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The availability of audio data on sound sharing platforms such as Freesoundgives users access to large amounts of annotated audio. Utilising such data fortraining is becoming increasingly popular, but the problem of label noise thatis often prevalent in such datasets requires further investigation. This paperintroduces ARCA23K, an Automatically Retrieved and Curated Audio datasetcomprised of over 23000 labelled Freesound clips. Unlike past datasets such asFSDKaggle2018 and FSDnoisy18K, ARCA23K facilitates the study of label noise ina more controlled manner. We describe the entire process of creating thedataset such that it is fully reproducible, meaning researchers can extend ourwork with little effort. We show that the majority of labelling errors inARCA23K are due to out-of-vocabulary audio clips, and we refer to this type oflabel noise as open-set label noise. Experiments are carried out in which westudy the impact of label noise in terms of classification performance andrepresentation learning.

ARCA23K: An audio dataset for investigating open-set label noise