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http://dx.doi.org/10.17661/jkiiect.2017.10.1.85

Sound Model Generation using Most Frequent Model Search for Recognizing Animal Vocalization  

Ko, Youjung (Department of Computer Engineering, Hanbat National University)
Kim, Yoonjoong (Department of Computer Engineering, Hanbat National University)
Publication Information
The Journal of Korea Institute of Information, Electronics, and Communication Technology / v.10, no.1, 2017 , pp. 85-94 More about this Journal
Abstract
In this paper, I proposed a sound model generation and a most frequent model search algorithm for recognizing animal vocalization. The sound model generation algorithm generates a optimal set of models through repeating processes such as the training process, the Viterbi Search process, and the most frequent model search process while adjusting HMM(Hidden Markov Model) structure to improve global recognition rate. The most frequent model search algorithm searches the list of models produced by Viterbi Search Algorithm for the most frequent model and makes it be the final decision of recognition process. It is implemented using MFCC(Mel Frequency Cepstral Coefficient) for the sound feature, HMM for the model, and C# programming language. To evaluate the algorithm, a set of animal sounds for 27 species were prepared and the experiment showed that the sound model generation algorithm generates 27 HMM models with 97.29 percent of recognition rate.
Keywords
Animal Vocalization Recognition; MFCC; Most Frequent Model Search Algorithm; HMM; Sound Model Generation;
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