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AudioMuse-AI is an open-source system designed to automatically generate playlists and analyze music libraries using artificial intelligence and audio signal processing techniques. The platform runs locally in a Dockerized environment and performs detailed sonic analysis on audio files to understand characteristics such as tempo, mood, and acoustic similarity. By analyzing the underlying audio content rather than relying on external metadata services, the system can organize large personal music libraries and generate curated playlists for different moods or listening contexts. AudioMuse-AI integrates with several popular self-hosted music servers including Jellyfin, Navidrome, and Emby, allowing users to extend existing media servers with advanced AI-powered recommendation capabilities. The system uses machine learning and audio analysis tools such as Librosa and ONNX models to extract features directly from audio tracks.
Features
- Automatic playlist generation based on audio content analysis
- Local sonic feature extraction using tools such as Librosa and ONNX
- Dockerized deployment environment for simplified installation
- Integration with media servers including Jellyfin, Navidrome, LMS, and Emby
- AI-driven mood and similarity analysis for music organization
- Self-hosted system that avoids reliance on external music APIs
Project Samples
