  Recognizing sounds is a key aspect of computational audio scene analysis and
machine perception. In this paper, we advocate that sound recognition is
inherently a multi-modal audiovisual task in that it is easier to differentiate
sounds using both the audio and visual modalities as opposed to one or the
other. We present an audiovisual fusion model that learns to recognize sounds
from weakly labeled video recordings. The proposed fusion model utilizes an
attention mechanism to dynamically combine the outputs of the individual audio
and visual models. Experiments on the large scale sound events dataset,
AudioSet, demonstrate the efficacy of the proposed model, which outperforms the
single-modal models, and state-of-the-art fusion and multi-modal models. We
achieve a mean Average Precision (mAP) of 46.16 on Audioset, outperforming
prior state of the art by approximately +4.35 mAP (relative: 10.4%).
