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In this paper, we introduce the concept of Eventness for audio eventdetection, which can, in part, be thought of as an analogue to Objectness fromcomputer vision. The key observation behind the eventness concept is that audioevents reveal themselves as 2-dimensional time-frequency patterns with specifictextures and geometric structures in spectrograms. These time-frequencypatterns can then be viewed analogously to objects occurring in natural images(with the exception that scaling and rotation invariance properties do notapply). With this key observation in mind, we pose the problem of detectingmonophonic or polyphonic audio events as an equivalent visual object(s)detection problem under partial occlusion and clutter in spectrograms. We adapta state-of-the-art visual object detection model to evaluate the audio eventdetection task on publicly available datasets. The proposed network hascomparable results with a state-of-the-art baseline and is more robust onminority events. Provided large-scale datasets, we hope that our proposedconceptual model of eventness will be beneficial to the audio signal processingcommunity towards improving performance of audio event detection.

Eventness: Object Detection on Spectrograms for Temporal Localization of Audio Events