Abstract
Acoustic Scene Classification (ASC) and Sound Event Detection (SED) are two separate tasks in the field of computational sound scene analysis. In this work, we present a new dataset with both sound scene and sound event labels and use this to demonstrate a novel method for jointly classifying sound scenes and recognizing sound events. We show that by taking a joint approach, learning is more efficient and whilst improvements are still needed for sound event detection, SED results are robust in a dataset where the sample distribution is skewed towards sound scenes. We cannot compare different systems which use different datasets, therefore we cannot make any claims on superiority on the state of the art. We show that performance on the joint system is comparable with performance on the isolated ASC system using the same dataset.
Abstract (translated)
声场景分类（ASC）和声事件检测（SED）是计算声场景分析领域中两个独立的任务。在这项工作中，我们提出了一个同时带有声音场景和声音事件标签的新数据集，并用它来演示一种联合分类声音场景和识别声音事件的新方法。我们表明，通过采用联合方法，学习更有效，虽然声音事件检测仍然需要改进，但在样本分布偏向声音场景的数据集中，SED结果是稳健的。我们不能比较使用不同数据集的不同系统，因此我们不能在最新技术上宣称其优越性。我们表明，联合系统的性能与使用相同数据集的独立ASC系统的性能相当。
URL
https://arxiv.org/abs/1904.10408
