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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.

has been cited by the following article:

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

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

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

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.2 No.4, December 14, 2010

  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.