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

arXiv 2305.09463 ↗First posted 16 May 2023cs.SD cs.AI eess.AScited by 0

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Abstract (arXiv)

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%.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 26 references that are in the map. First posted on arXiv 16 May 2023.

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References · 16

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4 more
  1. 2020Sound Context Classification Basing on Join Learning Model and Multi-Spectrogram Featurescited by 5
  2. 2021Deep Learning Frameworks Applied For Audio-Visual Scene Classificationcited by 4
  3. 2021A Low-Compexity Deep Learning Framework For Acoustic Scene Classificationcited by 3
  4. 2022Robust, General, and Low Complexity Acoustic Scene Classification Systems and An Effective Visualization for Presenting a Sound Scene Contextcited by 1

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