Date: Tuesday, May 17 (Main Conference Day 1)
Start Time: 12:00 pm
End Time: 12:30 pm
As embedded processors become more powerful, our ability to implement complex machine learning solutions at the edge is growing. Vision has led the way, solving problems as far-reaching as facial recognition and autonomous navigation. Now, ML audio is starting to appear in more and more edge applications, for example in the form of voice assistants, voice user interfaces and voice communication systems. Although audio data is quite different from video and image data, ML audio solutions often use many of the same techniques initially developed for video and images. In this talk, we’ll introduce the ML techniques commonly used for audio at the edge, and compare and contrast them with those commonly used for vision. Come and get inspired to integrate ML-based audio into your next solution.