  Acoustic scene classification systems using deep neural networks classify
given recordings into pre-defined classes. In this study, we propose a novel
scheme for acoustic scene classification which adopts an audio tagging system
inspired by the human perception mechanism. When humans identify an acoustic
scene, the existence of different sound events provides discriminative
information which affects the judgement. The proposed framework mimics this
mechanism using various approaches. Firstly, we employ three methods to
concatenate tag vectors extracted using an audio tagging system with an
intermediate hidden layer of an acoustic scene classification system. We also
explore the multi-head attention on the feature map of an acoustic scene
classification system using tag vectors. Experiments conducted on the detection
and classification of acoustic scenes and events 2019 task 1-a dataset
demonstrate the effectiveness of the proposed scheme. Concatenation and
multi-head attention show a classification accuracy of 75.66 % and 75.58 %,
respectively, compared to 73.63 % accuracy of the baseline. The system with the
proposed two approaches combined demonstrates an accuracy of 76.75 %.
