Environmental Noise Dataset for Sound Event Classification and Detection

Sound Event Classification (SEC) and Sound Event Detection (SED) are gaining momentum across various domains. With the rise of machine learning, identifying specific sound sources amid background noise in outdoor environments has become a major focus. Recognizable sound types are many and vary depending on context, ranging from vehicles, trains, and aircraft to human and animal activity. This work introduces two open-access datasets, DataSEC and DataSED, created to address gaps identified in the existing dataset literature. Together, they provide over 35 hours of authentic, non-synthesized.wav audio, collected from sound level meter measurements and online repositories. DataSEC consists of 4292 audio samples, with each sample representing a single event that has been classified into one of 22 defined sound classes and 28 subclasses. DataSED comprises 712 real-world recordings containing multiple events, accompanied by over 4000 labels provided in .csv format. These datasets extend across a range of urban to rural environments and have been designed to support research in real-world sound event classification and automated analysis of environmental noise.