Label noise is emerging as a pressing issue in sound event classification.\nThis arises as we move towards larger datasets that are difficult to annotate\nmanually, but it is even more severe if datasets are collected automatically\nfrom online repositories, where labels are inferred through automated\nheuristics applied to the audio content or metadata. While learning from noisy\nlabels has been an active area of research in computer vision, it has received\nlittle attention in sound event classification. Most recent computer vision\napproaches against label noise are relatively complex, requiring complex\nnetworks or extra data resources. In this work, we evaluate simple and\nefficient model-agnostic approaches to handling noisy labels when training\nsound event classifiers, namely label smoothing regularization, mixup and\nnoise-robust loss functions. The main advantage of these methods is that they\ncan be easily incorporated to existing deep learning pipelines without need for\nnetwork modifications or extra resources. We report results from experiments\nconducted with the FSDnoisy18k dataset. We show that these simple methods can\nbe effective in mitigating the effect of label noise, providing up to 2.5\\% of\naccuracy boost when incorporated to two different CNNs, while requiring minimal\nintervention and computational overhead.\n