论文标题
城市声音分类：努力进行公平的比较
Urban Sound Classification : striving towards a fair comparison
论文作者
论文摘要
城市声音分类已经取得了显着的进步，并且仍然是音频模式识别的积极研究领域。特别是，它允许监测噪声污染，这成为大城市日益关注的问题。本文的贡献是两个方面。首先，我们介绍了2020 Dase Task 5获奖解决方案，旨在帮助监测城市噪声污染。对于验证集的粗 /细分类，它实现了0.82 / 0.62的宏AUPRC。此外，在ESC-50和US8K数据集上，它的准确率分别达到89.7％和85.41％。其次，找到公平的比较并重现现有模型的性能并不容易。有时，作者复制原始论文的结果，这无助于可重复性。结果，我们通过使用相同的输入表示，指标和优化器来评估性能进行公平的比较。我们保留原始论文使用的数据增强。我们希望这个框架可以帮助评估该领域的新体系结构。为了获得更好的可重复性，该代码可在我们的GitHub存储库中获得。
Urban sound classification has been achieving remarkable progress and is still an active research area in audio pattern recognition. In particular, it allows to monitor the noise pollution, which becomes a growing concern for large cities. The contribution of this paper is two-fold. First, we present our DCASE 2020 task 5 winning solution which aims at helping the monitoring of urban noise pollution. It achieves a macro-AUPRC of 0.82 / 0.62 for the coarse / fine classification on validation set. Moreover, it reaches accuracies of 89.7% and 85.41% respectively on ESC-50 and US8k datasets. Second, it is not easy to find a fair comparison and to reproduce the performance of existing models. Sometimes authors copy-pasting the results of the original papers which is not helping reproducibility. As a result, we provide a fair comparison by using the same input representation, metrics and optimizer to assess performances. We preserve data augmentation used by the original papers. We hope this framework could help evaluate new architectures in this field. For better reproducibility, the code is available on our GitHub repository.
