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Environmental Noise Dataset for Sound Event Classification and Detection.

Luca Fredianelli¹, Francesco Artuso², Geremia Pompei³

¹Institute for Chemical-Physical Processes of the Italian Research Council, Via Moruzzi 1, 56100, Pisa, Italy. luca.fredianelli@cnr.it.

Scientific Data

|October 30, 2025

Summary

This study introduces DataSEC and DataSED, two open-access datasets for sound event classification and detection. These datasets aid research in analyzing environmental noise and identifying sound sources in real-world settings.

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Area of Science:

Acoustics and Signal Processing
Machine Learning Applications
Environmental Science

Background:

Sound event classification (SEC) and detection (SED) are increasingly important for analyzing audio data.
Identifying sound sources in noisy outdoor environments is a significant challenge.
Existing datasets often have limitations in scope and authenticity.

Purpose of the Study:

To introduce two novel, open-access datasets, DataSEC and DataSED.
To address identified gaps in existing sound event datasets.
To support research in real-world sound event classification and automated environmental noise analysis.

Main Methods:

Collected over 35 hours of authentic, non-synthesized .wav audio data.
Utilized sound level meter measurements and online repositories for data acquisition.
Structured DataSEC with 4292 single-event samples across 22 classes and 28 subclasses.
Developed DataSED with 712 recordings and over 4000 labels in .csv format.

Main Results:

DataSEC provides classified single sound events.
DataSED offers multi-event recordings with detailed event labeling.
Datasets cover diverse urban to rural environments.
The datasets contain authentic, real-world audio recordings.

Conclusions:

DataSEC and DataSED provide valuable resources for SEC and SED research.
These datasets facilitate the development of robust algorithms for environmental sound analysis.
The open-access nature promotes further research and development in the field.