AudioMuse-AI is an open source, self-hosted application that uses AI-powered sonic analysis to organize music libraries and generate smart playlists. Unlike traditional music managers that rely on metadata such as genres or tags, AudioMuse-AI analyzes the actual audio characteristics of your music to discover similar tracks, create playlists, and visualize your collection. It integrates with popular self-hosted music servers including Jellyfin, Navidrome, Emby, Lyrion Music Server, and other Open Subsonic-compatible platforms.
Many music applications depend on manually assigned genres or online recommendation services. AudioMuse-AI takes a different approach by performing local machine learning analysis on your audio files using technologies such as Librosa and ONNX. This allows it to generate recommendations based on how songs actually sound rather than relying on metadata or external APIs.
Because all analysis happens locally, the software is especially appealing to users who value privacy and maintain self-hosted music libraries.
Key Features of AudioMuse-AI
AI-Based Sonic Analysis
AudioMuse-AI analyzes the acoustic properties of every track in your collection to build an internal representation of each song. This enables highly accurate recommendations based on musical similarity rather than artist names or genres.
Automatic Playlist Generation
The application can automatically generate playlists for different moods, styles, or listening sessions. Users can also create playlists from a favorite song, allowing the system to find tracks with similar sonic characteristics.
Music Map Visualization
One of the standout features is the interactive Music Map, which displays songs in a two-dimensional space based on their musical similarity. This offers a unique way to explore large music collections visually.
Song Paths
AudioMuse-AI can create smooth transitions between two songs by selecting tracks that gradually bridge the musical differences, making playlists feel more natural and cohesive.
Natural Language Search
Users can search for music using descriptive phrases such as "calm piano music" or "high-tempo low-energy songs." Recent versions also introduce lyric-based semantic search for discovering music by meaning as well as sound.
Self-Hosted and Privacy Focused
All processing is performed locally without uploading music to cloud services. AudioMuse-AI is distributed as Docker containers and supports Docker Compose, Podman, and Kubernetes deployments.
Download AudioMuse-AI v3.7.0 - Software Mirrors |
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AudioMuse-AI v3.7.0 for Windows |
AudioMuse-AI v3.7.0 for macOS |
AudioMuse-AI v3.7.0 for LinuxAudioMuse-AI-x86_64-linux.rpm | 1.4 GB AudioMuse-AI-x86_64-linux.deb | 1.4 GB |
AudioMuse-AI v3.7.0 Source Code |
AudioMuse-AI v3.7.0 Release Notes:💛 Give AudioMuse-AI a gift this Christmas AudioMuse-AI will always remain free and open source, because everyone deserves the chance to rediscover their music, regardless of their resources. But keeping it alive, improving it, and making it grow takes time, dedication, and resources. Right now, we have 2,521 users on Navidrome alone, based on the users who have anonymous data collection enabled on Navidrome. If just 20% of them donate €10, that would be enough to help sustain AudioMuse-AI and keep it growing for another year. Your donation helps cover the AI subscriptions needed for development (~€220/month), homelab electricity for testing (~€20/month) and further improvements and research. As the year comes to an end and Christmas approaches, if AudioMuse-AI has brought something valuable to your music library, consider making a small donation as a gift to the project. And if you are fortunate enough to have a little more to give, your support could make a real difference in keeping it alive and helping it reach even more people. Every contribution matters. Thank you for helping AudioMuse-AI remain free for everyone. 💛 Release Date: October 29th, 2026 AudioMuse-AI v3.7.0 introduces DCLAP Text Search By Song Similarity tab, allowing you to search for similar songs using the DCLAP index in addition to the default MusicNN index. This release also includes several improvements to scheduled task integration in the plugins, cache cleaning in the macOS native version, and the stability of audio analysis and clustering.What's Changed
New Contributors
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Performance and User Experience
Once the initial library analysis is complete, playlist generation and music discovery are fast and responsive. Initial analysis may take some time depending on the size of the music library and available hardware, but it only needs to process new or changed tracks afterward. Minimum recommended hardware includes a modern four-core CPU and 8 GB of RAM.
The web interface is clean and continues to improve with features such as a setup wizard, dashboard, multi-user support, and authentication. The project is actively maintained with frequent feature updates and bug fixes.
Pros
Free and open source.
AI analyzes actual audio instead of relying on metadata.
Excellent automatic playlist generation.
Interactive music visualization.
Privacy-friendly local processing.
Supports Jellyfin, Navidrome, Emby, and Lyrion.
Docker and Kubernetes support.
Active development and growing community.
Cons
Initial music analysis can take several hours for large libraries.
Requires self-hosting knowledge.
Benefits are greatest with well-organized local music collections.
Hardware requirements are higher than traditional music library managers.
Who Should Use AudioMuse-AI?
AudioMuse-AI is ideal for self-hosting enthusiasts, audiophiles, Jellyfin and Navidrome users, music collectors, and anyone with a large local music library who wants smarter playlist generation without relying on commercial streaming services.
Users who primarily listen through Spotify, Apple Music, or YouTube Music will benefit less, since AudioMuse-AI is designed for self-hosted collections rather than streaming platforms.
Final Verdict
AudioMuse-AI is one of the most innovative open source projects for self-hosted music libraries. Its AI-powered sonic analysis, intelligent playlist generation, visual music exploration, and privacy-first design offer capabilities that go well beyond traditional music management software.
Although it requires a self-hosted environment and an initial analysis period, the results are impressive. For users running Jellyfin, Navidrome, Emby, or similar platforms, AudioMuse-AI is an excellent addition that can transform how a personal music collection is explored and enjoyed.
Developer:
NeptuneHub
Operating System:
Windows / macOS / Linux
Date Added:
2026-10-30T07:07:21.390Z
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