Audio Event Recognition by Multitask Learning of Audio Attribute Classification

Abstract

Audio Event Recognize, which is about how to recognize audio events in the environment. It is receiving increased attention. With the development of technologies and hardware, deep learning has become the primary method of audio event recognition. In the convention methods, audio event recognition lacks supervised information. Thus, to learn from using the multiple information fusion to recognize audio events like the human auditory system, this paper proposes a method based on multitask learning of audio attribute classification. The attribute labels are defined by the audio production process. In the preliminary experiments, we add three kinds of audio attribute information to support network learning. Experiments show that for the ESC-50 and Urbansound8K datasets, audio attribute classification achieves higher accuracy, and recognition system performance improves obviously. This paper verified the stability of the three attributes and the effectiveness of attribute tags as auxiliary information.