhl, “Birdsong and Human Speech: Common Themes and Mechanisms,” Annual Reviews Neuroscience, Vol. 22, 1999, pp. 567-631.
[7]	J. C. Russ, “The Image Processing Handbook,” 5th Edition, CRC Press, 2006.
[8]	S. M. Ross, “Introduction to Probability and Statistics for Engineers and Scientists,” 3rd Edition, Elsevier Academic Press, USA, 2004.
[9]	J. Shawe-Taylor and N. Cristianini, “An In-troduction to Support Vector Machines and Other Kernel-Based Learning Methods,” 1st Edition, Cambridge University Press, UK, 2000.
[10]	T. Ojala, M. Pietikainen and D. Harwood, “A Comparative Study of Texture Measures with Classification Based on Feature Distributions,” Pattern Recognition, Vol. 29, No. 1, 1996, pp. 51-59.
[11]	ImageJ 1.41, National Institutes of Health, USA, 2010. http://rsbweb.nih.gov/ij/