Multi-Temporal Resolution Convolutional Neural Networks for Acoustic Scene Classification [article]

Alexander Schindler, Thomas Lidy, Andreas Rauber
2018 arXiv   pre-print
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
more » ... . 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.
arXiv:1811.04419v1 fatcat:dyaxepbdavduna6bdnf2rxoywa