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DSP_Python_notes_and_examples

Python notebooks for DSP lectures and LAB exercises

Welcome to the course repository for DSP. This collection of notebooks allows you to explore signal processing concepts, analyze audio files, and design filters using Python.

📂 Repository Structure

  • Predavanja/: Interactive notebooks covering core theory (e.g., Sampling, Fourier Transform).
  • Lab/: Assignment notebooks for you to complete.
  • data/: Audio files (.wav) and datasets used in the notebooks.
  • Lib/: Helper scripts and custom functions.
  • Primeri/: Other examples (Puredata, Processing, Scilab, Octave, ...).

🚀 How to Use These Notebooks

Option A: Google Colab (Recommended)

You can run these notebooks directly in your browser without installing anything.

  1. Navigate to the notebook file on GitHub (e.g., inside Lab/).
  2. Click the "Open in Colab" badge at the top of the file (if available) or replace github.com with githubusercontent.com in the URL to import it manually.
  3. Important: To use the audio files in the data/ folder, you may need to run the setup cell provided in each notebook to clone this repo into the Colab environment.

Option B: Run Locally

  1. Clone this repository: 
    git clone [https://github.com/LAPSyLAB/DSP_Python_notes_and_examples.git](https://github.com/LAPSyLAB/DSP_Python_notes_and_examples.git)
cd dsp-course
  2. Install the required packages: 
    pip install -r requirements.txt
  3. Launch Jupyter Lab: 
    jupyter lab

Course Material by [FRI/University of Ljubljana]. Licensed under MIT.