SigLib Digital Signal Processing and Machine Learning Library
What is SigLib?
SigLib is a highly portable DSP and ML library that supports many different architectures and operating systems, include:
- ARM Cortex-A and Cortex-M
- DSPs from TI, ADI etc.
Why use SigLib?
SigLib includes over 1000 fully tested DSP functions and now includes functions for Artificial Intelligence and Machine Learning.
SigLib is the easiest way to develop portable signal processing applications because the code can be developed graphically on a standard desktop or laptop computer (Windows, Linux or Mac OSX) and then re-compiled for the target DSP, without the graphical functionality.
Ensure you have the required dependencies installed, as per the following section:
Gnuplot and Gnuplot/C
SigLib uses Gnuplot and Gnuplot/C for displaying signals and data graphically.
Gnuplot/C is included in the SigLib package and pipes data to Gnuplot.
Under Windows, download and install the gp528-win64-mingw.exe package from here : https://sourceforge.net/projects/gnuplot/files/gnuplot/5.2.8/gp528-win64-mingw.exe/download . add the location of the installation to the system path.
Under Ubuntu Linux, use the following command::
sudo apt install gnuplot
Extract the .zip archive and set the appropriate compiler environment variables.
Building The Source
All of the source is included in the 'src' folder, which includes batch files, shell scripts and makefiles for the different supported architectures.
Documentation is available in the folder 'docs', in particular :
- siglib_users.pdf - SigLib User's Manual - This is the place to start for installation and overview information
- siglib_ref.pdf - SigLib Function Reference Manual - This is the place to go for detailed functional overviews
The easiest way to navigate the folders is to use the provided html files, starting with Welcome.html.
SigLib is free for educational and personal use, all other use cases require a developer's license, which is available from: Sigma Numerix Ltd.
Sigma Numerix Ltd are specialists in writing and supporting Signal Processing and Machine Learning applications.
To inquire about our services, contact us at [email protected].
Copyright ©; 2022, Sigma Numerix Ltd. All rights reserved. SigLib and Digital Filter Plus are trademarks of Sigma Numerix Ltd. All other trademarks acknowledged.
Sigma Numerix are continuously increasing the functionality of SigLib and reserve the right to alter the product at any time.