Dutch Data Science Week Workshop

• Signal processing applications
• Convolution
• Fourier analysis

• 1D signals: filter banks, pooling
• 2D signals: bag-of-words

• PCA / LDA
• Convolutional neural network

• Speaker / gender / age / nationality recognition...
• ...based on Fourier / bag-of-words / convnet

This repo uses conda's virtual environment for Python 3.

Install (mini)conda if not yet installed by following the instructions on https://conda.io/docs/install/quick.html.

Create the environment using the environment.yml config file:

$ cd /sp/for/datascience/ddsw/folder
$ conda env create -f environment.yml
$ source activate dsp

If that doesn't work, inspect the environment.yml and install the packages you need one by one.

Then, start a Jupyter notebook server

$ jupyter-notebook

and pick the notebook you want to run.

Or view a notebook as a presentation (if applicable; this is indicated by the presence of 'Slide Type' dropdowns in the top right of the notebook cells)

$ # possible presentations are intro.ipynb, feature-engineering.ipynb and feature-learning.ipynb
$ jupyter nbconvert intro.ipynb --to slides --post serve

In the hackathon we will apply Digital Signal Processing methods to audio sigals. Provided is a handful of speech samples and associated metadata of the speakers. Based on that, we'll try to recognize the speaker's identity/gender/age/etc using machine learning. See hackathon/hackathon.ipynb for the assignment; or a couple of my implementations in hackathon/hackathon-answers.ipynb.

In fourier-lab.ipynb we will look at how fourier analysis can help us to find periodicity in a timeseries, and we will use FFT to identify outliers in the sunspots dataset. As a bonus exercise (more of a thought experiment), we look at how we can use FFT for extrapolation. See fourier-lab-answers.ipynb for possible solutions.

In convnet-lab.ipynb we implement an end-to-end transfer learning pipeline using Keras. We will train a CNN on the first 5 digits of MNIST, and use the features learnt at this step to classify the last 5. A student should be able to solve this exercise by following the lecture material in convnet.ipynb; solutions are in convnet-lab-answers.ipynb