MAVD-traffic dataset

This is a dataset for sound event detection in urban environments, which is the first of a series of datasets planned within an ongoing research project for urban noise monitoring in Montevideo city, Uruguay. The dataset is called MAVD for Montevideo Audio and Video Dataset. This release focuses on traffic noise, hence the name MAVD-traffic, as it is usually the predominant noise source in urban environments. Apart from audio recordings it also includes synchronized video files. The sound event annotations follow an ontology for traffic sounds that is the combination of a set of two taxonomies: vehicle types (e.g. car, bus) and vehicle components (e.g.engine, brakes), and a set of actions related to them (e.g. idling, accelerating). Thus, the proposed ontology allows for a flexible and detailed description of traffic sounds. Since the taxonomies follow a hierarchy it can be used with different levels of detail. The dataset was presented in: Pablo Zinemanas, Pablo Cancela and Martín Rocamora. "MAVD: a dataset for sound event detection in urban environments." DCASE 2019 Workshop, 25-26 October 2019, New York, USA When MAVD-traffic is used for academic research, we would highly appreciate it if scientific publications cite the previous paper.本数据集面向城市环境声音事件检测任务，是乌拉圭蒙得维的亚市城市噪声监测持续研究项目规划的系列数据集之首。本数据集命名为MAVD（Montevideo Audio and Video Dataset，蒙得维的亚音视频数据集），本次发布版本聚焦交通噪声，因此定名为MAVD-traffic——交通噪声通常为城市环境中占主导地位的噪声源。除音频录音外，该数据集还包含与音频同步的视频文件。其声音事件标注遵循交通声音本体（ontology）规范，该本体由两类分类体系与一组关联动作集合组合而成：一类为车辆类型分类体系（如轿车、巴士），另一类为车辆部件分类体系（如发动机、制动器），关联动作则涵盖怠速、加速等场景。因此，该本体可实现交通声音灵活且细致的描述。由于分类体系采用层级结构，因此可根据不同细节需求灵活使用。本数据集已在以下文献中发表：Pablo Zinemanas、Pablo Cancela与Martín Rocamora所撰《MAVD：面向城市环境声音事件检测的数据集》，发表于2019年10月25-26日美国纽约举办的DCASE 2019工作坊。若将MAVD-traffic用于学术研究，恳请相关学术出版物引用上述文献。