Soroll-IA: A Weakly Labeled Audio Dataset for Real-World Industrial Port Monitoring

2026-06-24

AI Summary

This work addresses the lack of weakly labeled audio datasets tailored to real-world industrial port environments, which has hindered the development of robust sound event detection and acoustic analysis. To bridge this gap, we introduce and publicly release the first weakly annotated audio dataset for industrial ports, comprising approximately 22 hours of audio (7,396 segments) recorded at fixed sensor nodes in the Port of Valencia, Spain, capturing 26 representative sound source classes. Two versions of weak labels, derived from expert consensus, are provided. We establish benchmark tasks that account for challenges such as high ambient noise, long-distance recording, and overlapping events, and evaluate performance using CNN14 for high-accuracy audio tagging and MobileNetV2 for edge-compatible real-time classification. This dataset serves as a valuable benchmark for weakly supervised sound event detection, audio tagging, and machine learning under low-resource conditions.

Abstract

Soroll-IA is a weakly labeled environmental audio dataset recorded in a real-world industrial port environment in Valencia (Spain) using two fixed sensing nodes. The dataset comprises approximately 22 hours of audio segmented into 7,396 clips and covers 26 sound event classes representative of industrial port acoustic activity commonly observed in such environments, such as crane sirens, train movements, traffic, and other logistical and industrial sounds. Recordings were captured under highly challenging acoustic conditions, including strong background noise, long-distance sources, and frequent event overlap. All audio clips were annotated by domain experts following a weak labeling strategy, where tags indicate the presence of sound events within a clip without temporal localization. To account for inter-annotator variability, two ground-truth versions are released: one without cross-validation, where a class is considered present if annotated by at least one expert, and a second, more conservative version based on cross-validation, where agreement by at least two-thirds of the annotators is required. The dataset is intended to support research in audio tagging, weakly supervised sound event detection, and machine learning under realistic industrial acoustic conditions. Benchmark results are provided using two complementary architectures: CNN14 representing high-capacity convolutional models for audio tagging, and MobileNetV2, selected for its suitability in real-time classification on low-resource edge devices. To the best of current knowledge, Soroll-IA constitutes an available dataset dedicated exclusively to industrial port acoustic environments, aiming to foster advances in robust environmental sound analysis for safety-critical and operational monitoring applications. The dataset is available online and collected under Attribution-NonCommercial 4.0 International license.

Problem

• weakly labeled audio
• industrial port monitoring
• sound event detection
• environmental sound analysis
• real-world acoustic conditions

Innovation

• weakly labeled audio dataset
• industrial port acoustic monitoring
• sound event detection
• inter-annotator variability
• edge-device audio classification