Swiftlet sound identification using vector quantization and gaussian mixture model

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...

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Main Author:	Siti Nurzalikha Zaini, Husni Zaini
Format:	Thesis
Language:	English
Published:	2018
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/24593/1/Swiftlet%20sound%20identification%20using%20vector%20quantization%20and%20gaussian%20mixture%20model.pdf
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id	my-ump-ir.24593
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spelling	my-ump-ir.245932021-11-10T01:08:44Z Swiftlet sound identification using vector quantization and gaussian mixture model 2018-08 Siti Nurzalikha Zaini, Husni Zaini TK Electrical engineering. Electronics Nuclear engineering 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. 2018-08 Thesis http://umpir.ump.edu.my/id/eprint/24593/ http://umpir.ump.edu.my/id/eprint/24593/1/Swiftlet%20sound%20identification%20using%20vector%20quantization%20and%20gaussian%20mixture%20model.pdf pdf en public masters Universiti Malaysia Pahang Faculty of Electrical and Electronics Engineering
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
collection	UMPSA Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Siti Nurzalikha Zaini, Husni Zaini Swiftlet sound identification using vector quantization and gaussian mixture model
description	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.
format	Thesis
qualification_level	Master's degree
author	Siti Nurzalikha Zaini, Husni Zaini
author_facet	Siti Nurzalikha Zaini, Husni Zaini
author_sort	Siti Nurzalikha Zaini, Husni Zaini
title	Swiftlet sound identification using vector quantization and gaussian mixture model
title_short	Swiftlet sound identification using vector quantization and gaussian mixture model
title_full	Swiftlet sound identification using vector quantization and gaussian mixture model
title_fullStr	Swiftlet sound identification using vector quantization and gaussian mixture model
title_full_unstemmed	Swiftlet sound identification using vector quantization and gaussian mixture model
title_sort	swiftlet sound identification using vector quantization and gaussian mixture model
granting_institution	Universiti Malaysia Pahang
granting_department	Faculty of Electrical and Electronics Engineering
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/24593/1/Swiftlet%20sound%20identification%20using%20vector%20quantization%20and%20gaussian%20mixture%20model.pdf
_version_	1783732073566044160

Swiftlet sound identification using vector quantization and gaussian mixture model

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