03480ntm a2200373 i 4500 vtls000105278 KUKTEM 20251117113404.0 180924t20182018my a f a m 001 0 eng d THE0005201(Local) 201905141448 hanafiah 201809241128 fateeha UMP eng UMP rda FKEE .N879 2018 r Thesis Siti Nurzalikha Zaini Husni Zaini, author. Swiftlet sound identification using vector quantization and gaussian mixture model / Siti Nurzalikha Zaini Husni Zaini Kuantan, Pahang : UMP, 2018 © 2018 xii, 79 pages : illustrations (some color) ; 30 cm. + 1 CD-ROM text rdacontent text rdacontent unmediated rdamedia computer rdamedia volume rdacarrier computer disc rdacarrier text file PDF rda Faculty of Electrical and Electronics Engineering Thesis (Master of Engineering (Electronics)) -- Universiti Malaysia Pahang – 2018 Includes bibliographical references Bird sound identification has become one of the applications in audio recognition technology. Audio recognition is a great way to classify swiftlet‟s sound between baby, adult, and colony. In real life, biologists are having difficulties to identify the difference between these three types of sound except for human expert hearing experience in swiftlet farming. The identification of swiftlet sound is used to increase the production nest and quality of habitat because the main characteristic of swiftlet is its attraction toward sound. The aim of this study is to implement in swiftlet sound specifically using audio recognition to identify the types of sound. In this work, swiftlet sound feature extracted using Linear Predictive Cepstral Coefficient (LPCC), and Mel Frequency Cepstral Coefficient (MFCC) then classify the sounds using Minimum Distance Classifier (MDC), Vector Quantization (VQ) and Gaussian Mixture Model (GMM). Firstly, the features extracted using LPCC and MFCC are stored in the database. Secondly, feature extraction results in the database used for classifying the swiftlets sound using MDC, VQ with codebook size is 8, 16, 32 and 64 and GMM by 1-mixture and 2-mixture for classification. Thirdly, the best performance classification selected for an additional feature in feature extraction such as Delta and Delta-Acceleration qualifier to improve accuracy for getting a better result. Based on the result of this study, the best performance was selected based on higher accuracy identification is MFCC with GMM by 2-mixture accuracy 88.89%. At the end of the experiment, the MFCC with additional features Delta-Acceleration using classification GMM by 2-mixture with improvement 6.67% compared to original and make it up to 95.56% accuracy which is considered as good percentage result. As conclusion, the best feature extraction for swiftlet sound identification is MFCC with Delta-Acceleration features by classify the sound using GMM 2-mixture. Faculty of Electrical and Electronics Engineering Dissertations Universities and colleges Disertations Theses 0 0 lcc 0 1 20000 20000 2019-09-04 0 FKEE .N879 2018 r Thesis 0000124978 2019-09-04 00:00:00 1 2019-09-04 THESIS 0 0 lcc 0 1 20000 20000 2019-09-04 0 CD 11577 | FKEE .N879 2018 r Thesis 0000124979 2019-09-04 00:00:00 1 2019-09-04 THESIS VIRTUA40 7823 7829 VTLSSORT0080*0200*0400*0900*1000*2450*2640*2641*3000*3360*3361*3370*3371*3380*3381*3470*5000*5020*5040*5200*6100*6500*6501*9992