# Attention based Convolutional Recurrent Neural Network for Environmental Sound Classification

arXiv: https://arxiv.org/abs/1907.02230 · HTML: https://mlsanity.com/paper/1907.02230/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2019.

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

- [Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook](https://mlsanity.com/paper/2109.14737/) (2021)
- [Attentional Graph Convolutional Network for Structure-aware Audio-Visual Scene Classification](https://mlsanity.com/paper/2301.00145/) (2022)
- [TEE4EHR: Transformer Event Encoder for Better Representation Learning in Electronic Health Records](https://mlsanity.com/paper/2402.06367/) (2024)
- [BAST: Binaural Audio Spectrogram Transformer for Binaural Sound Localization](https://mlsanity.com/paper/2207.03927/) (2022)
- [Contrastive Environmental Sound Representation Learning](https://mlsanity.com/paper/2207.08825/) (2022)
- [Raw Audio Classification with Cosine Convolutional Neural Network (CosCovNN)](https://mlsanity.com/paper/2412.00312/) (2024)

## Abstract (arXiv)

Environmental sound classification (ESC) is a challenging problem due to the complexity of sounds. The ESC performance is heavily dependent on the effectiveness of representative features extracted from the environmental sounds. However, ESC often suffers from the semantically irrelevant frames and silent frames. In order to deal with this, we employ a frame-level attention model to focus on the semantically relevant frames and salient frames. Specifically, we first propose an convolutional recurrent neural network to learn spectro-temporal features and temporal correlations. Then, we extend our convolutional RNN model with a frame-level attention mechanism to learn discriminative feature representations for ESC. Experiments were conducted on ESC-50 and ESC-10 datasets. Experimental results demonstrated the effectiveness of the proposed method and achieved the state-of-the-art performance in terms of classification accuracy.