Progress of the Digital Signal Processing (DSP) part of the Project.

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Our Project, Automatic Audio Replacement of Objectionable Content for Sri Lankan Locale, is mainly based on Digital Signal Processing (DSP), Natural Language Processing (NLP), and Speech Recognition. Among them, the audio pre-processing phase, which mainly focuses on the Digital Signal Processing is the area I worked on, and this article briefs the progress of that part in our project.

The DSP portion of our implemented system mainly has two focuses. 
1. Noise Reduction
2. Amplification




Audio inputs of the users might be of varying qualities and they might be recorded under different environmental conditions. In order to proceed ahead with our system, and to detect objectionable content efficiently and accurately, having cleaned audios are important. Hence, when a user inputs an audio into the system, it will first go through the noise reduction process.

We have tried various filters for our audio samples, and finally, we came into a conclusion that Low Pass Filter and the High Pass Filter are the two main filters that can be used for our project. We experimented this noise reduction process using various Python libraries and through the literature review and the experiments done, we decided to use the thinkdsp python library which seemed to be efficient and user-friendly than the other options we tried out.



After the audios are cleaned, we found out that some of those outputs were not in an audible range due to the effect of noise reduction. To overcome this problem we tried several mechanisms, and as a result, we used the amplification process to get the audios back into an audible range, after they are cleaned. For the amplification process numpy, scipy and matplotlib python libraries were used.

By now, this process satisfies the noise reduction requirement of our system and provides an output that can be used for further processes of our system.


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