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

arXiv: https://arxiv.org/abs/1810.04273 · HTML: https://mlsanity.com/paper/1810.04273/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2018.

## Built on by (most cited first, 3 of 3)

- [Robust, General, and Low Complexity Acoustic Scene Classification Systems and An Effective Visualization for Presenting a Sound Scene Context](https://mlsanity.com/paper/2210.08610/) (2022)
- [Domain-Dependent Speaker Diarization for the Third DIHARD Challenge](https://mlsanity.com/paper/2101.09884/) (2021)
- [VANPY: Voice Analysis Framework](https://mlsanity.com/paper/2502.17579/) (2025)

## Abstract (arXiv)

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).