Papers › MetaAudio: A Few-Shot Audio Classification Benchmark
MetaAudio: A Few-Shot Audio Classification Benchmark
Calum Heggan, Sam Budgett, Timothy Hospedales, Mehrdad Yaghoobi
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.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2204.02121")
Code
Syntology Ran 1 of 1 code samples harvested from 1 repository linked to this paper; 0 have no recorded run. Of those that ran: 1 ran · our draft was wrong.
By repository: official repository: 1 sample from 1 repository, 1 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
1 sample harvested; 1 ran; 0 honoured the contract we drafted; 0 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
Licence: 1 of the 1 sample is pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
Harvested from cheggan/metaaudio-a-few-shot-audio-classification-benchmark. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.
Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.
Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.
c49351fe44ccebfd · report
Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Few-Shot Audio Classification | BirdClef 2020 (Pruned) | Meta-Curvature (CRNN) | Top-1 Accuracy(5-Way-1-Shot) | 61.34 +- 0.46 | #1 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | BirdClef 2020 (Pruned) | SimpleShot Cl2N (CRNN) | Top-1 Accuracy(5-Way-1-Shot) | 57.66 +- 0.43 | #2 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | BirdClef 2020 (Pruned) | Meta-Baseline (CRNN) | Top-1 Accuracy(5-Way-1-Shot) | 57.28 +- 0.41 | #3 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | BirdClef 2020 (Pruned) | MAML (CRNN) | Top-1 Accuracy(5-Way-1-Shot) | 56.26 +- 0.45 | #4 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | BirdClef 2020 (Pruned) | Prototypical Networks (CRNN) | Top-1 Accuracy(5-Way-1-Shot) | 56.11 +- 0.46 | #5 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | BirdClef 2020 (Pruned) | SimpleShot CL2N (AST ImageNet & AudioSet- No fine-tune) | Top-1 Accuracy(5-Way-1-Shot) | 36.41 +- 0.42 | #6 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | BirdClef 2020 (Pruned) | SimpleShot CL2N (AST ImageNet - No fine-tune) | Top-1 Accuracy(5-Way-1-Shot) | 33.04 +- 0.41 | #7 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | ESC-50 | Meta-Curvature (CRNN) | Top-1 Accuracy(5-Way-1-Shot) | 76.17 +- 0.41 | #1 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | ESC-50 | MAML (CRNN) | Top-1 Accuracy(5-Way-1-Shot) | 74.66 ± 0.42 | #2 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | ESC-50 | Meta-Baseline (CRNN) | Top-1 Accuracy(5-Way-1-Shot) | 71.72 +- 0.38 | #3 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | ESC-50 | Prototypical Networks (CRNN) | Top-1 Accuracy(5-Way-1-Shot) | 68.83 +- 0.38 | #5 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | ESC-50 | SimpleShot CL2N (CRNN) | Top-1 Accuracy(5-Way-1-Shot) | 68.82 +-0.39 | #6 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | ESC-50 | SimpleShot CL2N (AST ImageNet & AudioSet- No fine-tune) | Top-1 Accuracy(5-Way-1-Shot) | 64.48 +- 0.41 | #7 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | ESC-50 | SimpleShot CL2N (AST ImageNet - No fine-tune) | Top-1 Accuracy(5-Way-1-Shot) | 60.41 +- 0.41 | #9 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | FSDKaggle2018 | MAML (CRNN) | Top-1 Accuracy(5-Way-1-Shot) | 43.45 +- 0.46 | #1 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | FSDKaggle2018 | Meta-Curvature (CRNN) | Top-1 Accuracy(5-Way-1-Shot) | 43.18 +- 0.45 | #2 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | FSDKaggle2018 | SimpleShot CL2N (CRNN) | Top-1 Accuracy(5-Way-1-Shot) | 42.05 +- 0.42 | #3 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | FSDKaggle2018 | Meta-Baseline (CRNN) | Top-1 Accuracy(5-Way-1-Shot) | 40.27 +- 0.44 | #4 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | FSDKaggle2018 | Prototypical Networks (CRNN) | Top-1 Accuracy(5-Way-1-Shot) | 39.44 +- 0.44 | #5 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | FSDKaggle2018 | SimpleShot CL2N (AST ImageNet & AudioSet- No fine-tune) | Top-1 Accuracy(5-Way-1-Shot) | 38.78 +- 0.41 | #7 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | FSDKaggle2018 | SimpleShot CL2N (AST ImageNet - No fine-tune) | Top-1 Accuracy(5-Way-1-Shot) | 33.52 +- 0.39 | #9 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | NSynth | Meta-Curvature (CRNN) | Top-1 Accuracy(5-Way-1-Shot) | 96.47 +-0.19 | #1 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | NSynth | Prototypical Networks (CRNN) | Top-1 Accuracy(5-Way-1-Shot) | 95.23 +- 0.19 | #2 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | NSynth | MAML (CRNN) | Top-1 Accuracy(5-Way-1-Shot) | 93.85 +- 0.24 | #3 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | NSynth | Meta-Baseline (CRNN) | Top-1 Accuracy(5-Way-1-Shot) | 90.74 +- 0.25 | #4 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | NSynth | SimpleShot CL2N (CRNN) | Top-1 Accuracy(5-Way-1-Shot) | 90.04 +- 0.27 | #5 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | NSynth | SimpleShot CL2N Classifier (AST pre-trained w/ ImageNet - No fine-tune) | Top-1 Accuracy(5-Way-1-Shot) | 66.68 +- 0.41 | #7 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | NSynth | SimpleShot CL2N Classifier (AST ImageNet & AudioSet - No fine-tune) | Top-1 Accuracy(5-Way-1-Shot) | 63.78 +- 0.42 | #9 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | VoxCeleb1 | Meta-Curvature (CRNN) | Top-1 Accuracy(5-Way-1-Shot) | 63.85 +- 0.44 | #1 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | VoxCeleb1 | MAML (CRNN) | Top-1 Accuracy(5-Way-1-Shot) | 60.89 +- 0.45 | #2 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | VoxCeleb1 | Prototypical Networks (CRNN) | Top-1 Accuracy(5-Way-1-Shot) | 59.64 +- 0.44 | #3 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | VoxCeleb1 | Meta-Baseline (CRNN) | Top-1 Accuracy(5-Way-1-Shot) | 55.54 +- 0.42 | #4 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | VoxCeleb1 | SimpleShot CL2N (CRNN) | Top-1 Accuracy(5-Way-1-Shot) | 48.50 +- 0.42 | #5 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | VoxCeleb1 | SimpleShot CL2N (AST ImageNet & AudioSet- No fine-tune) | Top-1 Accuracy(5-Way-1-Shot) | 28.79 +- 0.38 | #8 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | VoxCeleb1 | SimpleShot CL2N (AST ImageNet - No fine-tune) | Top-1 Accuracy(5-Way-1-Shot) | 28.09 +- 0.37 | #9 of 10 | Archive leaderboard | report |
| Few-Shot Audio Classification | Watkins Marine Mammal Sounds | SimpleShot CL2N (AST ImageNet - No fine-tune) | Top-1 Accuracy(5-Way-1-Shot) | 55.40 ± 0.42 | #2 of 5 | Archive leaderboard | report |
| Few-Shot Audio Classification | Watkins Marine Mammal Sounds | SimpleShot CL2N (AST ImageNet & AudioSet- No fine-tune) | Top-1 Accuracy(5-Way-1-Shot) | 51.81 ± 0.42 | #4 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections