Learning Sound Event Classifiers from Web Audio with Noisy Labels

Understand

As sound event classification moves towards larger datasets, issues of label noise become inevitable.

• Web sites can supply large volumes of user-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 42.5 hours of audio across 20 sound classes, including a small amount of manually-labeled data and a larger quantity of real-world noisy data.