  In this paper, we introduce the concept of Eventness for audio event
detection, which can, in part, be thought of as an analogue to Objectness from
computer vision. The key observation behind the eventness concept is that audio
events reveal themselves as 2-dimensional time-frequency patterns with specific
textures and geometric structures in spectrograms. These time-frequency
patterns can then be viewed analogously to objects occurring in natural images
(with the exception that scaling and rotation invariance properties do not
apply). With this key observation in mind, we pose the problem of detecting
monophonic or polyphonic audio events as an equivalent visual object(s)
detection problem under partial occlusion and clutter in spectrograms. We adapt
a state-of-the-art visual object detection model to evaluate the audio event
detection task on publicly available datasets. The proposed network has
comparable results with a state-of-the-art baseline and is more robust on
minority events. Provided large-scale datasets, we hope that our proposed
conceptual model of eventness will be beneficial to the audio signal processing
community towards improving performance of audio event detection.
