Monday, 4 May, 14:30 – 18:00

Signal Processing Meets Deep Learning in MATLAB – From Getting Started to Developing Real-World Applications

The adoption of deep learning across a wide range of signal processing applications has been attracting an increasing level of attention over the last few years. Deep learning for real-world signal processing systems has accentuated the need for application-specific tools and expertise for creating, labelling, augmenting and processing the vast amounts of signal data required to train and evaluate the learning models.

Using MATLAB code and new features, we will start from the basics of designing and training a network. We will then move onto more advanced topics, including data annotation, advanced feature extraction, training acceleration on GPUs and GPU clouds, and real-time implementation of deep networks on embedded devices. While focusing on a practical speech-based example, we will also discuss applications to other types of signals, such as Communications, Radar, and Medical Devices.

Presenter Bio: Jihad Ibrahim is a principal software developer and product lead of Audio Toolbox at MathWorks. He joined MathWorks in 2006 and has contributed to the development of Signal Toolbox, DSP System Toolbox, Communications Toolbox, and Audio Toolbox. He received his PhD in Electrical Engineering from Virginia Tech.

Presenter Bio: Gabriele Bunkheila is a senior product manager at MathWorks, where he coordinates the strategy of MATLAB toolboxes for audio and DSP. 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.

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