audio-analysis-mcp 0.1.0

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audio-analysis-mcp

A Python MCP server providing audio analysis tools for AI-driven sound recreation. It imports, separates, analyzes, and compares audio so an AI agent can configure hardware synthesizers to match a target sound.

Tools

Tool	Description
import_audio	Import a local audio file, normalize to 44.1kHz 16-bit mono WAV
stem_separate	Separate audio into stems (vocals, drums, bass, other, guitar, piano) using Demucs
audio_list_devices	List available audio input devices
audio_render	Capture audio from a system device (BlackHole, USB audio)
spectrum_analyze	Extract mel spectrogram, spectral features, ADSR, and modulation
audio_compare	Compare target vs. synthesized audio (mel spectrogram distance, per-band energy)
note_transcribe	Polyphonic transcription via Basic Pitch — outputs MIDI + note events JSON
note_triage	Analyze transcription, select best candidate notes for isolation
note_isolate	Isolate a note from audio within a time-frequency box via STFT masking
amplitude_analyze	Per-cluster ADSR analysis with cross-candidate consistency check (not ready for production — see docs/TODO.md)

Install & run

Requires uv (which provisions Python 3.11 for you — the pin matters: Basic Pitch needs CoreML/TensorFlow, which break on 3.12+).

1. Install uv: curl -LsSf https://astral.sh/uv/install.sh | sh

2. Add this to your MCP client config:

   {
     "mcpServers": {
       "audio-analysis-mcp": { "command": "uvx", "args": ["audio-analysis-mcp"] }
     }
   }
   

3. Restart the client. Most tools work out of the box; three need optional deps (below): audio_render and audio_list_devices need PortAudio, and structure_analyze needs SongFormer.

Optional: audio_render (system-audio capture)

audio_render / audio_list_devices need PortAudio (brew install portaudio on macOS), plus BlackHole for system audio. Without them the server still runs; only those two tools error.

Optional: structure_analyze (song-structure detection)

Needs SongFormer. Enable it by adding it to the run:

{
  "mcpServers": {
    "audio-analysis-mcp": {
      "command": "uvx",
      "args": ["--with", "songformer @ git+https://github.com/uribrecher/SongFormer.git@v0.2.0", "audio-analysis-mcp"]
    }
  }
}

License note: this pulls MuQ model weights licensed CC-BY-NC-4.0 (non-commercial use only). SongFormer's own code/weights are CC-BY-4.0 (ASLP-lab/NPU).

FastAPI service mode

The HTTP /jobs/* service is an optional extra: uvx --from 'audio-analysis-mcp[service]' python -m audio_analysis_mcp.service.

Development

uv sync --dev --group research --extra service   # dev tools + signalflow (tone_generation tests)
uv run pytest -m "not slow"       # fast suite (CI default)
uv run mypy src/                  # type check
uv run python -m audio_analysis_mcp  # run the stdio server from source

Scratch tools

The scratch/ directory holds ad-hoc Python scripts used during research and debugging — not part of the MCP server. They explore algorithms (clustering, ADSR fitting, envelope shapes, overlap detection), inspect intermediate outputs from real songs (e.g. Van Halen "Jump" triage clusters), and generate the plots referenced from docs/TODO.md and docs/research/.

These scripts are intentionally untested and may bit-rot as the underlying analysis modules evolve. Treat them as a notebook: useful starting points for reproducing past experiments or scaffolding new ones, not as a stable API. Run them directly with uv run python scratch/<script>.py.

License

Licensed under the GNU General Public License v3.0 (GPL-3.0-or-later). See LICENSE.

This project depends on GPL-licensed components (notably vmo and cvxopt, both GPLv3), so the combined work is distributed under the GPL.