AudioIC

AudioIC provides tools for calculating the information content (IC) as a proxy for human experienced surprise when listening to music. This repo is the official implementation of:

• "Estimating Musical Surprisal from Audio in Autoregressive Diffusion Model Noise Spaces". Code and documentation is coming soon.
• "Estimating Musical Surprisal in Audio", retrained on open data.

AudioIC includes a command line tool and python classes for calculating IC using a GIVT model.

Installation

You can install the package using pip with or without the extra dependencies required for the demo.

Install the package for general use:

pip install audioic

Install the package with demo dependencies:

git clone https://github.com/sonycslparis/audioic.git
cd audioic
pip install ".[demo]"

Usage

Running the audioic Command-Line Tool

The audioic command-line tool allows you to compute the information content (IC) of audio files. To use it, specify the audio files you want to process and provide an output directory where the results will be saved as CSV files:

python -m audioic.audioic --audio_files "['<audio-file1>', '<audio-file2>', ...]" --output_dir <output-dir> --device "cpu"

Replace <audio-file1>, <audio-file2>, etc., with the paths to your audio files, and <output-dir> with the directory where you want the output files to be stored.

To run the tool on a GPU (default), specify the --device argument as "cuda":

CUDA_VISIBLE_DEVICES=<device-id> python -m audioic --audio_files "['<audio-file1>', '<audio-file2>', ...]" --output_dir <output-dir> --device "cuda"

Replace <device-id> by a cuda device id.

Using the AudioIC programmatically

The demo.ipynb notebook demonstrates how to use the library programmatically to calculate and visualize the IC of audio files.

Citation

If you use this project in your research, please cite the following paper:

@INPROCEEDINGS{10890619,
    author={Bjare, Mathias Rose and Cantisani, Giorgia and Lattner, Stefan and Widmer, Gerhard},
    booktitle={ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
    title={Estimating Musical Surprisal in Audio}, 
    year={2025},
    volume={},
    number={},
    pages={1-5},
    keywords={Computational modeling;Music;Predictive models;Signal processing;Brain modeling;Transformers;Electroencephalography;Complexity theory;Integrated circuit modeling;Speech processing;Music information retrieval;Musical surprisal;Perceptual models;Neural networks},
    doi={10.1109/ICASSP49660.2025.10890619}}

License

This project is licensed under the CC BY-NC 4.0 License.

To obtain a commercial license, please contact music@csl.sony.fr.