On a time-frequency blurring operator with applications in data augmentation
<|reference_start|>On a time-frequency blurring operator with applications in data augmentation: 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.<|reference_end|>
@article{halvdansson2024on,
    title={On a time-frequency blurring operator with applications in data
  augmentation},
    author={Simon Halvdansson},
    journal={arXiv preprint arXiv:2405.12899},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.12899},
    primaryClass={math.FA cs.SD eess.AS}
}