Robust Laughter Detection in Noisy Environments

By Jon Gillick, Wesley Hanwen Deng, Kimiko Ryokai, David Bamman. 2021

42 citations recorded by the source corpus.

We investigate the problem of automatically identifying and extracting laughter from audio ﬁles in noisy environments. We conduct an empirical evaluation of several machine learning models using audio data of varying sound quality, ﬁnding that while previously published methods work relatively well in controlled environments, performance drops precipitously in real-world settings with background noise. In the process, we contribute a new dataset of laughter annotations on top of the existing AudioSet corpus, with precise segmentations for the start and end points of each laugh, and we present a new approach to laughter detection that performs comparatively well in uncontrolled environments. We discuss the utility of our approach as well as the importance of understanding the variability of model performance in a range of real-world testing environments.

Subfields: Computer Vision and Pattern Recognition, 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 Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

Generative Spoken Dialogue Language Modeling (2022, methodology): “To detect laughs in the speech, we use an open-source model described in Gillick et al. (2021).”