AudioMuse

Most people who have been recording audio for years, whether musicians with old demos, podcasters with unused tape, or field recordists with ambient captures, have a hard drive full of files they have not listened to in years. AudioMuse addresses this: it is an AI tool that analyzes your local audio library and helps you find what you forgot you had.

AudioMuse is an open-source AI application that performs sonic analysis on local audio files. It extracts features like tempo, key, timbre, energy, and mood from each track, then indexes them for intelligent search. Instead of browsing by filename, users can search by sound characteristics: "find tracks similar to this one" or "show me all dark, ambient recordings under 90 BPM."

How it works

AudioMuse scans a user-specified directory of audio files (WAV, MP3, FLAC, and other common formats). For each file, it runs a feature extraction pipeline that analyzes spectral content, rhythmic patterns, harmonic structure, and dynamic range. These features are stored in a local index.

The search interface accepts both text queries ("upbeat jazz with piano") and reference tracks ("find more like this"). The AI computes similarity scores between the query and the indexed library, returning ranked results. Because processing happens locally, no audio data leaves the user's machine.

Key features

• Local processing. All analysis runs on-device. Audio files never leave the user's computer.
• Sonic feature extraction. Analyzes tempo, key, timbre, energy, mood, and dozens of other acoustic features per track.
• Similarity search. Uses a reference track to find sonically similar files in the library.
• Text-based search. Query by mood ("dark"), instrumentation ("piano"), tempo ("slow"), or any combination.
• Open source. Code is publicly available on GitHub, allowing customization and community contributions.

Who it is for

Musicians and producers with large libraries of unused recordings, demos, and sample collections. Podcasters with hours of recorded but unorganized tape. Sound designers and field recordists managing thousands of ambient captures. Anyone with a large local audio collection that has become too large to browse manually.

Limitations

• Local-only. AudioMuse is a desktop application, not a cloud service. It requires local storage and processing power.
• Setup required. Users must install the software, point it at audio directories, and wait for the initial indexing scan.
• Feature extraction accuracy. Sonic analysis works well for distinct musical characteristics but can be inconsistent for ambient, atonal, or heavily processed audio.
• No cloud sync. There is no cross-device library sync. Each installation indexes its own local files.

AudioMuse is available on GitHub.