# Convolutional Neural Networks and x-vector Embedding for DCASE2018 Acoustic Scene Classification Challenge Source: [Convolutional Neural Networks and x-vector Embedding for DCASE2018 Acoustic Scene Classification Challenge]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:13:58.604Z Authors: Hossein Zeinali, Lukas Burget, Jan Cernocky Published: 2018-10-01T18:45:12.000Z ## Abstract 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). ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()