Design of Chatbot (MusicBot) for Music Recommendation System Using Deep Learning

In today's digital age, music has become an integral part of our lives, offering solace, inspiration, and connection. However, the vast ocean of music choices often leaves individuals overwhelmed when searching for the perfect track to match their emotional state. This paper presents a multi-modal approach to music recommendation, integrating chat-based, voice-based, and facial recognition-based interaction method. The “Music Mood” aims to revolutionize the music listening experience by developing an innovative music recommendation system that relies on advanced machine learning models instead of traditional natural language processing techniques. This hybrid model will analyze users' emotional states through their text inputs, vocal tones, and facial expressions, providing personalized music recommendations that resonate with their current moods. The main objective is to create a system that not only comprehends the user's emotional state but also predicts their musical inclinations accurately. By integrating machine learning models capable of analyzing text's tone, voice, and facial emotions, we aim to offer a holistic solution that tailors music recommendations to individual emotions, bringing depth and authenticity to the music listening experience.