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
Please allow approximately 45 minutes to attend the presentation and Q&A session. We will be recording this webinar, so if you can't make it for the live broadcast, register and we will send you a link to watch it on-demand.