This is SIGNALS lab is about taking signals from the time domain into the frequency domain and will focus on analysing digital signals with the Discrete Fourier Transform.
The signal processing labs will use Jupyter notebooks: a combination of Python code and notes that you access using a web browser.
You can find a github repository for the signals notebooks here: https://github.com/laic/uoe_speech_processing_course. You can find the notebooks for this module in that repository under signals/signals-lab-1.
Getting started with Jupyter Notebooks
Don’t worry if you don’t know any Python – this is not a formal requirement of the course, and you’ll learn what you need simply by doing the exercises. You can run the notebooks on your personal computer but we’d suggest that you use Edina Notable service You can access from the Learn site for this course. You can also use direct login if you are already logged into Learn.
- Use a web browser to open the instructions; read them through once before doing anything else.
- The default would be to then follow “Run the notebooks online using Edina Noteable“.
- If you feel confident or already know Jupyter notebooks, you could also run the notebooks on your own computer: follow “Running Jupyter Notebooks on your computer“
- This involves installing Python 3.9 and Miniconda on your own computer, but these are things you will find useful more generally in the future too. This uses around 1 GB of disk space.
- Once you have succeeded, finish this task by completing Section 4 of the Jupyter notebook sp-m0-how-to-start
You can always ask the tutors in the lab sessions for help with setting things up!
For further technical support in setting up Jupyter Notebooks, use this forum.
Please note, the material in notebooks is to support your learning. Nothing related to the code specifically is directly assessed (though the concepts in the notebooks marked essential may be). You don’t need to know Python to do this course, though MSc SLP students will need to know Python for many other courses so getting a more bit of practise/exposure definitely doesn’t hurt!
Notebooks for this module
First have a look at the guide to the signals notebooks. You can just look in the signals directory once you’re downloaded your own copy of the notebook repository.
After that, there is one essential notebook to work through:
The lab is setup so the focus is mostly on changing small bits of existing code. However, if you are already experienced with python and numpy/matplotlib/librosa, you may want to try coding up some of the steps we take yourself.
Those notebooks are relatively light on the maths behind these technologies, but there are also some extension materials in signals-lab-1/extension directory that go into the details more.