Libhum is a Python library and set of utilities to extract and compare electricity frequency signals (ENF).
The library uses GPU accelaration (CUDA or OpenCL) to accurately match ENF signals in seconds.
On an Apple Macbook Air M1, the library is able to match an 1 hour signal over a 10 years history in about 20 seconds.
pip install libhum
Use the compute_enf command to extract the main ENF from an audio or video file to an .enf file:
libhum compute_enf samples/target.ogg target.enf --plot
By default, the library is looking for an ENF signal in the 50 Hz band. Use the -f/--frequency
option to use a different frequency.
Use the match_enf command to find the best matches between two signals:
libhum match_enf samples/reference.enf samples/target.enf --plot
The default backend is using Numpy and is considerably slower than the OpenCL or CUDA backends which
rely on GPU accelaration. Append the --backend=cuda or --backend=opencl option to use a
GPU-accelerated backend.
Some of the signal processing algorithms used by the library originate from Robert Heaton's ENF matching library.
See LICENSE file for details.
If you use Libhum in any scientific publication, please use the following BibTex citation:
@software{Javaux_Libhum,
author = {Javaux, Raphael},
license = {LGPL-3.0},
title = {{Libhum}},
url = {https://github.com/RaphaelJ/libhum},
year={2024},
}