# Audio tagging with noisy labels and minimal supervision

arXiv: https://arxiv.org/abs/1906.02975 · HTML: https://mlsanity.com/paper/1906.02975/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2019.

## Built on by (most cited first, 14 of 14)

- [Conformer-Based Self-Supervised Learning for Non-Speech Audio Tasks](https://mlsanity.com/paper/2110.07313/) (2021)
- [Receptive Field Regularization Techniques for Audio Classification and Tagging with Deep Convolutional Neural Networks](https://mlsanity.com/paper/2105.12395/) (2021)
- [Inductive biases, pretraining and fine-tuning jointly account for brain responses to speech](https://mlsanity.com/paper/2103.01032/) (2021)
- [How to Estimate Model Transferability of Pre-Trained Speech Models?](https://mlsanity.com/paper/2306.01015/) (2023)
- [NoisywikiHow: A Benchmark for Learning with Real-world Noisy Labels in Natural Language Processing](https://mlsanity.com/paper/2305.10709/) (2023)
- [E-PANNs: Sound Recognition Using Efficient Pre-trained Audio Neural Networks](https://mlsanity.com/paper/2305.18665/) (2023)
- [ARCA23K: An audio dataset for investigating open-set label noise](https://mlsanity.com/paper/2109.09227/) (2021)
- [All Mistakes Are Not Equal: Comprehensive Hierarchy Aware Multi-label Predictions (CHAMP)](https://mlsanity.com/paper/2206.08653/) (2022)
- [Effective Audio Classification Network Based on Paired Inverse Pyramid Structure and Dense MLP Block](https://mlsanity.com/paper/2211.02940/) (2022)
- [Speech Enhancement for Wake-Up-Word detection in Voice Assistants](https://mlsanity.com/paper/2101.12732/) (2021)
- [Do sound event representations generalize to other audio tasks? A case study in audio transfer learning](https://mlsanity.com/paper/2106.11335/) (2021)
- [DASEE A Synthetic Database of Domestic Acoustic Scenes and Events in Dementia Patients Environment](https://mlsanity.com/paper/2104.13423/) (2021)
- [Audio Contrastive-based Fine-tuning: Decoupling Representation Learning and Classification](https://mlsanity.com/paper/2309.11895/) (2023)
- [Hierarchical Label Propagation: A Model-Size-Dependent Performance Booster for AudioSet Tagging](https://mlsanity.com/paper/2503.21826/) (2025)

## Abstract (arXiv)

This paper introduces Task 2 of the DCASE2019 Challenge, titled "Audio tagging with noisy labels and minimal supervision". This task was hosted on the Kaggle platform as "Freesound Audio Tagging 2019". The task evaluates systems for multi-label audio tagging using a large set of noisy-labeled data, and a much smaller set of manually-labeled data, under a large vocabulary setting of 80 everyday sound classes. In addition, the proposed dataset poses an acoustic mismatch problem between the noisy train set and the test set due to the fact that they come from different web audio sources. This can correspond to a realistic scenario given by the difficulty in gathering large amounts of manually labeled data. We present the task setup, the FSDKaggle2019 dataset prepared for this scientific evaluation, and a baseline system consisting of a convolutional neural network. All these resources are freely available.