  In this paper, the Brno University of Technology (BUT) team submissions for
Task 1 (Acoustic Scene Classification, ASC) of the DCASE-2018 challenge are
described. Also, the analysis of different methods on the leaderboard set is
provided. The proposed approach is a fusion of two different Convolutional
Neural Network (CNN) topologies. The first one is the common two-dimensional
CNNs which is mainly used in image classification. The second one is a
one-dimensional CNN for extracting fixed-length audio segment embeddings, so
called x-vectors, which has also been used in speech processing, especially for
speaker recognition. In addition to the different topologies, two types of
features were tested: log mel-spectrogram and CQT features. Finally, the
outputs of different systems are fused using a simple output averaging in the
best performing system. Our submissions ranked third among 24 teams in the ASC
sub-task A (task1a).
