Limitations of Weak Labels for Embedding and Tagging

By Nicolas Turpault, Romain Serizel, Emmanuel Vincent. 2020

11 citations recorded by the source corpus.

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.

Subfields: Developmental Biology, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Developmental Biology

1. Xavier Serra
2. Jordi Pons
3. D. Ellis
4. Eduardo Fonseca
5. Xavier Favory
6. Manoj Plakal
7. Frederic Font

In Signal Processing

1. Xavier Serra
2. Eduardo Fonseca
3. Frederic Font
4. Xavier Favory
5. Jordi Pons
6. D. Ellis
7. Manoj Plakal
8. Aren Jansen

Citations in the corpus

• FSD50K: An Open Dataset of Human-Labeled Sound Events (2020, background): “The impact and limitations of weak labels in SER are discussed in [32, 78].”
• The Benefit of Temporally-Strong Labels in Audio Event Classification (2021, background): “A performance boost is reported by adding a small amount of strongly labeled data to the weak labels, possibly due to the mitigation of the inherent problems in weakly labeled data [13, 14, 15].”