Automated Genre Tagging for Music Libraries

Problem Context

Music curators, DJs, and data analysts maintain spreadsheets with thousands of songs. They need to assign genres to artists for playlists, analytics, or curation. Manually typing genres is slow and error-prone, especially when artists have multiple genres or inconsistent labels.

Pain Points

Users waste hours typing genres manually or struggling with Excel formulas like VLOOKUP. They face errors when artists have conflicting genres (e.g., a band labeled as 'rock' in one row and 'indie' in another). Spreadsheets become messy, and workflows stall when genre data is incomplete.

Impact

Delayed playlists, inaccurate analytics, and frustrated teams cost time and money. For example, a DJ might lose bookings if their playlist genres are wrong, or a data analyst could mislabel trends. The tedium also leads to burnout and avoids larger projects.

Urgency

This problem grows with library size—every new song adds manual work. Users can’t ignore it because genre accuracy is critical for discovery, monetization (e.g., streaming splits), and professional reputation. A one-time fix (like a script) won’t scale.

Target Audience

Music curators, DJs, radio station staff, data analysts in the music industry, and small teams managing playlists or music databases. Also applies to hobbyists with large digital collections who want to organize their libraries efficiently.

Solution Approach

A web-based tool that integrates with Excel/Google Sheets via add-in or API. Users upload their song libraries, and the tool auto-fills genres by matching artists to a crowdsourced genre database. It handles single/multiple genres per artist and learns from user corrections to improve accuracy over time.

Key Features

1. Crowdsourced Genre Database: A growing dataset of artist-genres, updated by users and verified by the community.
2. AI Suggestions: For artists not in the database, the tool suggests genres based on similar artists.
3. Conflict Resolution: Flags artists with conflicting genres and lets users pick the correct one (or add multiple).

User Experience

Users paste their spreadsheet into the tool or connect their Excel file. The tool scans for artists, matches them to genres, and populates the 'Genre' column automatically. Users review suggestions, correct errors, and export the updated spreadsheet—all in under 5 minutes. For recurring use, they can set up auto-syncs or bulk updates.

Differentiation

Unlike Excel formulas (which break with new data) or manual work, this tool scales with library size. It’s faster than VLOOKUP, more accurate than AI-only tools (which hallucinate genres), and cheaper than hiring a data entry team. The crowdsourced database ensures genres stay up-to-date without user effort.

Scalability

Starts with individual users, then adds team plans for studios or radio stations. Can expand to support Spotify/Apple Music API integrations, AI-trained genre predictions, or custom genre taxonomies for niche markets (e.g., classical sub-genres).

Expected Impact

Saves 5+ hours/week per user, eliminates errors in genre data, and enables faster playlists/analytics. For teams, it reduces onboarding time for new hires and ensures consistency across projects. The tool becomes a critical part of their workflow—removing it would require reverting to manual work.