Signal Analysis and Feature Extraction for AI with Wavelets
Building AI models with signal and time-series data has become very popular for advanced applications in predictive maintenance and health monitoring, automated driving systems, financial portfolio management, biomedical systems, and many others. Robust signal analysis, preprocessing and feature extraction techniques are critical to building these models.
Analyzing physiological, speech, vibration, and other non-stationary signals with traditional Fourier based signal processing techniques can be challenging. Wavelet based techniques can help address the limitation of these techniques and build better AI models.
In this session, through detailed examples, you will learn how to perform:
- Wavelet analysis with apps in MATLAB without needing to be an expert
- Clean and preprocess data with signal filtering with wavelets
- Feature extraction from signals data for machine learning and deep learning workflows with multiresolution analysis and wavelet Scattering
About the Presenter
Esha Shah is a Product Manager at MathWorks focusing on Signal Processing and Wavelets Toolbox. She supports MATLAB users focusing on advanced signal processing and AI workflows. Before joining MathWorks, she received her Master’s in Engineering Management from Dartmouth College and Bachelor’s in Electronics and Telecommunication Engineering from Pune University, India.
Recorded: 22 Sep 2021
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