data-driven-audio-signal-processing-exercise

Data Driven Audio Signal Processing - A Tutorial with Computational Examples

This tutorial accompanies the lecture Data Driven Audio Signal Processing. The lecture and the tutorial are designed for International Standard Classification of Education (ISCED) level 7 (Master, in total 6 ECTS credits).

Jupyter notebooks can be accessed via the services

• dynamic version using mybinder: https://mybinder.org/v2/gh/spatialaudio/data-driven-audio-signal-processing-exercise/dev?labpath=index.ipynb
• static version using nbviewer: https://nbviewer.org/github/spatialaudio/data-driven-audio-signal-processing-exercise/blob/dev/index.ipynb
• sources (tex, ipynb) at: https://github.com/spatialaudio/data-driven-audio-signal-processing-exercise

Jupyter notebooks with rendered outputs can be viewed at https://nbviewer.org/github/spatialaudio/data-driven-audio-signal-processing-exercise/blob/main/index.ipynb

Versions / Tags

• v0.1 for winter term 2021/22, initial version
• v0.2 for winter term 2022/23
• v0.3 for winter term 2023/24, many beamer tex slides added, CI
• TBD for winter term 2024/25

Branch Conventions

• the default branch of the repository is dev used for development
• all notebook outputs in dev branch are cleared for convenient diff handling
• main branch contains notebooks with rendered outputs, which is maintained from time to time
• do not rely on main branch as this is hard reset from time to time
• probably in future we rename main to somewhat less confusing

Anaconda Environment for Local Usage

The Anaconda distribution is a convenient solution to install a required environment, i.e. to have access to a Jupyter Notebook renderer with a Python interpreter on a personal computer. It is very likely that a very recent installation of Anaconda already delivers most of the required standard packages just using the base environment. It is however good practice to create a dedicated environment for each project. So, for this tutorial we might use a myddasp (or whatever name works for us) environment. We might consider the following install routine:

• clone the repo to local machine (if not already available)
  • git clone [email protected]:spatialaudio/data-driven-audio-signal-processing-exercise.git (via SSH) or
  • git clone https://github.com/spatialaudio/data-driven-audio-signal-processing-exercise.git (via https) or
  • get a zip file from current dev commit via https://github.com/spatialaudio/data-driven-audio-signal-processing-exercise/archive/refs/heads/dev.zip
• get into the folder where the exercises are located, e.g. cd my_ddasp_folder
• in the subfolder .binder the environment.yml can be used to create a dedicated conda myddasp environment as
  • conda env create -f environment.yml --force
  • (we can remove this environment with conda env remove --name myddasp)
• activate this environment with conda activate myddasp
• this should also have installed sound / audio related libraries using pip
  • pip install pyloudnorm==0.1.0
  • we might check this with pip list
• Jupyter notebook renderer needs to know our dedicated environment: python -m ipykernel install --user --name myddasp --display-name "myddasp"
• we might want to archive the actually installed package versions by
  • python -m pip list > detailed_packages_list_pip.txt and
  • conda env export --no-builds > detailed_packages_list_conda.txt
• start a Jupyter lab environment via a local server instance by jupyter lab
• start the landing page index.ipynb of the tutorial
• make sure that the notebooks we want to work with are using our dedicated kernel myddasp

Authorship

• University of Rostock:
  • Frank Schultz, concept, coding
  • Sascha Spors, concept

Referencing

Please cite this open educational resource (OER) project as Frank Schultz, Data Driven Audio Signal Processing - A Tutorial Featuring Computational Examples, University of Rostock ideally with relevant file(s), github URL, commit number and/or version tag, year.

License

• Creative Commons Attribution 4.0 International License (CC BY 4.0) for text/graphics
• MIT License for software