Learning Sound Event Classifiers from Web Audio with Noisy Labels

ABSTRACT As sound event classification moves towards larger datasets, issues of label noise become inevitable. Web sites can supply large volumes ofuser-contributed audio and metadata, but inferring labels from this metadata introduces errors due to unreliable inputs, and limitations in the mapping. There is, however, little research into the impact of these errors. To foster the investigation of label noise in sound event classification we present FSDnoisy18k, a dataset containing 42.5hours of audio across 20 sound classes, including a small amount of manually-labeled data and a larger quantity of realworld noisydata. We characterize the label noise empirically, and provide a CNN baseline system. Experiments suggest that training withlarge amounts of noisy data can outperform training with smaller amounts of carefully-labeled data. We also show that noiserobust lossfunctions can be effective in improving performance in presence of corrupted labels.