# R-CRNN: Region-based Convolutional Recurrent Neural Network for Audio Event Detection

arXiv: https://arxiv.org/abs/1808.06627 · HTML: https://mlsanity.com/paper/1808.06627/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2018.

## Built on by (most cited first, 4 of 4)

- [Federated Self-Supervised Learning for Acoustic Event Classification](https://mlsanity.com/paper/2203.11997/) (2022)
- [Recognizing Ornaments in Vocal Indian Art Music with Active Annotation](https://mlsanity.com/paper/2505.04419/) (2025)
- [EZhouNet:A framework based on graph neural network and anchor interval for the respiratory sound event detection](https://mlsanity.com/paper/2509.01153/) (2025)
- [MAST: Label-Efficient, Robust, and Generalizable Sound Detection for Biodiversity Monitoring via Masked Audio Pretraining and Self-Training](https://mlsanity.com/paper/2609.15221/) (2026)

## Abstract (arXiv)

This paper proposes a Region-based Convolutional Recurrent Neural Network (R-CRNN) for audio event detection (AED). The proposed network is inspired by Faster-RCNN, a well known region-based convolutional network framework for visual object detection. Different from the original Faster-RCNN, a recurrent layer is added on top of the convolutional network to capture the long-term temporal context from the extracted high level features. While most of the previous works on AED generate predictions at frame level first, and then use post-processing to predict the onset/offset timestamps of events from a probability sequence; the proposed method generates predictions at event level directly and can be trained end-to-end with a multitask loss, which optimizes the classification and localization of audio events simultaneously. The proposed method is tested on DCASE 2017 Challenge dataset. To the best of our knowledge, R-CRNN is the best performing single-model method among all methods without using ensembles both on development and evaluation sets. Compared to the other region-based network for AED (R-FCN) with an event-based error rate (ER) of 0.18 on the development set, our method reduced the ER to half.