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We propose a new deep network for audio event recognition, called AENet. Incontrast to speech, sounds coming from audio events may be produced by a widevariety of sources. Furthermore, distinguishing them often requires analyzingan extended time period due to the lack of clear sub-word units that arepresent in speech. In order to incorporate this long-time frequency structureof audio events, we introduce a convolutional neural network (CNN) operating ona large temporal input. In contrast to previous works this allows us to trainan audio event detection system end-to-end. The combination of our networkarchitecture and a novel data augmentation outperforms previous methods foraudio event detection by 16%. Furthermore, we perform transfer learning andshow that our model learnt generic audio features, similar to the way CNNslearn generic features on vision tasks. In video analysis, combining visualfeatures and traditional audio features such as MFCC typically only leads tomarginal improvements. Instead, combining visual features with our AENetfeatures, which can be computed efficiently on a GPU, leads to significantperformance improvements on action recognition and video highlight detection.In video highlight detection, our audio features improve the performance bymore than 8% over visual features alone.

AENet: Learning Deep Audio Features for Video Analysis