  In this technical report, a low-complexity deep learning system for acoustic
scene classification (ASC) is presented. The proposed system comprises two main
phases: (Phase I) Training a teacher network; and (Phase II) training a student
network using distilled knowledge from the teacher. In the first phase, the
teacher, which presents a large footprint model, is trained. After training the
teacher, the embeddings, which are the feature map of the second last layer of
the teacher, are extracted. In the second phase, the student network, which
presents a low complexity model, is trained with the embeddings extracted from
the teacher. Our experiments conducted on DCASE 2023 Task 1 Development dataset
have fulfilled the requirement of low-complexity and achieved the best
classification accuracy of 57.4%, improving DCASE baseline by 14.5%.
