A multi-device dataset for urban acoustic scene classification

TL;DR

This paper presents a new multi-device urban acoustic scene dataset for classification tasks, introduces a baseline CNN system, and evaluates its performance across diverse European city recordings.

Contribution

It introduces the TUT Urban Acoustic Scenes 2018 dataset with multi-device recordings and evaluates a baseline CNN system for urban acoustic scene classification.

Findings

Baseline system achieves competitive performance.

Dataset includes recordings from six European cities.

Multi-device recordings increase dataset variability.

Abstract

This paper introduces the acoustic scene classification task of DCASE 2018 Challenge and the TUT Urban Acoustic Scenes 2018 dataset provided for the task, and evaluates the performance of a baseline system in the task. As in previous years of the challenge, the task is defined for classification of short audio samples into one of predefined acoustic scene classes, using a supervised, closed-set classification setup. The newly recorded TUT Urban Acoustic Scenes 2018 dataset consists of ten different acoustic scenes and was recorded in six large European cities, therefore it has a higher acoustic variability than the previous datasets used for this task, and in addition to high-quality binaural recordings, it also includes data recorded with mobile devices. We also present the baseline system consisting of a convolutional neural network and its performance in the subtasks using the recommended cross-validation setup.