mlsanityData to 14 Oct 2026

Acoustic Scene Classification Using CNN-GRU Model Without Knowledge Distillation

arXiv 2509.09931 ↗First posted 12 Sep 2025eess.AScited by 0

On the map: Knowledge distillation → Speech and audio distillation

Abstract (arXiv)

In this technical report, we present the SNTL-NTU team's Task 1 submission for the Low-Complexity Acoustic Scenes and Events (DCASE) 2025 challenge. This submission departs from the typical application of knowledge distillation from a teacher to a student model, aiming to achieve high performance with limited complexity. The proposed model is based on a CNN-GRU model and is trained solely using the TAU Urban Acoustic Scene 2022 Mobile development dataset, without utilizing any external datasets, except for MicIRP, which is used for device impulse response (DIR) augmentation. The proposed model has a memory usage of 114.2KB and requires 10.9M muliply-and-accumulate (MAC) operations. Using the development dataset, the proposed model achieved an accuracy of 60.25%.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Speech and audio distillation, one of 12 directions in Knowledge distillation. Builds on 4 of its 6 references that are in the map. First posted on arXiv 12 Sep 2025.

Builds on

4 of 6 references.

References · 4

  1. 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,380
  2. 2020Acoustic scene classification in DCASE 2020 Challenge: generalization across devices and low complexity solutionscited by 50
  3. 2023Device-Robust Acoustic Scene Classification via Impulse Response Augmentationcited by 11
  4. 2024Data-Efficient Low-Complexity Acoustic Scene Classification in the DCASE 2024 Challengecited by 9

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  1. 2024Data-Efficient Low-Complexity Acoustic Scene Classification via Distilling and Progressive Pruning
  2. 2024Data Efficient Acoustic Scene Classification using Teacher-Informed Confusing Class Instruction
  3. 2025Joint Feature and Output Distillation for Low-complexity Acoustic Scene Classification
  4. 2022Low-complexity CNNs for Acoustic Scene Classification
  5. 2023Low-complexity deep learning frameworks for acoustic scene classification using teacher-student scheme and multiple spectrograms
  6. 2025Improving Acoustic Scene Classification with City Features
  7. 2024Low-Complexity Acoustic Scene Classification Using Parallel Attention-Convolution Network
  8. 2025Adaptive Knowledge Distillation using a Device-Aware Teacher for Low-Complexity Acoustic Scene Classification