MARC details
| 000 -LEADER |
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| fixed length control field |
03480ntm a2200373 i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
vtls000105278 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
KUKTEM |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251117113404.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
180924t20182018my a f a m 001 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0005201(Local) |
| 039 #9 - LEVEL OF BIBLIOGRAPHIC CONTROL AND CODING DETAIL [OBSOLETE] |
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| Level of rules in bibliographic description |
201905141448 |
| Level of effort used to assign nonsubject heading access points |
hanafiah |
| -- |
201809241128 |
| -- |
fateeha |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMP |
| Language of cataloging |
eng |
| Transcribing agency |
UMP |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FKEE .N879 2018 r Thesis |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Siti Nurzalikha Zaini Husni Zaini, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
Swiftlet sound identification using vector quantization and gaussian mixture model / |
| Statement of responsibility, etc. |
Siti Nurzalikha Zaini Husni Zaini |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMP, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2018 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2018 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xii, 79 pages : |
| Other physical details |
illustrations (some color) ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD-ROM |
| 336 ## - CONTENT TYPE |
|---|
| Content type term |
text |
| Source |
rdacontent |
| 336 ## - CONTENT TYPE |
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| Content type term |
text |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
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| Media type term |
unmediated |
| Source |
rdamedia |
| 337 ## - MEDIA TYPE |
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| Media type term |
computer |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
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| Carrier type term |
volume |
| Source |
rdacarrier |
| 338 ## - CARRIER TYPE |
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| Carrier type term |
computer disc |
| Source |
rdacarrier |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| File type |
text file |
| Encoding format |
PDF |
| Source |
rda |
| 500 ## - GENERAL NOTE |
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| General note |
Faculty of Electrical and Electronics Engineering |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (Master of Engineering (Electronics)) -- Universiti Malaysia Pahang – 2018 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
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. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
Faculty of Electrical and Electronics Engineering |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Disertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Theses |