# Multi-Temporal Resolution Convolutional Neural Networks for Acoustic Scene Classification

arXiv: https://arxiv.org/abs/1811.04419 · HTML: https://mlsanity.com/paper/1811.04419/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2018.

## Built on by (most cited first, 1 of 1)

- [Investigating Multi-Feature Selection and Ensembling for Audio Classification](https://mlsanity.com/paper/2206.07511/) (2022)

## Abstract (arXiv)

In this paper we present a Deep Neural Network architecture for the task of acoustic scene classification which harnesses information from increasing temporal resolutions of Mel-Spectrogram segments. This architecture is composed of separated parallel Convolutional Neural Networks which learn spectral and temporal representations for each input resolution. The resolutions are chosen to cover fine-grained characteristics of a scene's spectral texture as well as its distribution of acoustic events. The proposed model shows a 3.56% absolute improvement of the best performing single resolution model and 12.49% of the DCASE 2017 Acoustic Scenes Classification task baseline.