Detection of Explicit Lyrics in Hindi Music Using Different Machine Learning Algorithms

Abstract

Detecting explicit lyrics in Hindi music is a crucial responsibility to prevent the public from hearing offensive and improper material. In recent years, different machine learning methods have been used to find obscene lyrics in Hindi music. K-Nearest Neighbours (KNN), Convolutional Neural Networks (CNN), Naive Bayes, and Long Short-Term Memory (LSTM) are four techniques for recognising explicit lyrics in Hindi music that are compared in the present research. Hindi song lyrics that have been classified as explicit or not make up the dataset used in this study. Python is used to implement each method, and multiple performance metrics including accuracy and F1-score are used for evaluations. The findings demonstrate that, with an accuracy of 81.6% and an F1-score of 0.96, the LSTM model surpasses every other method. To see how the CNN and LSTM models learn, the training and validation graphs for each model are shown. The graphs show that the LSTM model performs better at detecting explicit lyrics in Hindi music than other machine learning models, with greater accuracy and lower loss. Overall, the study underlines the benefits of employing LSTM over alternative approaches and shows how well machine learning methodologies work for identifying explicit lyrics in Hindi music.