Shih-Chii Liu (UZH|ETH, Switzerland), Enea Ceolini (UZH|ETH, Switzerland)
This tutorial focusses on advances in audio machine learning algorithms that have led to new applications in smart assistants that are currently ubiquitious in devices such as smartphones, laptops, and cars. While the largest challenge of large vocabulary speech recognition can be addressed by powerful but computationally demanding models running in the cloud, many other less complex tasks can be implemented by models running only on the device and which require limited resources and is executable with low latency. This general idea of computing on the edge (fog level), opens up new challenges for ML research that now has to deal with the necessity of having small models that run fast on limited resource devices. These challenges include domains such as personalization, continual training, domain adaptation and especially privacy related issues. This tutorial will be useful for participants who are interested in learning about the ML audio algorithms suitable for embedded low power systems. They will learn about the important steps in developing, training and deploying ML audio models for various applications and the model constraints. Examples of solutions implemented especially for low resource devices and architecture considerations will be presented.
- Understand the machine learning audio algorithms suitable for embedded low power systems
- Understand the important steps in developing, training and deploying ML audio models
- Understand the model architecture choices based on the IoT platform constraints