# Unsupervised Discriminative Learning of Sounds for Audio Event Classification

arXiv: https://arxiv.org/abs/2105.09279 · HTML: https://mlsanity.com/paper/2105.09279/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 21 references that are in the map.
- First posted on arXiv 19 May 2021.

## Builds on (most cited first, 14 of 14)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [Look, Listen and Learn](https://mlsanity.com/paper/1705.08168/) (2017)
- [Deep Speech: Scaling up end-to-end speech recognition](https://mlsanity.com/paper/1412.5567/) (2014)
- [Objects that Sound](https://mlsanity.com/paper/1712.06651/) (2017)
- [YFCC100M: The New Data in Multimedia Research](https://mlsanity.com/paper/1503.01817/) (2015)
- [Improving Generalization via Scalable Neighborhood Component Analysis](https://mlsanity.com/paper/1808.04699/) (2018)
- [Telling Left from Right: Learning Spatial Correspondence of Sight and Sound](https://mlsanity.com/paper/2006.06175/) (2020)
- [Audio Word2Vec: Unsupervised Learning of Audio Segment Representations using Sequence-to-sequence Autoencoder](https://mlsanity.com/paper/1603.00982/) (2016)
- [ESResNet: Environmental Sound Classification Based on Visual Domain Models](https://mlsanity.com/paper/2004.07301/) (2020)
- [A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection](https://mlsanity.com/paper/2006.01919/) (2020)
- [Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging](https://mlsanity.com/paper/1607.03681/) (2016)
- [Unsupervised Feature Learning for Audio Analysis](https://mlsanity.com/paper/1712.03835/) (2017)

## Abstract (arXiv)

Recent progress in network-based audio event classification has shown the benefit of pre-training models on visual data such as ImageNet. While this process allows knowledge transfer across different domains, training a model on large-scale visual datasets is time consuming. On several audio event classification benchmarks, we show a fast and effective alternative that pre-trains the model unsupervised, only on audio data and yet delivers on-par performance with ImageNet pre-training. Furthermore, we show that our discriminative audio learning can be used to transfer knowledge across audio datasets and optionally include ImageNet pre-training.