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Introduction to Deep Learning for Audio and Speech Applications

Highlights

  • Acquiring, segmenting and labeling audio recordings and ingesting existing datasets
  • Extracting standard speech and audio features and using 2D time-frequency representations
  • Designing and analyzing deep networks and exchanging models with other popular frameworks (e.g. via ONNX)
  • Accelerating computations using GPUs and prototyping trained models on real-world signals

About the Presenter

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: 22 Oct 2019