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Found 6 papers, 2 papers with code
Multi-Sample Dynamic Time Warping for Few-Shot Keyword Spotting
In experimental evaluations for few-shot keyword spotting, it is shown that this method yields a very similar performance as using all individual query samples as templates while having a runtime that is only slightly slower than when using Fr\'echet means.
AdaProj: Adaptively Scaled Angular Margin Subspace Projections for Anomalous Sound Detection with Auxiliary Classification Tasks
The state-of-the-art approach for semi-supervised anomalous sound detection is to first learn an embedding space by using auxiliary classification tasks based on meta information or self-supervised learning and then estimate the distribution of normal data.
Projected Belief Networks With Discriminative Alignment for Acoustic Event Classification: Rivaling State of the Art CNNs
The PBN is two networks in one, a FFNN that operates in the forward direction, and a generative network that operates in the backward direction.
Self-Supervised Learning for Anomalous Sound Detection
However, the less difficult the classification task becomes, the less informative are the embeddings and the worse is the resulting ASD performance.
Why do Angular Margin Losses work well for Semi-Supervised Anomalous Sound Detection?
Furthermore, multiple experiments are conducted to show that using a related classification task as an auxiliary task teaches the model to learn representations suitable for detecting anomalous sounds in noisy conditions.
TACos: Learning Temporally Structured Embeddings for Few-Shot Keyword Spotting with Dynamic Time Warping
To segment a signal into blocks to be analyzed, few-shot keyword spotting (KWS) systems often utilize a sliding window of fixed size.
