pyaudio-helper 1.1.2

PyAudio Helper

PyAudio Helper

Dependencies for pyaudio

Across the three popular platforms, Windows, macOS, and Linux, pyaudio, is the underlying framework that pyaudio_helper relies upon. Getting PyAudio configured is different for all three OS's. Conda and CondaForge have support for installing pyaudio on both Linux and Windows. Under Python 3.6 and below PyAudio will install when pip installing the scikit-dsp-comm package, as described below. For Python 3.7+ PyAudio first needs to be installed using conda install pyaudio to obtain binary (whl) files.

The real-time audio DSP capabilities of pyaudio_helper allow for two channel algorithm development with real-time user control enabled by the ipywidgets when running in the Jupyter notebook.

Finally, we now can utilize the real-time DSP capabilities of pyaudio_helper to work in combination with streaming of I/Q samples using new functions rtlsdr_helper. This allows in particular demodulation of radio signals and downsampling to baseband analog signals for streaming playback of say an FM broadcast station. This new capability is featured as a short article at the end of this readme file.

All the capability of the package is available less PyAudio and the RTL-SDR radio, without doing any special installations. See the wiki pages for more information. Just keep in mind that now a Python 3.7+ install on windows must include the installation PyAudio as described above.

Installation Notes

Part of the processing architecture makes use of ipwidgets. These can be installed for you by specifying the extras option:

pip install pyaudio-helper[extras]

Installation is described in greater detail below.

1. pyaudio_helper.py wraps a class around the code required in PyAudio (wraps the C++ library PortAudio) to set up a non-blocking audio input/output stream. The user only has to write the callback function to implement real-time DSP processing using any of the input/output devices available on the platform. This resulting object also contains a capture buffer for use in post processing and a timing markers for assessing the processing time utilized by the callback function. When developing apps in the Jupyter Notebook there is support for the IPywidgets along with threading.

Authors

@mwickert

@andrewsmitty