Multi-Temporal Resolution Convolutional Neural Networks for Acoustic Scene Classification
Abstract
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.
Cite
@article{arxiv.1811.04419,
title = {Multi-Temporal Resolution Convolutional Neural Networks for Acoustic Scene Classification},
author = {Alexander Schindler and Thomas Lidy and Andreas Rauber},
journal= {arXiv preprint arXiv:1811.04419},
year = {2018}
}
Comments
In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), November 2017