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.