4 research outputs found
MT-SLVR: Multi-Task Self-Supervised Learning for Transformation In(Variant) Representations
Contrastive self-supervised learning has gained attention for its ability to
create high-quality representations from large unlabelled data sets. A key
reason that these powerful features enable data-efficient learning of
downstream tasks is that they provide augmentation invariance, which is often a
useful inductive bias. However, the amount and type of invariances preferred is
not known apriori, and varies across different downstream tasks. We therefore
propose a multi-task self-supervised framework (MT-SLVR) that learns both
variant and invariant features in a parameter-efficient manner. Our multi-task
representation provides a strong and flexible feature that benefits diverse
downstream tasks. We evaluate our approach on few-shot classification tasks
drawn from a variety of audio domains and demonstrate improved classification
performance on all of themComment: Last author version accepted to InterSpeech23. 5 page
MetaAudio: A Few-Shot Audio Classification Benchmark
Currently available benchmarks for few-shot learning (machine learning with
few training examples) are limited in the domains they cover, primarily
focusing on image classification. This work aims to alleviate this reliance on
image-based benchmarks by offering the first comprehensive, public and fully
reproducible audio based alternative, covering a variety of sound domains and
experimental settings. We compare the few-shot classification performance of a
variety of techniques on seven audio datasets (spanning environmental sounds to
human-speech). Extending this, we carry out in-depth analyses of joint training
(where all datasets are used during training) and cross-dataset adaptation
protocols, establishing the possibility of a generalised audio few-shot
classification algorithm. Our experimentation shows gradient-based
meta-learning methods such as MAML and Meta-Curvature consistently outperform
both metric and baseline methods. We also demonstrate that the joint training
routine helps overall generalisation for the environmental sound databases
included, as well as being a somewhat-effective method of tackling the
cross-dataset/domain setting.Comment: 9 pages with 1 figure and 2 main results tables. V1 Preprin