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Knee-Deep Learning

Practical Steps to Get Started with Audio ML

Dive in and start creating!

The purpose of this talk is to give a very practical introduction to audio machine learning to audio developers without prior machine learning experience, allowing them to get started quickly on making their own experiments and realizing innovative ideas.

Throughout, a simple example model architecture suitable for beginners is used.

We dive right in using simple and free tools to acquire data, set up code to create an ML training and inference pipeline, look at training techniques and analyze and evaluate the results as we go. We go through what hardware is needed for training at different scales, ranging from cloud compute to consumer GPUs.

After training some simple models, we explore different deployment options, including cloud-based inference, on-device native code using popular inference frameworks, and dedicated embedded hardware modules.