• Rituraj Ingale
• Prasad Kirkade
• Sairaj Mote
• V. B. Baviskar

In this paper, we are going to create an online tool that will automatically censor explicit content from an audio file (or specified words) using Natural Language Processing and Audio Processing. The implementation of the system relies on two components: First, audio is recorded using any audio recording device like microphone, smartphone or any recording device that can produce output in the system specified audio format, second, audio processing is done on cloud computing platform for speed and ease of use. In the initial phase, we are going to use Google Speech To Text API for converting audio to time-stamped trans-scripture that then can be used to identify and replace explicit words from the audio file. When the processing is done on the cloud the output file will then can be downloaded by the user. The challenges that we will be solving are, creating a deep learning model that identifies explicit words without converting them to text and creating a PWA that can run across all major form factors.

Natural Language Processing, Google Speech To Text, Machine learning, Cloud Computing, Audio Processing.

"Machine Learning-Based Automatic Audio Censoring System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.214-217, June 2019, Available :http://www.jetir.org/papers/JETIR1908775.pdf

"Machine Learning-Based Automatic Audio Censoring System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp214-217, June 2019, Available at : http://www.jetir.org/papers/JETIR1908775.pdf