000 03480ntm a2200373 i 4500
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008 180924t20182018my a f a m 001 0 eng d
020 _aTHE0005201(Local)
039 9 _a201905141448
_bhanafiah
_y201809241128
_zfateeha
040 _aUMP
_beng
_cUMP
_erda
090 _aFKEE .N879 2018 r Thesis
100 0 _aSiti Nurzalikha Zaini Husni Zaini,
_eauthor.
245 1 0 _aSwiftlet sound identification using vector quantization and gaussian mixture model /
_cSiti Nurzalikha Zaini Husni Zaini
264 1 _aKuantan, Pahang :
_bUMP,
_c2018
264 4 _c© 2018
300 _axii, 79 pages :
_billustrations (some color) ;
_c30 cm. +
_e1 CD-ROM
336 _atext
_2rdacontent
336 _atext
_2rdacontent
337 _aunmediated
_2rdamedia
337 _acomputer
_2rdamedia
338 _avolume
_2rdacarrier
338 _acomputer disc
_2rdacarrier
347 _atext file
_bPDF
_2rda
500 _aFaculty of Electrical and Electronics Engineering
502 _aThesis (Master of Engineering (Electronics)) -- Universiti Malaysia Pahang – 2018
504 _aIncludes bibliographical references
520 3 _aBird 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.
610 2 0 _aFaculty of Electrical and Electronics Engineering
_xDissertations
650 0 _aUniversities and colleges
_xDisertations
650 0 _aTheses
999 _aVIRTUA40
_c7823
_d7829
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