Noise Signature Identification (Ambient Sounds in the University of South Florida, EBII)

We recorded the ambient sound of several rooms of the Engineering Building II of the University of South Florida. After filtering the sample to isolate ambient noise, we trained the system using both binary classification -whether or not an audio sample belonged to a specific room- and multi-class classification, which room out of the 19 possible rooms, hallways, entries, and meeting spaces does the audio sample belong to.
These files contain the ARFF files used to train and test the models in Weka (https://www.cs.waikato.ac.nz/ml/weka/). They are separated by rooms to the Binary classification, except one for the Multiclass classification.

本数据集采集了南佛罗里达大学（University of South Florida）第二工程楼多个房间的环境声信号。在对样本进行滤波以分离出环境噪声后，我们采用二分类与多分类两种方式对系统进行训练：二分类任务用于判断音频样本是否属于某一指定房间；多分类任务则需从19个可选房间、走廊、入口及会议空间中，判定音频样本所属的具体空间。
本数据集包含用于在Weka（https://www.cs.waikato.ac.nz/ml/weka/）中训练与测试模型的ARFF（Attribute-Relation File Format，属性关系文件格式）文件。其中，除用于多分类任务的单个文件外，其余文件均按房间类别划分，以支撑二分类任务的开展。