# Unsupervised Contrastive Learning of Sound Event Representations

arXiv: https://arxiv.org/abs/2011.07616 · HTML: https://mlsanity.com/paper/2011.07616/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2020.

## Built on by (most cited first, 30 of 32)

- [BEATs: Audio Pre-Training with Acoustic Tokenizers](https://mlsanity.com/paper/2212.09058/) (2022)
- [BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation](https://mlsanity.com/paper/2103.06695/) (2021)
- [HEAR: Holistic Evaluation of Audio Representations](https://mlsanity.com/paper/2203.03022/) (2022)
- [Audio Self-supervised Learning: A Survey](https://mlsanity.com/paper/2203.01205/) (2022)
- [Contrastive Audio-Language Learning for Music](https://mlsanity.com/paper/2208.12208/) (2022)
- [Self-supervised Audio Teacher-Student Transformer for Both Clip-level and Frame-level Tasks](https://mlsanity.com/paper/2306.04186/) (2023)
- [Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning](https://mlsanity.com/paper/2210.16192/) (2022)
- [Holistic Representation Learning for Multitask Trajectory Anomaly Detection](https://mlsanity.com/paper/2311.01851/) (2023)
- [ATST: Audio Representation Learning with Teacher-Student Transformer](https://mlsanity.com/paper/2204.12076/) (2022)
- [Masked Autoencoders with Multi-Window Local-Global Attention Are Better Audio Learners](https://mlsanity.com/paper/2306.00561/) (2023)
- [Self-Supervised Learning for Few-Shot Bird Sound Classification](https://mlsanity.com/paper/2312.15824/) (2023)
- [SpliceOut: A Simple and Efficient Audio Augmentation Method](https://mlsanity.com/paper/2110.00046/) (2021)
- [Regularized Contrastive Pre-training for Few-shot Bioacoustic Sound Detection](https://mlsanity.com/paper/2309.08971/) (2023)
- [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)
- [Audio Barlow Twins: Self-Supervised Audio Representation Learning](https://mlsanity.com/paper/2209.14345/) (2022)
- [Representing Spatial Trajectories as Distributions](https://mlsanity.com/paper/2210.01322/) (2022)
- [On the Effect of Data-Augmentation on Local Embedding Properties in the Contrastive Learning of Music Audio Representations](https://mlsanity.com/paper/2401.08889/) (2024)
- [Looking Similar, Sounding Different: Leveraging Counterfactual Cross-Modal Pairs for Audiovisual Representation Learning](https://mlsanity.com/paper/2304.05600/) (2023)
- [MT-SLVR: Multi-Task Self-Supervised Learning for Transformation In(Variant) Representations](https://mlsanity.com/paper/2305.17191/) (2023)
- [Prototype based Masked Audio Model for Self-Supervised Learning of Sound Event Detection](https://mlsanity.com/paper/2409.17656/) (2024)
- [Discovering COVID-19 Coughing and Breathing Patterns from Unlabeled Data Using Contrastive Learning with Varying Pre-Training Domains](https://mlsanity.com/paper/2306.01864/) (2023)
- [Pretraining Representations for Bioacoustic Few-shot Detection using Supervised Contrastive Learning](https://mlsanity.com/paper/2309.00878/) (2023)
- [Optimizing Audio Augmentations for Contrastive Learning of Health-Related Acoustic Signals](https://mlsanity.com/paper/2309.05843/) (2023)
- [Exploring Self-Supervised Contrastive Learning of Spatial Sound Event Representation](https://mlsanity.com/paper/2309.15938/) (2023)
- [The Computation of Generalized Embeddings for Underwater Acoustic Target Recognition using Contrastive Learning](https://mlsanity.com/paper/2505.12904/) (2025)
- [Augmented Contrastive Self-Supervised Learning for Audio Invariant Representations](https://mlsanity.com/paper/2112.10950/) (2021)
- [Learning neural audio features without supervision](https://mlsanity.com/paper/2203.15519/) (2022)
- [Contrastive Learning from Synthetic Audio Doppelg"angers](https://mlsanity.com/paper/2406.05923/) (2024)
- [Proceedings of the First Workshop on Weakly Supervised Learning (WeaSuL)](https://mlsanity.com/paper/2107.03690/) (2021)

## Abstract (arXiv)

Self-supervised representation learning can mitigate the limitations in recognition tasks with few manually labeled data but abundant unlabeled data---a common scenario in sound event research. In this work, we explore unsupervised contrastive learning as a way to learn sound event representations. To this end, we propose to use the pretext task of contrasting differently augmented views of sound events. The views are computed primarily via mixing of training examples with unrelated backgrounds, followed by other data augmentations. We analyze the main components of our method via ablation experiments. We evaluate the learned representations using linear evaluation, and in two in-domain downstream sound event classification tasks, namely, using limited manually labeled data, and using noisy labeled data. Our results suggest that unsupervised contrastive pre-training can mitigate the impact of data scarcity and increase robustness against noisy labels, outperforming supervised baselines.