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.
Recorded: 14 Dec 2023
Select a Web Site
Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .
You can also select a web site from the following list
How to Get Best Site Performance
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.
Americas
- América Latina (Español)
- Canada (English)
- United States (English)
Europe
- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)
- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom (English)
Asia Pacific
- Australia (English)
- India (English)
- New Zealand (English)
- 中国
- 日本Japanese (日本語)
- 한국Korean (한국어)