Separating different sounds in a recording is called sound separation. In this project, I focused on just two uses: music and movies. When it comes to music, sound separation means pulling out voices and instruments from a song. For example, let's break down the parts of two songs that I found randomly. Thanks to the creators.
Automatic handwritten character recognition (HCR) and optical character recognition (OCR) are quite popular for commercial and academic reasons. For alpha-syllabary languages this problem increases manifolds due to its non-linear structure. Bengali, a member of alpha-syllabary family, is way trickier than English as it has 50 letters - 11 vowels and 39 consonants - plus 18 diacritics. This means there are roughly 13,000 ways to write Bengali letters, whereas English only has about 250 ways to do the same. This huge number of combinations makes recognizing Bengali characters a lot harder. These different elements has been shown below for a visual understanding.
A primary focus lies in developing machine learning models capable of detecting toxicity within online discussions. Toxicity, in this context, refers to anything perceived as rude, disrespectful, or potentially causing someone to exit a conversation. Typically, toxicity is categorized using binary classification, but this approach limits the ability to discern the severity of toxic comments. In my project for a Kaggle competition, I present a system aimed at addressing this limitation.