Swiftlet sound identification using vector quantization and minimum distance classifier

Siti Nurzalikha Zaini, Husni Zaini and M. Z., Ibrahim (2016) Swiftlet sound identification using vector quantization and minimum distance classifier. In: National Conference for Postgraduate Research , 24-25 September 2016 , Pekan, Pahang. pp. 1-5.. (Unpublished)

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Abstract

There are high demand on swiftlet nest as it benefits in health, cosmetic and food industry. Therefore, the study about technologies and method to increase their production in swiftlet farming using sound technology is needed. In the real situation, the classification of swiftlet sound is evaluated by human expert using try and error method at swiftlet house. However, this required high level of human skill and prone to mistake. In this work, we present an automatic swiftlet sound identification using vector quantization and minimum distance classifier. Firstly, swiftlet sound extracted using mel-frequency cepstral coefficient. Secondly, vector quantization with codebook size is 8,16,32 and 64 and minimum distance classifier was used for the sound classification. Finally, performance of the system was measured by in three type swiftlets, baby, adults and colony type. It shows that, the highest identification was ?? when using what and what linear predictive cepstral coefficient features change to mel frequency cepstral coefficient additional deltaacceleration features.

Item Type:	Conference or Workshop Item (Speech)
Uncontrolled Keywords:	Swiflet; Mel frequency; Linear predictive; Vector quantization; Minimum distance classsifier
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division:	Faculty of Electrical & Electronic Engineering
Depositing User:	Mrs. Neng Sury Sulaiman
Date Deposited:	20 Jul 2018 07:57
Last Modified:	20 Jul 2018 07:57
URI:	http://umpir.ump.edu.my/id/eprint/18733
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