Domain adaptation and classification on bird noises in the SINGA:PURA urban polyphonic dataset

Domain adaptation and classification on bird noises in the SINGA:PURA urban polyphonic dataset

This paper makes use of the SINGA:PURA Urban Polyphonic Dataset to study the effectiveness of different methods of audio data classification in relation to the domain sensitivity of classifier performance. Audio files were classified according to the label taxonomy in the SiNGA;PURA dataset. The ap...

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Main Author: Lam, Bryan Theng Wei
Other Authors: Gan Woon Seng
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Engineering
Classification
Online Access:https://hdl.handle.net/10356/176440
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1764402024-05-17T15:44:40Z Domain adaptation and classification on bird noises in the SINGA:PURA urban polyphonic dataset Lam, Bryan Theng Wei Gan Woon Seng School of Electrical and Electronic Engineering EWSGAN@ntu.edu.sg Engineering Classification This paper makes use of the SINGA:PURA Urban Polyphonic Dataset to study the effectiveness of different methods of audio data classification in relation to the domain sensitivity of classifier performance. Audio files were classified according to the label taxonomy in the SiNGA;PURA dataset. The approach taken compares the performance of a logistic regression classifier to that of a Convolutional Neural Network (CNN) classifier, as well as to a Domain-Adversarial Neural Network (DANN) model on classification tasks in situations where domain data is available and vice versa. Some other factors affecting classification performance are also discussed. Bachelor's degree 2024-05-16T13:24:10Z 2024-05-16T13:24:10Z 2024 Final Year Project (FYP) Lam, B. T. W. (2024). Domain adaptation and classification on bird noises in the SINGA:PURA urban polyphonic dataset. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176440 https://hdl.handle.net/10356/176440 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
Classification
spellingShingle Engineering
Classification
Lam, Bryan Theng Wei
Domain adaptation and classification on bird noises in the SINGA:PURA urban polyphonic dataset
description This paper makes use of the SINGA:PURA Urban Polyphonic Dataset to study the effectiveness of different methods of audio data classification in relation to the domain sensitivity of classifier performance. Audio files were classified according to the label taxonomy in the SiNGA;PURA dataset. The approach taken compares the performance of a logistic regression classifier to that of a Convolutional Neural Network (CNN) classifier, as well as to a Domain-Adversarial Neural Network (DANN) model on classification tasks in situations where domain data is available and vice versa. Some other factors affecting classification performance are also discussed.
author2 Gan Woon Seng
author_facet Gan Woon Seng
Lam, Bryan Theng Wei
format Final Year Project
author Lam, Bryan Theng Wei
author_sort Lam, Bryan Theng Wei
title Domain adaptation and classification on bird noises in the SINGA:PURA urban polyphonic dataset
title_short Domain adaptation and classification on bird noises in the SINGA:PURA urban polyphonic dataset
title_full Domain adaptation and classification on bird noises in the SINGA:PURA urban polyphonic dataset
title_fullStr Domain adaptation and classification on bird noises in the SINGA:PURA urban polyphonic dataset
title_full_unstemmed Domain adaptation and classification on bird noises in the SINGA:PURA urban polyphonic dataset
title_sort domain adaptation and classification on bird noises in the singa:pura urban polyphonic dataset
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/176440
_version_ 1800916307755925504

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