Sound is a rich source of information about the world around us.
Modern deep learning approaches can give human-like performance on a range of sound classifiction tasks.
This makes it possible to build systems that use sound to for example:
understand speech, to analyze music, to assist in medical diagnostics, detect quality problems in manufacturing, and to study the behavior of animals.
This talk will show you how to build practical machine learning models that can classify sound.
We will convert sound into spectrograms, a visual representation of sound over time,
and apply machine learning models similar to what is used to for image classification.
The focus will be on Convolutional Neural Networks, which have been shown to work very well for this task.
The Keras and Tensorflow deep learning frameworks will be used. Some tricks for getting usable results with small amounts of data will be covered, including transfer learning, audio embeddings and data augmentation.
A basic understanding of machine learning is recommended.
Familiarity with digital sound is a bonus.