Multi-label Few-shot Learning for Sound Event Recognition

By Kai-Hsiang Cheng, Szu-Yu Chou, Yi-Hsuan Yang. 2019

30 citations recorded by the source corpus.

Few-shot classification aims to generalize the concept from seen classes to unseen novel classes using only a few examples. Although significant progress in few-shot classification has been made, most approaches focus on a standard multi-class scenario and are based on learning single-label embedding of the labeled examples to classify the unlabeled examples. Besides, we note that state-of-the-art methods in few-shot learning mostly adopt a metric-based architecture and the the so-called episode training strategy. While this approach works nicely for multiclass classification, it is hard to apply it to the multi-label scenario because of the complexity of forming an episode. In this paper, we propose a One-vs.-Rest episode selection strategy to mitigate this issue and apply the strategy to the multi-label few-shot problem. Experiments conducted using the large-scale data found in the AudioSet show that the models with our training strategy extract the semantic features under the multi-label setting.

Subfields: Civil and Structural Engineering, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

In Signal Processing

Citations in the corpus

FSD50K: An Open Dataset of Human-Labeled Sound Events (2020, background): “While they may not be adequate for deep learning approaches, they can be useful for other practices, e.g., few shot learning [75].”
Unsupervised Contrastive Learning of Sound Event Representations (2020, background): “Alternatives to conventional supervised learning include the paradigms of semi-supervised learning [3], few-shot learning [4], learning from noisy labels [5, 6], or self-supervision [7].”