A system for Automatic Audio Replacement of Objectionable Content for Sri Lankan Locale!

By -
In today's world, audiovisual media plays a significant role in impacting the society at large. The media content a person is exposed to naturally affects the way he thinks and behaves. Hence, it is important to pay attention towards the content of media. In the present, majority of the audiovisual  media content which people watch or listen to, undeniably features some form of objectionable content, such as hate speech, crude language, ethnic and racial slurs, which none of us, should be exposed to.

Identifying this issue, several parties all around the world have made several recorded attempts to resolve this problem through various implementations with the use of various technologies. Yet, the above mentioned issue remains as an issue unaddressed for us here in Sri Lanka, since all those parties have focused their concern mainly on English Language.


Sri Lanka is a country, which is well known for its rich cultural and traditional values. Over 15 million people, which can be considered as more than two thirds of its local population, are using Sinhala and Tamil languages. Therefore, profanities in  Sinhala and Tamil media, have become a significant issue.

Certain movies, songs and other audiovisual programs contain some sort of  inappropriate content for kids. Media featuring objectionable content reach millions of people very smoothly via social media. As a result corruption of youth and degradation of culture easily takes place. Even though, there are several censorship standards in the entertainment industry to solve this issue, there is no such defined automatic system in Sri Lankan locale which will assist the editors in removing censored phrases or words, and replacing them.


Focusing on this issue in Sri Lanka, we, Team Shabdha, propose a  software based solution with the use of digital signal processing and Artificial Intelligence, for Automatic Audio Replacement of Objectionable Content for Sri Lankan Locale. This system will create a space for the users to selectively filter out the potentially objectionable audio content in an audio input, and then seamlessly replace them with predetermined audio inputs.  We believe that this research based project, which can be further adopted for real time purposes and all media types, will certainly be a significant technological invention that paves the path in preserving the Sri Lankan culture. 



Comments

Post a Comment

Popular posts from this blog

The proposed method and technology

By -
Image
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...
Read more

System Evaluation

By -
Image
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 ...
Read more

Conclusion | Automatic Audio Replacement of Objectionable Content for Sri Lankan Locale

By -
Image
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 ...
Read more