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

Low-complexity deep learning frameworks for acoustic scene classification using teacher-student scheme and multiple spectrograms