Our paper “Investigating the Effect of Sound-Event Loudness on Crowdsourced Audio Annotations” has been accepted to IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Calgary, Canada, April 2018.
Audio annotation is an important step in developing machine listening systems. It is also a time consuming process, which has motivated investigators to crowdsource audio annotations. However, there are many factors that affect annotations, many of which have not been adequately investigated. In previous work, we investigated the effects of visualization aids and sound scene complexity on the quality of crowdsourced sound-event annotations. In this paper, we extend that work by investigating the effect of sound-event loudness on both sound-event source annotations and sound-event proximity annotations. We find that the sound class, loudness, and annotator bias affect how listeners annotate proximity. We also find that loudness affects recall more than precision and that the strengths of these effects are strongly influenced by the sound class. These findings are not only important for designing effective audio annotation processes, but also for effectively training and evaluating machine-listening systems.
The full paper is available on our Publications page