Today we will highlight signal processing applications using deep learning techniques. Deep Learning developed and evolved for image processing and computer vision applications, but it is now increasingly and successfully used on signal and time series data.
Deep learning for signal data typically requires preprocessing, transformation, and feature extraction steps that image processing applications often do not. While large high-quality image datasets can be created with relatively low-cost cameras, signal sets are harder to obtain and will usually suffer from large variability caused by wideband noise, interference, non-linear trends, jitter, phase distortion, and missing samples.
