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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
Automatic Contextual Audio Denoising
Diep Luong, Konstantinos Drossos, Mikko Heikkinen +1
Audio context determines which sound components and sources are relevant and which can be perceived as irrelevant (noise) by listeners. For example, traffic noise is informative in…