# Joint Generative-Contrastive Representation Learning for Anomalous Sound Detection

arXiv: https://arxiv.org/abs/2305.12111 · HTML: https://mlsanity.com/paper/2305.12111/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2023.

## Built on by (most cited first, 6 of 6)

- [Deep Generic Representations for Domain-Generalized Anomalous Sound Detection](https://mlsanity.com/paper/2409.05035/) (2024)
- [A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open Challenges](https://mlsanity.com/paper/2501.15196/) (2025)
- [Contrastive Learning with Spectrum Information Augmentation in Abnormal Sound Detection](https://mlsanity.com/paper/2509.15570/) (2025)
- [Sub-Band Spectral Matching with Localized Score Aggregation for Robust Anomalous Sound Detection](https://mlsanity.com/paper/2603.13749/) (2026)
- [Spectrogram features for audio and speech analysis](https://mlsanity.com/paper/2603.14917/) (2026)
- [MemNMF: Memory-Augmented NMF on LPC Spectra for Anomalous Sound Detection](https://mlsanity.com/paper/2607.22086/) (2026)

## Abstract (arXiv)

In this paper, we propose a joint generative and contrastive representation learning method (GeCo) for anomalous sound detection (ASD). GeCo exploits a Predictive AutoEncoder (PAE) equipped with self-attention as a generative model to perform frame-level prediction. The output of the PAE together with original normal samples, are used for supervised contrastive representative learning in a multi-task framework. Besides cross-entropy loss between classes, contrastive loss is used to separate PAE output and original samples within each class. GeCo aims to better capture context information among frames, thanks to the self-attention mechanism for PAE model. Furthermore, GeCo combines generative and contrastive learning from which we aim to yield more effective and informative representations, compared to existing methods. Extensive experiments have been conducted on the DCASE2020 Task2 development dataset, showing that GeCo outperforms state-of-the-art generative and discriminative methods.