# Multi-stream Network With Temporal Attention For Environmental Sound Classification

arXiv: https://arxiv.org/abs/1901.08608 · HTML: https://mlsanity.com/paper/1901.08608/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2019.

## Built on by (most cited first, 7 of 7)

- [Slow-Fast Auditory Streams For Audio Recognition](https://mlsanity.com/paper/2103.03516/) (2021)
- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [Investigating Multi-Feature Selection and Ensembling for Audio Classification](https://mlsanity.com/paper/2206.07511/) (2022)
- [Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices](https://mlsanity.com/paper/2103.03483/) (2021)
- [Attentional Graph Convolutional Network for Structure-aware Audio-Visual Scene Classification](https://mlsanity.com/paper/2301.00145/) (2022)
- [AudRandAug: Random Image Augmentations for Audio Classification](https://mlsanity.com/paper/2309.04762/) (2023)
- [Joint framework with deep feature distillation and adaptive focal loss for weakly supervised audio tagging and acoustic event detection](https://mlsanity.com/paper/2103.12388/) (2021)

## Abstract (arXiv)

Environmental sound classification systems often do not perform robustly across different sound classification tasks and audio signals of varying temporal structures. We introduce a multi-stream convolutional neural network with temporal attention that addresses these problems. The network relies on three input streams consisting of raw audio and spectral features and utilizes a temporal attention function computed from energy changes over time. Training and classification utilizes decision fusion and data augmentation techniques that incorporate uncertainty. We evaluate this network on three commonly used data sets for environmental sound and audio scene classification and achieve new state-of-the-art performance without any changes in network architecture or front-end preprocessing, thus demonstrating better generalizability.