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

Acoustic Scene Classification Using Fusion of Attentive Convolutional Neural Networks for DCASE2019 Challenge