sonosthesia-audio-pipeline 0.0.9

Sonosthesia tools for baking audio analysis data

sonosthesia-audio-pipeline

Python based tooling to analyse audio files and write results to file for use in realtime visualization apps. Results can be written using Message Pack for efficient (de)serialization or JSON for human readable output. Readers are provided for the Unity timeline, to be used alongside the original audio files.

Installation

Installation requires python (version 3.9 to 3.12 are supported). Once you have python you can run

pip install sonosthesia-audio-pipeline

Python Pipeline

Sound Analysis

Librosa is used to extract audio features which are of particular interest for driving reactive visuals, notably:

• Beats and tempo
• RMS magnitude
• Energy in low, mid and high frequency bands
• Onsets
• Spectral centroid and bandwidth

The analysis data is writen to a file using MessagePack which is highly efficient both in terms of size and (de)serialization performance. The serialized data is an array of dictionaries each of which represents a time step

{
    'time': float,
    'rms': float,
    'lows': float,
    'mids': float,
    'highs': float,
    'centroid': float,
    'onset': bool
}

There is a preview mode which uses matplotlib to present analysis data

Source Separation

Currently using Demucs because it seems to score better on overall SDR and is a lot easier to install with pip than Spleeter.

Look into using Essentia which seems to be good for highler level musical descriptors.

Notes

• Can install python with chocolatey or homebrew
• Note works with Python up to 3.12, issues with 3.13 as both librosa and matplotlib do not support it
• Exit command prompt on windows with Ctrl Z and enter
• Locate interpreter on windows with python -c "import os, sys; print(os.path.dirname(sys.executable))"

Unity Timeline

The com.sonosthesia.audio package provides tooling which allows audio analysis files generated using the Python Pipeline described above to be played alongside corresponding timeline audio through sonosthesia signals