Machine Learning with Signal Processing Techniques
Stochastic Signal Analysis is a field of science concerned with the processing, modification and analysis of (stochastic) signals.
Anyone with a background in Physics or Engineering knows to some degree about signal analysis techniques, what these technique are and how they can be used to analyze, model and classify signals.
Data Scientists coming from a different fields, like Computer Science or Statistics, might not be aware of the analytical power these techniques bring with them.
In this blog post, we will have a look at how we can use Stochastic Signal Analysis techniques, in combination with traditional Machine Learning Classifiers for accurate classification and modelling of time-series and signals.
At the end of the blog-post you should be able understand the various signal-processing techniques which can be used to retrieve features from signals and be able to classify ECG signals (and even identify a personby their ECG signal), predict seizures from EEG signals, classify and identify targets in radar signals, identify patients with neuropathy or myopathyetc from EMG signals by using the FFT, etc etc.
In this blog-post we’ll discuss the following topics:
- Basics of Signals
- Transformations between time- and frequency-domain by means of FFT, PSD and autocorrelation.
- Statistical