Signal Feature Extraction using Time-Frequency Analysis for AI Workflows

Overview

Wavelet transforms are a powerful tool for analyzing signals in both the time and frequency domains. They are particularly useful for analyzing non-stationary and transient signals. In this webinar, we will explore the use of wavelet transforms and feature extraction from real-world signals, which can then be used for a range of signal processing tasks and for training AI models. We will explore options for interactive, app-based workflows that allow users to analyze signals without the need write any code. This webinar is ideal for engineers, algorithm developers, researchers, scientists, and engineering students who work in signal processing and AI fields.

Highlights

• Perform signal and wavelet analysis
• Extract features from signals to train AI models.
• Explore apps available for signal processing
• Apply wavelet transforms and feature extraction to real-world examples

About the Presenter

Akash Gopisetty is a Product Marketing Manager at MathWorks, specializing in signal processing, wireless communication, and large-scale system modeling. He holds a Master of Science degree in Electrical and Computer Engineering from Carnegie Mellon University (Go Tartans!). Prior to this role, he worked in academia promoting STEM education at the pre-university level in schools across the world. When not at work, Akash enjoys making ice cream, playing tennis and hiking.