One shot learning for acoustics classification of Malaysia bird species

Malaysia is famed for its beautiful bio-diverse forest and its bird species, some of it is still understudied. Using acoustic detection, we can study these bird as current advanced in machine learning application have resulted in cutting edge performance for acoustic classification application. Howe...

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Main Author:	Koay, Xian Hong
Format:	Thesis
Language:	English
Published:	2022
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/99476/1/KoayXianHongMSKE2022.pdf
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id	my-utm-ep.99476
record_format	uketd_dc
spelling	my-utm-ep.994762023-02-27T07:30:35Z One shot learning for acoustics classification of Malaysia bird species 2022 Koay, Xian Hong TK Electrical engineering. Electronics Nuclear engineering Malaysia is famed for its beautiful bio-diverse forest and its bird species, some of it is still understudied. Using acoustic detection, we can study these bird as current advanced in machine learning application have resulted in cutting edge performance for acoustic classification application. However, most of these applications need large amount of data for prediction to have acceptable accuracy. We, however, do not have this kind of resources. This situation is experienced by large demographic of this country, which provide importance to our studies. Adding to that problem, one common issue that come with studying of species is that we can only know the status of species in a habitat up to certain point. Such problem needs to be solve using methodologies that can cope with the fluidity of information. As such, we propose neural network framework that able to notice any changes in the class categories and learn new classes on the fly. To solve our issue, we seek to design a Siamese Style convolutional Neural Network for one-shot-learning architecture. Additionally, we would train it using base convolutional neural network with low complexity so that it can be realistically implement in hardware of low computing power. We evaluated and benchmarked our framework, showing promising results as the Network is able to classify trained bird species with accuracy of 90% or higher with only around 100 sound clips per bird species. Additionally, it is able to detect new bird species on the fly and add it to its class successfully, however, it still needs some work as the accuracy is around 50% on this part. All of these is achieved using base Convolutional Neural Networks of low complexity with only 4 layers of conv layers and 2 layers of fully connected layers. From this thesis, It is shown that this neural network can work and I am optimistic that this work can be further improved, which can be done by using a higher variety of dataset and transfer learning and a further tweaking of the base neural network architecture. 2022 Thesis http://eprints.utm.my/id/eprint/99476/ http://eprints.utm.my/id/eprint/99476/1/KoayXianHongMSKE2022.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:149924 masters Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering Faculty of Engineering - School of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Koay, Xian Hong One shot learning for acoustics classification of Malaysia bird species
description	Malaysia is famed for its beautiful bio-diverse forest and its bird species, some of it is still understudied. Using acoustic detection, we can study these bird as current advanced in machine learning application have resulted in cutting edge performance for acoustic classification application. However, most of these applications need large amount of data for prediction to have acceptable accuracy. We, however, do not have this kind of resources. This situation is experienced by large demographic of this country, which provide importance to our studies. Adding to that problem, one common issue that come with studying of species is that we can only know the status of species in a habitat up to certain point. Such problem needs to be solve using methodologies that can cope with the fluidity of information. As such, we propose neural network framework that able to notice any changes in the class categories and learn new classes on the fly. To solve our issue, we seek to design a Siamese Style convolutional Neural Network for one-shot-learning architecture. Additionally, we would train it using base convolutional neural network with low complexity so that it can be realistically implement in hardware of low computing power. We evaluated and benchmarked our framework, showing promising results as the Network is able to classify trained bird species with accuracy of 90% or higher with only around 100 sound clips per bird species. Additionally, it is able to detect new bird species on the fly and add it to its class successfully, however, it still needs some work as the accuracy is around 50% on this part. All of these is achieved using base Convolutional Neural Networks of low complexity with only 4 layers of conv layers and 2 layers of fully connected layers. From this thesis, It is shown that this neural network can work and I am optimistic that this work can be further improved, which can be done by using a higher variety of dataset and transfer learning and a further tweaking of the base neural network architecture.
format	Thesis
qualification_level	Master's degree
author	Koay, Xian Hong
author_facet	Koay, Xian Hong
author_sort	Koay, Xian Hong
title	One shot learning for acoustics classification of Malaysia bird species
title_short	One shot learning for acoustics classification of Malaysia bird species
title_full	One shot learning for acoustics classification of Malaysia bird species
title_fullStr	One shot learning for acoustics classification of Malaysia bird species
title_full_unstemmed	One shot learning for acoustics classification of Malaysia bird species
title_sort	one shot learning for acoustics classification of malaysia bird species
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2022
url	http://eprints.utm.my/id/eprint/99476/1/KoayXianHongMSKE2022.pdf
_version_	1776100600779374592

One shot learning for acoustics classification of Malaysia bird species

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