Sound-based Multi-Equipment Activity Recognition

This dataset is recorded on real noisy construction job sites.
It consists of two main sound data:
1) single-equipment and 2) multi-equipment.
It consists of five case studies. The first three case studies consist of single-equipment sound for two types of heavy equipment. The last two case studies consist of both single-equipment sound data and real-world multi-equipment sound mixture.
The purpose of this dataset is to train a model using single-equipment sound data and test it on multi-equipment sound mixtures for both synthetic and real-world sound mixtures.
This dataset is recorded using an off-the-shelf Zoom H1 digital handy recorder, which is a single-channel microphone.
Because the dataset is recorded using a single-channel microphone, the process of the multi-label sound classifier is considered a software-based method.
On the other hand, there are many datasets that are recorded using microphone arrays. The process of the multi-label sound classifier for these datasets is considered a hardware-based method.