Improved Zero-Shot Audio Tagging & Classification with Patchout Spectrogram Transformers. (arXiv:2208.11402v1 [cs.SD])

Standard machine learning models for tagging and classifying acoustic signals
cannot handle classes that were not seen during training. Zero-Shot (ZS)
learning overcomes this restriction by predicting classes based on adaptable
class descriptions. This study sets out to investigate the effectiveness of
self-attention-based audio embedding architectures for ZS learning. To this
end, we compare the very recent patchout spectrogram transformer with two
classic convolutional architectures. We evaluate these three architectures on
three tasks and on three different benchmark datasets: general-purpose …

arxiv audio classification spectrogram tagging transformers