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Found 3 papers, 2 papers with code
Leveraging Acoustic Images for Effective Self-Supervised Audio Representation Learning
However, since 2D planar arrays are cumbersome and not as widespread as ordinary microphones, we propose that the richer information content of acoustic images can be distilled, through a self-supervised learning scheme, into more powerful audio and visual feature representations.
From Biased Data to Unbiased Models: a Meta-Learning Approach
It is well known that large deep architectures are powerful models when adequately trained, but may exhibit undesirable behavior leading to confident incorrect predictions, even when evaluated on slightly different test examples.
Audio-Visual Model Distillation Using Acoustic Images
In this paper, we investigate how to learn rich and robust feature representations for audio classification from visual data and acoustic images, a novel audio data modality.
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