# Sound event detection via dilated convolutional recurrent neural networks

arXiv: https://arxiv.org/abs/1911.10888 · HTML: https://mlsanity.com/paper/1911.10888/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2019.

## Built on by (most cited first, 8 of 8)

- [Sound Event Detection Transformer: An Event-based End-to-End Model for Sound Event Detection](https://mlsanity.com/paper/2110.02011/) (2021)
- [DiffSED: Sound Event Detection with Denoising Diffusion](https://mlsanity.com/paper/2308.07293/) (2023)
- [Hybrid Data Augmentation and Deep Attention-based Dilated Convolutional-Recurrent Neural Networks for Speech Emotion Recognition](https://mlsanity.com/paper/2109.09026/) (2021)
- [HiSSNet: Sound Event Detection and Speaker Identification via Hierarchical Prototypical Networks for Low-Resource Headphones](https://mlsanity.com/paper/2303.07538/) (2023)
- [Exploring Performance-Complexity Trade-Offs in Sound Event Detection Models](https://mlsanity.com/paper/2503.11373/) (2025)
- [Towards Open World Sound Event Detection](https://mlsanity.com/paper/2605.03934/) (2026)
- [LightAVSeg: Lightweight Audio-Visual Segmentation](https://mlsanity.com/paper/2605.08805/) (2026)
- [Few-shot Class-variable Incremental Audio Classification via Prototype Adaptation and Pseudo Class-variable Training](https://mlsanity.com/paper/2606.08898/) (2026)

## Abstract (arXiv)

Convolutional recurrent neural networks (CRNNs) have achieved state-of-the-art performance for sound event detection (SED). In this paper, we propose to use a dilated CRNN, namely a CRNN with a dilated convolutional kernel, as the classifier for the task of SED. We investigate the effectiveness of dilation operations which provide a CRNN with expanded receptive fields to capture long temporal context without increasing the amount of CRNN's parameters. Compared to the classifier of the baseline CRNN, the classifier of the dilated CRNN obtains a maximum increase of 1.9%, 6.3% and 2.5% at F1 score and a maximum decrease of 1.7%, 4.1% and 3.9% at error rate (ER), on the publicly available audio corpora of the TUT-SED Synthetic 2016, the TUT Sound Event 2016 and the TUT Sound Event 2017, respectively.