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cs.SD2026
Sampling Bias Compensation for Robust Evaluation of Audio Classification Systems with Partially Labeled Evaluation Datasets
Javier Naranjo-Alcazar, Annamaria Mesaros, Tuomas Virtanen +1
The performance of acoustic machine learning systems is commonly evaluated using fully annotated test sets. In real-world deployments, however, exhaustively labeling large volumes…
cs.SD2026
Soroll-IA: A Weakly Labeled Audio Dataset for Real-World Industrial Port Monitoring
Javier Naranjo-Alcazar, Jordi Grau-Haro, Ruben Ribes-Serrano +2
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 compri…