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This is a toolbox in form of a python package that contains algorithms to be used in dsp (digital signal processing) research projects.
This is kind of a "sandbox" project with many different experimental implementations across a variety of DSP-related topics. Some parts are more thoroughly tested and validated than others, so "caution" is advised. Please feel free to reach out in case you find bugs or want to talk about certain functionality.
It is under active development and it will take some time until it reaches a certain level of maturity. Beware that backwards compatibility is not an actual concern and significant changes to the API might come in the future. If you find some implementations interesting or useful, please feel free to use it for your projects and expand or change functionalities.
Check out the examples_ for some basic examples of the dsptoolbox package
and refer to the documentation_ for the complete description of classes and functions.
Use pip to install dsptoolbox
.. code-block:: console
$ pip install dsptoolbox
(Requires Python 3.10 or higher)
In order to install the package successfully using Linux, you need to install
PortAudio manually, since installing sounddevice_ will not do it automatically. To do this,
run the following commands on your console:
.. code-block:: console
$ sudo apt-get install libasound-dev libportaudio2 libsndfile1
If this does not work properly for some reason, refer to the documentation for
sounddevice_ or PortAudio_.
.. _documentation: http://dsptoolbox.readthedocs.io/ .. _examples: https://github.com/nico-franco-gomez/dsptoolbox/tree/main/examples .. _sounddevice: https://python-sounddevice.readthedocs.io/en/0.4.5/ .. _PortAudio: http://www.portaudio.com
FAQs
Collection of dsp algorithms to be used for analysis of audio signals
We found that dsptoolbox demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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