Gabriele Bunkheila, MathWorks
Using deep learning in speech and audio poses important computational challenges when transitioning from research models to real-world designs. The need to work in a wide range of operating conditions requires large training datasets, and implementing designs on low-power embedded devices requires exploration to find the optimal parameters and the right trade-offs between prediction performance and computational complexity.
In this webinar, we’ll show how to use MATLAB and NVIDIA GPUs to build deep networks, accelerate data-intensive problems, and train multiple network configurations in parallel.
During the session you will learn about:
Gabriele Bunkheila is a senior product manager at MathWorks for audio and DSP applications. After joining MathWorks in 2008, he worked as a signal processing application engineer for several years, supporting MATLAB and Simulink users across industries from algorithm design to real-time implementations. Before MathWorks, he held a number of research and development positions, and he was a lecturer of sound theory and technologies at the national film school of Rome. He has a master’s degree in physics and a Ph.D. in communications engineering.
Recorded: 31 Mar 2020
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: .Select web site
You can also select a web site from the following list:
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.