Unsupervised Learning of Audio Perception for Robotics Applications: Learning to Project Data to T-SNE/UMAP space

Understand

Audio perception is a key to solving a variety of problems ranging from acoustic scene analysis, music meta-data extraction, recommendation, synthesis and analysis.

• It can potentially also augment computers in doing tasks that humans do effortlessly in day-to-day activities.
• This paper builds upon key ideas to build perception of touch sounds without access to any ground-truth data.
• We show how we can leverage ideas from classical signal processing to get large amounts of data of any sound of interest with a high precision.