# Integrating the Data Augmentation Scheme with Various Classifiers for Acoustic Scene Modeling

arXiv: https://arxiv.org/abs/1907.06639 · HTML: https://mlsanity.com/paper/1907.06639/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2019.

## Built on by (most cited first, 9 of 9)

- [Slow-Fast Auditory Streams For Audio Recognition](https://mlsanity.com/paper/2103.03516/) (2021)
- [Receptive Field Regularization Techniques for Audio Classification and Tagging with Deep Convolutional Neural Networks](https://mlsanity.com/paper/2105.12395/) (2021)
- [QTI Submission to DCASE 2021: residual normalization for device-imbalanced acoustic scene classification with efficient design](https://mlsanity.com/paper/2206.13909/) (2022)
- [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)
- [Fairness and underspecification in acoustic scene classification: The case for disaggregated evaluations](https://mlsanity.com/paper/2110.01506/) (2021)
- [Advancing underwater acoustic target recognition via adaptive data pruning and smoothness-inducing regularization](https://mlsanity.com/paper/2304.11907/) (2023)
- [Robust Feature Learning on Long-Duration Sounds for Acoustic Scene Classification](https://mlsanity.com/paper/2108.05008/) (2021)
- [Audio-Visual Scene Classification Using A Transfer Learning Based Joint Optimization Strategy](https://mlsanity.com/paper/2204.11420/) (2022)

## Abstract (arXiv)

This technical report describes the IOA team's submission for TASK1A of DCASE2019 challenge. Our acoustic scene classification (ASC) system adopts a data augmentation scheme employing generative adversary networks. Two major classifiers, 1D deep convolutional neural network integrated with scalogram features and 2D fully convolutional neural network integrated with Mel filter bank features, are deployed in the scheme. Other approaches, such as adversary city adaptation, temporal module based on discrete cosine transform and hybrid architectures, have been developed for further fusion. The results of our experiments indicates that the final fusion systems A-D could achieve an accuracy higher than 85% on the officially provided fold 1 evaluation dataset.