Background of our project

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We could find a considerable amount of research work with regard to our project idea “A system for Automatic Audio
Replacement of Objectionable Content for Sri Lankan Locale”.Most of the researches were based on English. 
These are the brief summaries about the most related research work-

01) Automatic Replacement of Objectionable Audio Content from Audio Signals, A. Stuart, R. Sahasrabudhe and S. Kendal. (2009, February 26). Patent US 20090055189A1 [Online]. Available:
This research paper is very much similar to our proposed project but it is a hardware solution specifically for the English language.
This will first filter objectionable content from a real-time audio signal, identifies the type of objectionable content and replaces
them with an audio clip corresponding to the replacement setting. 
02)Filtering some Portions of a Multimedia Stream, P. Nair. (2014, May 08). Patent US
2014O129225A1 [Online]. Available:
It is about a client-server system which is a very similar system for our proposed project but based on the English language.
03)Communication Device Language Filter, G.Fein and E. Merritt. (2010, November 04).
Patent US 2010O28O828A1 [Online]. Available:
This system works with cellular communication which gives the ability to delete, replace or modify offensive language that occurs
within a telephone conversation with the use of a pre-defined set of filters.
04) T. Nadungodage and R. Weerasinghe.Continuous Sinhala Speech Recognizer.
Presented at Conf. on Human Language Technology for Development. [Online].
An attempt to convert the Sinhala spoken words into machine-readable input, which means converting an acoustic signal captured
by a microphone or a telephone, to a stream of words similar to google assistant.
05) P. G. N. Priyadarshani, N. G. J. Dias and A. Punchihewa. Dynamic Time Warping
Based Speech Recognition for Isolated Sinhala Words. Presented at Midwest Symp. On
Circuits and Systems. [Online]. Available:
In this, they use feature extraction and feature matching and compare the patterns of the audio signal with the database of reference
patterns and give out the written word.
06) Systems and Methods for Filtering Objectionable Content, V. Vanjani. (2015, July
23). Patent US 20150205574A1 [Online]. Available:
This explains a mechanism where a maker file lists objectionable content is stored in the user device, and when a user plays audio
content for which a marker file exists, the system can automatically filter out the objectionable content marked in the marker file.
07)Modification of Speech Quality in Conversations Over Voice Channels, S. Basson, D.
Kanevsky, D. Nahamoo and T. Sainath. (2012, January 19). Patent US 20120016674A1
[Online]. Available:

This system provides techniques for modifying speech quality in a conversation over a voice channel. In this speaker can selectively
manage such modification.

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The proposed method and technology

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We are proposing a method that can easily find and replace the objectionable content in an audio file using an artificial intelligence model. This method will be useful for the media editors, since they can easily find and replace the objectionable content in the audio file with one click. He can input the audio file and select the objectionable content type. After that our system will automatically find and replace the objectionable content with the predefined words or phrases. The replaced words are intended to match with the voice and the modulation of the original input.  When the program starts to run, the user will have the ability to input an audio file(wav or mp3) to be analyzed. Thereafter the audio clip will be analyzed and filtered out the noise using several filters (eg: Low pass, High Pass, etc.). Following that, the user can choose what specific objectionable audio content should be identified and replaced. The program will provide four options namely...
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System Evaluation

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After the models were finalized, the hyperparameters of the classifiers and vectorizers were tuned for optimum accuracy using the pipelining method in Scikit-learn. The classification reports of final chosen models for Sinhala, Tamil and English are depicted in TABLE XV. The performance of the finalized binary classification models was evaluated using the Receiver Operating Characteristic (ROC) Curve in Fig 3, Fig 4. and Fig 5, which display the graphical relationships among the metrics shown in TABLE XIV. With the use of ‘Area Under the Curve’ measure, from Fig 1, Fig 2. and Fig 3, it can be observed that the performance of the classifiers is greater than the classifiers with no power. Fig. 1. Receiver Operating Characteristic Curve of the model for Sinhala. Fig. 2. Receiver Operating Characteristic Curve of the model for Tamil.                               Fig. 3.  Receiver Operating Characteristic Curve ...
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Conclusion | Automatic Audio Replacement of Objectionable Content for Sri Lankan Locale

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When considering Sri Lanka, the government had to temporarily ban a few social media platforms several times from 2018 to 2019 to control the violence between the communities which was accelerating via hate speech and racist fake news spreading through social media. Yet, there is no proper mechanism to specifically filter and replace objectionable content in audio for Sri Lankan locale. In addressing this issue, we proposed a system for Sri Lankan locale, which automatically detects and replaces objectionable content in audio. In this proposed system, to selectively filter out the potentially objectionable audio content, the input audio is first preprocessed and converted into text format. The presence of racist, cursing, and sexist objectionable content are detected along with their corresponding locations and timestamps through the filtering mechanism. Afterwards, the detected objectionable content is seamlessly replaced with predetermined audio input. The model was tested against a ...
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