This paper describes Task 2 of the DCASE 2018 Challenge, titled
"General-purpose audio tagging of Freesound content with AudioSet labels". This
task was hosted on the Kaggle platform as "Freesound General-Purpose Audio
Tagging Challenge". The goal of the task is to build an audio tagging system
that can recognize the category of an audio clip from a subset of 41 diverse
categories drawn from the AudioSet Ontology. We present the task, the dataset
prepared for the competition, and a baseline system.

本文介绍了 DCASE 2018 挑战赛的任务 2，即使用 AudioSet 标签对 Freesound 内容进行通用音频标记。本人们在 Kaggle 平台上托管了此任务， 目标是建立能够识别来自 41 个不同类别的音频剪辑的类别的音频标记系统。我们介绍了任务、为比赛准备的数据集和基准系统。

使用 AudioSet 标签对 Freesound 音频进行通用标记：任务描述，数据集和基线

General-purpose Tagging of Freesound Audio with AudioSet Labels: Task  Description, Dataset, and Baseline

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.

本文介绍了 DCASE 2018 挑战赛的声景分类任务和提供给该任务的 TUT Urban Acoustic Scenes 2018 数据集， 并评估了基线系统在该任务中的性能。同时，本文还提出了基线系统，该系统包括卷积神经网络， 并介绍了其在子任务中的性能。