# Learning Sound Event Classifiers from Web Audio with Noisy Labels

arXiv: https://arxiv.org/abs/1901.01189 · HTML: https://mlsanity.com/paper/1901.01189/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2019.

## Built on by (most cited first, 22 of 22)

- [USB: A Unified Semi-supervised Learning Benchmark for Classification](https://mlsanity.com/paper/2208.07204/) (2022)
- [Universal Speech Enhancement with Score-based Diffusion](https://mlsanity.com/paper/2206.03065/) (2022)
- [CrisperWhisper: Accurate Timestamps on Verbatim Speech Transcriptions](https://mlsanity.com/paper/2408.16589/) (2024)
- [iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection](https://mlsanity.com/paper/2201.02331/) (2022)
- [Seeing Sound, Hearing Sight: Uncovering Modality Bias and Conflict of AI models in Sound Localization](https://mlsanity.com/paper/2505.11217/) (2025)
- [SemiReward: A General Reward Model for Semi-supervised Learning](https://mlsanity.com/paper/2310.03013/) (2023)
- [Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noise](https://mlsanity.com/paper/2105.00349/) (2021)
- [NoisywikiHow: A Benchmark for Learning with Real-world Noisy Labels in Natural Language Processing](https://mlsanity.com/paper/2305.10709/) (2023)
- [Project Achoo: A Practical Model and Application for COVID-19 Detection from Recordings of Breath, Voice, and Cough](https://mlsanity.com/paper/2107.10716/) (2021)
- [SpliceOut: A Simple and Efficient Audio Augmentation Method](https://mlsanity.com/paper/2110.00046/) (2021)
- [Self-Supervised Learning from Automatically Separated Sound Scenes](https://mlsanity.com/paper/2105.02132/) (2021)
- [Improving Sound Event Classification by Increasing Shift Invariance in Convolutional Neural Networks](https://mlsanity.com/paper/2107.00623/) (2021)
- [Stepping Forward on the Last Mile](https://mlsanity.com/paper/2411.04036/) (2024)
- [Do We Really Need Gold Samples for Sample Weighting Under Label Noise?](https://mlsanity.com/paper/2104.09045/) (2021)
- [ARCA23K: An audio dataset for investigating open-set label noise](https://mlsanity.com/paper/2109.09227/) (2021)
- [Towards Robust Image-in-Audio Deep Steganography](https://mlsanity.com/paper/2303.05007/) (2023)
- [Exploring Self-Supervised Contrastive Learning of Spatial Sound Event Representation](https://mlsanity.com/paper/2309.15938/) (2023)
- [Few-shot Long-Tailed Bird Audio Recognition](https://mlsanity.com/paper/2206.11260/) (2022)
- [Whilter: A Whisper-based Data Filter for "In-the-Wild" Speech Corpora Using Utterance-level Multi-Task Classification](https://mlsanity.com/paper/2507.21642/) (2025)
- [Detecting and measuring respiratory events in horses during exercise with a microphone: deep learning vs. standard signal processing](https://mlsanity.com/paper/2508.02349/) (2025)
- [Representation-Based Data Quality Audits for Audio](https://mlsanity.com/paper/2509.26291/) (2025)
- [Content is What Remains: Invariant Speech Tokenization from Parallel Utterances](https://mlsanity.com/paper/2607.19033/) (2026)

## Abstract (arXiv)

As sound event classification moves towards larger datasets, issues of label noise become inevitable. Web sites can supply large volumes of user-contributed audio and metadata, but inferring labels from this metadata introduces errors due to unreliable inputs, and limitations in the mapping. There is, however, little research into the impact of these errors. To foster the investigation of label noise in sound event classification we present FSDnoisy18k, a dataset containing 42.5 hours of audio across 20 sound classes, including a small amount of manually-labeled data and a larger quantity of real-world noisy data. We characterize the label noise empirically, and provide a CNN baseline system. Experiments suggest that training with large amounts of noisy data can outperform training with smaller amounts of carefully-labeled data. We also show that noise-robust loss functions can be effective in improving performance in presence of corrupted labels.