AI-Driven Music Metadata for Curated Playlists: Energy, Mood, BPM.

The user, a 'data analyst with a large music collection,' explicitly seeks MP3 tagging software that adds 'the most metadata required for making playlists, especially including different songs' characteristics relevant to making playlists (energy, mood, BPM, tone, key).' This highlights a strong, unmet need for granular, analytical metadata beyond standard album information. A SaaS solution could leverage AI and machine learning to analyze audio files and automatically generate these sophisticated tags (mood, energy, specific instruments, genre sub-categories). It could offer a desktop client that syncs with a cloud service, allowing users to process their large libraries, query and filter based on these new tags, and export them for use in other players or DJ software. Monetization could be a subscription based on the volume of music processed or advanced features like custom tag categories and API access.