# ARCA23K: An audio dataset for investigating open-set label noise

arXiv: https://arxiv.org/abs/2109.09227 · HTML: https://mlsanity.com/paper/2109.09227/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 19 Sep 2021.

## Builds on (most cited first, 11 of 11)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach](https://mlsanity.com/paper/1609.03683/) (2016)
- [VGGSound: A Large-scale Audio-Visual Dataset](https://mlsanity.com/paper/2004.14368/) (2020)
- [FSD50K: An Open Dataset of Human-Labeled Sound Events](https://mlsanity.com/paper/2010.00475/) (2020)
- [General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline](https://mlsanity.com/paper/1807.09902/) (2018)
- [Learning Sound Event Classifiers from Web Audio with Noisy Labels](https://mlsanity.com/paper/1901.01189/) (2019)
- [Audio tagging with noisy labels and minimal supervision](https://mlsanity.com/paper/1906.02975/) (2019)
- [Learning with Out-of-Distribution Data for Audio Classification](https://mlsanity.com/paper/2002.04683/) (2020)
- [Audio Tagging by Cross Filtering Noisy Labels](https://mlsanity.com/paper/2007.08165/) (2020)
- [Learning Sound Events From Webly Labeled Data](https://mlsanity.com/paper/1811.09967/) (2018)

## Built on by (most cited first, 3 of 3)

- [BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics](https://mlsanity.com/paper/2403.10380/) (2024)
- [From Sounds to Scenes: A Benchmark for Evaluating Context-Aware Auditory Scene Understanding in Large Audio Language Models](https://mlsanity.com/paper/2606.25391/) (2026)
- [A Benchmark of Generative Methods for Zero-Shot Environmental Sound Classification](https://mlsanity.com/paper/2412.03771/) (2024)

## Abstract (arXiv)

The availability of audio data on sound sharing platforms such as Freesound gives users access to large amounts of annotated audio. Utilising such data for training is becoming increasingly popular, but the problem of label noise that is often prevalent in such datasets requires further investigation. This paper introduces ARCA23K, an Automatically Retrieved and Curated Audio dataset comprised of over 23000 labelled Freesound clips. Unlike past datasets such as FSDKaggle2018 and FSDnoisy18K, ARCA23K facilitates the study of label noise in a more controlled manner. We describe the entire process of creating the dataset such that it is fully reproducible, meaning researchers can extend our work with little effort. We show that the majority of labelling errors in ARCA23K are due to out-of-vocabulary audio clips, and we refer to this type of label noise as open-set label noise. Experiments are carried out in which we study the impact of label noise in terms of classification performance and representation learning.