  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.
