Smart Music Recommendation System Integrating NLP and Hybrid Algorithms

In an era where technological growth gives a massive amount of knowledge in music, intuitive and personalized music delivery forms the cornerstone for better user experience and satisfaction. The increasing reliance on voice-driven interfaces can be leveraged to create newer music recommendation systems. This paper explores the development of the Smart Music Recommendation System, which will use natural language processing (NLP), speech recognition, and hybrid algorithms to recommend music to the user based on his mood input through voice. The system leverages Google Speech Recognition API for real-time voice interaction ensuring a seamless interaction between the user and the system. The system additionally detects the user’s emotional state from voice commands, while search algorithms are employed to fetch and recommend songs. A hybrid recommendation approach combines content-based filtering for emotion-specific playlist generation for every mood for an enhanced listening experience. An exceptional feature in our system is offline download using Yt-dlp to any local device and playback processes using an automated threading mechanism. The system proposed here was tested and gave efficient performance in voice interaction, song suggestion according to mood, and coordination with external platforms for retrieving and playing songs.