The proposed method and technology

By -




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, Vain reference to Deity, Ethnic and racial slurs, cursing and strong profanity along with an option to let the user define the word to be identified. If any such objectionable audio content is found, the system will automatically replace that specific word with a predefined polite word phrase or rather a predefined sound according to the user preference. Then the program will list out the identified offensive word along with its corresponding location in the audio signal. Thereafter, the system will provide some audio formats to save the final output. Finally, the user can preview the output.





Here we are using Digital signal processing and artificial intelligence as the main technologies. Digital signal processing techniques and methods are using for the digital signal understanding, noise reduction methods, audio file reading & writing and speech recognition. AI system helps us to recognize the speech and find the objectionable contents. We are using deep learning methods to find different type of objectionable content in different types of audios and different types of voices. Without the AI system it's impossible to find the objectionable content in different audios in different voices and different styles. W believe that our well trained AI model will find any kind of objectionable content in any type of audio.  

In future, we will be working on our implementation and we will update further details.

Comments

Post a Comment

Popular posts from this blog

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