Automated detection of profanities for film censorship using deep learning

Given the excessive profanities identified in audio and video files and the detrimental consequences to an individual‟s character and behaviour, content censorship is crucial to filter profanities from young viewers with higher exposure to uncensored content. Although manual detection and censorship...

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Bibliographic Details
Main Author:	Ba Wazir, Abdulaziz Saleh
Format:	Thesis
Published:	2022
Subjects:	Q300-390 Cybernetics
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id	my-mmu-ep.11874
record_format	uketd_dc
spelling	my-mmu-ep.118742023-11-28T04:51:34Z Automated detection of profanities for film censorship using deep learning 2022-06 Ba Wazir, Abdulaziz Saleh Q300-390 Cybernetics Given the excessive profanities identified in audio and video files and the detrimental consequences to an individual‟s character and behaviour, content censorship is crucial to filter profanities from young viewers with higher exposure to uncensored content. Although manual detection and censorship were implemented, the methods proved tedious. Inevitably, misidentifications involving profane language owing to human weariness and the low performance in human visual and hearing systems concerning long screening time occurred. Another challenge in this task is the lack of well-structured profane language dataset for research and application purposes. The thesis proposes profanities classification and detection models utilizing deep neural network and a novel profanity language dataset. This research proposed an intelligent model for profane words censorship through automated and robust detection by deep learning models. A dataset of profanities was collected and processed for the computation of audio spectral feature that serve as an input to evaluate the classification of spoken profane and normal words. The proposed classification models were first tested for 2-class (Profanity vs Normal) classification problem, the profanity class is then further decomposed into multi-class classification problem for exact detection of profanity. Experimental results show the viability of proposed system by demonstrating high performance of profane words classification with high speed. The proposed systems outperformed state-of-the-art pre-trained and baseline neural networks on the novel profanities dataset and proved to reduce the computational cost with minimal trainable parameters. Hence, proposed system was proven to be fast on screening and detection of audible profane content for films‟ censorship, as the time taken to process an input sample is only about 46% of the input sample‟s duration. 2022-06 Thesis http://shdl.mmu.edu.my/11874/ http://erep.mmu.edu.my/ phd doctoral Multimedia University Faculty of Engineering (FOE) EREP ID: 11584
institution	Multimedia University
collection	MMU Institutional Repository
topic	Q300-390 Cybernetics
spellingShingle	Q300-390 Cybernetics Ba Wazir, Abdulaziz Saleh Automated detection of profanities for film censorship using deep learning
description	Given the excessive profanities identified in audio and video files and the detrimental consequences to an individual‟s character and behaviour, content censorship is crucial to filter profanities from young viewers with higher exposure to uncensored content. Although manual detection and censorship were implemented, the methods proved tedious. Inevitably, misidentifications involving profane language owing to human weariness and the low performance in human visual and hearing systems concerning long screening time occurred. Another challenge in this task is the lack of well-structured profane language dataset for research and application purposes. The thesis proposes profanities classification and detection models utilizing deep neural network and a novel profanity language dataset. This research proposed an intelligent model for profane words censorship through automated and robust detection by deep learning models. A dataset of profanities was collected and processed for the computation of audio spectral feature that serve as an input to evaluate the classification of spoken profane and normal words. The proposed classification models were first tested for 2-class (Profanity vs Normal) classification problem, the profanity class is then further decomposed into multi-class classification problem for exact detection of profanity. Experimental results show the viability of proposed system by demonstrating high performance of profane words classification with high speed. The proposed systems outperformed state-of-the-art pre-trained and baseline neural networks on the novel profanities dataset and proved to reduce the computational cost with minimal trainable parameters. Hence, proposed system was proven to be fast on screening and detection of audible profane content for films‟ censorship, as the time taken to process an input sample is only about 46% of the input sample‟s duration.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Ba Wazir, Abdulaziz Saleh
author_facet	Ba Wazir, Abdulaziz Saleh
author_sort	Ba Wazir, Abdulaziz Saleh
title	Automated detection of profanities for film censorship using deep learning
title_short	Automated detection of profanities for film censorship using deep learning
title_full	Automated detection of profanities for film censorship using deep learning
title_fullStr	Automated detection of profanities for film censorship using deep learning
title_full_unstemmed	Automated detection of profanities for film censorship using deep learning
title_sort	automated detection of profanities for film censorship using deep learning
granting_institution	Multimedia University
granting_department	Faculty of Engineering (FOE)
publishDate	2022
_version_	1794019129087229952

Automated detection of profanities for film censorship using deep learning

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