Browse State-of-the-Art › Few-Shot Audio Classification
Few-Shot Audio Classification
5 papers with code · 10 benchmarks · 9 datasets archive 2025-07-28
Few-shot classification for audio signals. Presents a unique challenge compared to other few-shot domains as we deal with temporal dependencies as well.
Like other few-shot problems, few-shot audio classification can be tackled in a variety of ways, from using supervised meta-learning on the same primary dataset, to pre-training on an external dataset and utilising linear readout. For this reason, results in each dataset leaderboard should be correctly tagged e.g. with "Within Dataset Meta-Learning" etc
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
10 leaderboard tables shown for this task, 10 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.
Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
9 datasets whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
No subtask under this task in the archive's task tree.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
5 shown of 5 papers with code (8 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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30 Nov 2023 2 repositories listedIn this work, we introduce Acoustic Prompt Tuning (APT), a new adapter extending LLMs and VLMs to the audio domain by injecting audio embeddings to the input of LLMs, namely soft prompting.
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4 Oct 2024 1 repository listedThe Prototypical Network (ProtoNet) has emerged as a popular choice in Few-shot Learning (FSL) scenarios due to its remarkable performance and straightforward implementation.
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2 Feb 2024 1 repository listedIn recent years, self-supervised learning has excelled for its capacity to learn robust feature representations from unlabelled data.
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29 May 2023 1 repository listedContrastive self-supervised learning has gained attention for its ability to create high-quality representations from large unlabelled data sets.
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5 Apr 2022 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)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.
Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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