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

Lam Pham, Dat Ngo, Cam Le, Anahid Jalali, Alexander Schindler
5/16/2023
cs.SDcs.AIeess.AS

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Abstract

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%.

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Cite this paper

@article{pham2023lowcomplexity,
  title  = {Low-complexity deep learning frameworks for acoustic scene classification using teacher-student scheme and multiple spectrograms},
  author = {Lam Pham and Dat Ngo and Cam Le and Anahid Jalali and Alexander Schindler},
  year   = {2023},
  eprint = {2305.09463},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2305.09463v1}
}

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