# Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data Augmentation

arXiv: https://arxiv.org/abs/2007.08389 · HTML: https://mlsanity.com/paper/2007.08389/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2020.

## Built on by (most cited first, 13 of 13)

- [Voice2Series: Reprogramming Acoustic Models for Time Series Classification](https://mlsanity.com/paper/2106.09296/) (2021)
- [Slow-Fast Auditory Streams For Audio Recognition](https://mlsanity.com/paper/2103.03516/) (2021)
- [TC-SKNet with GridMask for Low-complexity Classification of Acoustic scene](https://mlsanity.com/paper/2210.02287/) (2022)
- [QTI Submission to DCASE 2021: residual normalization for device-imbalanced acoustic scene classification with efficient design](https://mlsanity.com/paper/2206.13909/) (2022)
- [How to Estimate Model Transferability of Pre-Trained Speech Models?](https://mlsanity.com/paper/2306.01015/) (2023)
- [Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization](https://mlsanity.com/paper/2111.06531/) (2021)
- [A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification](https://mlsanity.com/paper/2107.01461/) (2021)
- [Bringing the Discussion of Minima Sharpness to the Audio Domain: a Filter-Normalised Evaluation for Acoustic Scene Classification](https://mlsanity.com/paper/2309.16369/) (2023)
- [A Variational Bayesian Approach to Learning Latent Variables for Acoustic Knowledge Transfer](https://mlsanity.com/paper/2110.08598/) (2021)
- [Attentive max feature map and joint training for acoustic scene classification](https://mlsanity.com/paper/2104.07213/) (2021)
- [Robust Feature Learning on Long-Duration Sounds for Acoustic Scene Classification](https://mlsanity.com/paper/2108.05008/) (2021)
- [L2BN: Enhancing Batch Normalization by Equalizing the L2 Norms of Features](https://mlsanity.com/paper/2207.02625/) (2022)
- [SSNAPS: Audio-Visual Separation of Speech and Background Noise with Diffusion Inverse Sampling](https://mlsanity.com/paper/2602.01394/) (2026)

## Abstract (arXiv)

In this technical report, we present a joint effort of four groups, namely GT, USTC, Tencent, and UKE, to tackle Task 1 - Acoustic Scene Classification (ASC) in the DCASE 2020 Challenge. Task 1 comprises two different sub-tasks: (i) Task 1a focuses on ASC of audio signals recorded with multiple (real and simulated) devices into ten different fine-grained classes, and (ii) Task 1b concerns with classification of data into three higher-level classes using low-complexity solutions. For Task 1a, we propose a novel two-stage ASC system leveraging upon ad-hoc score combination of two convolutional neural networks (CNNs), classifying the acoustic input according to three classes, and then ten classes, respectively. Four different CNN-based architectures are explored to implement the two-stage classifiers, and several data augmentation techniques are also investigated. For Task 1b, we leverage upon a quantization method to reduce the complexity of two of our top-accuracy three-classes CNN-based architectures. On Task 1a development data set, an ASC accuracy of 76.9% is attained using our best single classifier and data augmentation. An accuracy of 81.9% is then attained by a final model fusion of our two-stage ASC classifiers. On Task 1b development data set, we achieve an accuracy of 96.7% with a model size smaller than 500KB. Code is available: https://github.com/MihawkHu/DCASE2020task1.