  Inspired by the success of recent data augmentation methods for signals which
act on time-frequency representations, we introduce an operator which convolves
the short-time Fourier transform of a signal with a specified kernel.
Analytical properties including boundedness, compactness and positivity are
investigated from the perspective of time-frequency analysis. A convolutional
neural network and a vision transformer are trained to classify audio signals
using spectrograms with different augmentation setups, including the above
mentioned time-frequency blurring operator, with results indicating that the
operator can significantly improve test performance, especially in the
data-starved regime.
