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

Convolutional Neural Networks and x-vector Embedding for DCASE2018 Acoustic Scene Classification Challenge