  In this report, the Brno University of Technology (BUT) team submissions for
Task 1 (Acoustic Scene Classification, ASC) of the DCASE-2019 challenge are
described. Also, the analysis of different methods is provided. The proposed
approach is a fusion of three different Convolutional Neural Network (CNN)
topologies. The first one is a VGG like two-dimensional CNNs. The second one is
again a two-dimensional CNN network which uses Max-Feature-Map activation and
called Light-CNN (LCNN). The third network is a one-dimensional CNN which
mainly used for speaker verification and called x-vector topology. All proposed
networks use self-attention mechanism for statistic pooling. As a feature, we
use a 256-dimensional log Mel-spectrogram. Our submissions are a fusion of
several networks trained on 4-folds generated evaluation setup using different
fusion strategies.
