Music Genre Classification with AST Models: A Path to Emotional Resilience in Bengaluru

The great rise and demand for digital music being consumed in today’s world has created an urgent need for efficient genre classification in order to improve music recommendation to satisfy users. The research explores a music classification model leveraging the Audio Spectrogram Transformer (AST) and mel-spectrogram based feature extraction for efficient and accurate genre classification. The model is designed to address issues such as depression, addictive habits, life’s challenges and societal expectations among the youth in Bengaluru by providing personalized music therapy to enhance emotional resilience of the individual.