  Many datasets and approaches in ambient sound analysis use weakly labeled
data.Weak labels are employed because annotating every data sample with a
strong label is too expensive.Yet, their impact on the performance in
comparison to strong labels remains unclear.Indeed, weak labels must often be
dealt with at the same time as other challenges, namely multiple labels per
sample, unbalanced classes and/or overlapping events.In this paper, we
formulate a supervised learning problem which involves weak labels.We create a
dataset that focuses on the difference between strong and weak labels as
opposed to other challenges. We investigate the impact of weak labels when
training an embedding or an end-to-end classifier.Different experimental
scenarios are discussed to provide insights into which applications are most
sensitive to weakly labeled data.
