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data-driven-audio-signal-processing-lecture

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Supplementary materials to the lecture data driven audio signal processing

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# Data Driven Audio Signal Processing

This repository collects supplementary materials to the master's course Selected Topics in Audio Signal Processing given by [Sascha Spors](http://www.int.uni-rostock.de/Staff-Info.23+B6JmNIYXNoPWUxOTliMTNjY2U2MDcyZjJiZTI0YTc4MmFkYTE5NjQzJnR4X2pwc3RhZmZfcGkxJTVCYmFja0lkJTVEPTMmdHhfanBzdGFmZl9waTElNUJzaG93VWlkJTVEPTExMQ__.0.html) at the University of Rostock, Germany. The contents are provided as [Open Educational Resource](https://de.wikipedia.org/wiki/Open_Educational_Resources), so feel free to fork, share, teach and learn. You can give the project a Star if you like it.

## Getting Started

The Jupyter notebooks are accessible in various ways

* Online as [static web pages](https://nbviewer.org/github/spatialaudio/data-driven-audio-signal-processing-lecture/blob/main/index.ipynb)
* Online for [interactive usage](https://mybinder.org/v2/gh/spatialaudio/data-driven-audio-signal-processing-lecture/HEAD?labpath=index.ipynb) with [binder](https://mybinder.org/)
* Local for interactive usage on your computer

Other online services (e.g. [Google Colaboratory](https://colab.research.google.com),
[Microsoft Azure](https://azure.microsoft.com/), ...) provide environments for
interactive execution of Jupyter notebooks as well.
Local execution on your computer requires a local Jupyter/IPython installation.
The [Anaconda distribution](https://www.continuum.io/downloads) can be
considered as a convenient starting point.
Then, you'd have to [clone/download the notebooks from Github](http://github.com/spatialaudio/digital-signal-processing-lecture).
Use a [Git](http://git-scm.org/) client to clone the notebooks and then start
your local Jupyter server. For manual installation under OS X/Linux please
refer to your packet manager.

## Usage and Contributing

The contents are provided as [Open Educational Resource](https://de.wikipedia.org/wiki/Open_Educational_Resources).
The text is licensed under [Creative Commons Attribution 4.0](https://creativecommons.org/licenses/by/4.0/)
, the code of the IPython examples under the [MIT license](https://opensource.org/licenses/MIT).
Feel free to use the entire collection, parts or even single notebooks for your
own purposes.
I am curious on the usage of the provided resources, so feel free to drop a
line or report to [Sascha.Spors@uni-rostock.de](mailto:Sascha.Spors@uni-rostock.de).

data-driven-audio-signal-processing-lecture

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