Feature extraction for poultry vocalization recognition based on improved MFCC

Li Zhizhong; Teng Guanghui

Volume 24 Issue 11

Nov. 2008

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Abstract

Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE) > 2008 > 24(11): 202-205.

Li Zhizhong, Teng Guanghui. Feature extraction for poultry vocalization recognition based on improved MFCC[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2008, 24(11): 202-205.

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Feature extraction for poultry vocalization recognition based on improved MFCC

Key Laboratory of Agricultural Bioenvironmental Engineering, Ministry of Agriculture, College of Water Conservancy and Civil Engineering, China Agricultural University, Beijing 100083, China

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Received Date: March 10, 2008
Revised Date: April 14, 2008
Published Date: November 29, 2008

Graphical Abstract

Abstract

Abstract

Animal’s sound has rich information. Animal’s health status and adaption for environment can be fed back from vocalism. Vocalization has been the important way for measuring animal welfares. For the auto recording and analysis of animal sound, some ways were studied for sound feature extraction. On the basis of Liner Prediction Coefficient (LPC) and Mel-Frequency-Cepstral Coefficient (MFCC), a new algorithm for feature extraction－Average MFCC was introduced. For evaluating the improvement of poultry sound recognition, SVM classification model was used to perform the experiments, the results showed that performance was obviously improved.
- feature extraction,
- hens vocalization,
- vocalism recognition,
- AMFCC

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