@article {47889,
	title = {Automatic recognition of animal vocalizations using averaged MFCC and linear discriminant analysis},
	journal = {Pattern Recognition Letters},
	volume = {27},
	year = {2006},
	month = {Jan-01-2006},
	pages = {93 - 101},
	abstract = {<p>In this paper we propose a method that uses the averaged Mel-frequency cepstral coefficients (MFCCs) and linear discriminant anal- ysis (LDA) to automatically identify animals from their sounds. First, each syllable corresponding to a piece of vocalization is segmented. The averaged MFCCs over all frames in a syllable are calculated as the vocalization features. Linear discriminant analysis (LDA), which finds out a transformation matrix that minimizes the within-class distance and maximizes the between-class distance, is utilized to increase the classification accuracy while to reduce the dimensionality of the feature vectors. In our experiment, the average classification accuracy is 96.8\% and 98.1\% for 30 kinds of frog calls and 19 kinds of cricket calls, respectively.</p>
},
	keywords = {linear discriminant analysis, Mel-frequency cepstral coefficients},
	issn = {01678655},
	doi = {10.1016/j.patrec.2005.07.004},
	url = {http://linkinghub.elsevier.com/retrieve/pii/S0167865505001959},
	author = {Lee, Chang-Hsing and Chou, Chih-Hsun and Han, Chin-Chuan and Huang, Ren-Zhuang}
}
