Data Simulation for AI
Developing AI models for any task often relies on the availability of strongly-labeled audio data. Recording such data manually is very time consuming and expensive, and as a result existing datasets for specific tasks are scarce and limited in size. To alleviate this problem, machine learning models rely on data simulation to “augment” the amount of data available. The augmentation process consists in applying transformations to the original data to simulate a recording in another environment and obtain a new set of recordings.