Model-centric approaches to solve AI problems have been dominant in applications where large and high-quality datasets are available. Such approaches aim to improve model performance through the development of more complex architectures. 

In signal processing applications, where data is usually scarce and noisy and where advanced models and architecture experts are hard to find, a potentially more fruitful approach is a data-centric one that focuses on improving the data to make simpler network architectures perform better. The idea is to enhance signal data by improving its labels, removing noise, and reducing variance and dimensionality. This idea can be extended to include transforming signals into domains where key features become more prominent and easier to distinguish.

In this talk I go over various examples that follow a data-centric approach to improve AI-model performance. I show how signal processing techniques like time-frequency transformations, filtering, denoising, multiresolution analysis, data synthesis, and data augmentation can be used to obtain state-of-the-art results with simpler AI architectures in the solution of signal classification, signal regression, and anomaly detection problems.