# Low-complexity deep learning frameworks for acoustic scene classification

arXiv: https://arxiv.org/abs/2206.06057 · HTML: https://mlsanity.com/paper/2206.06057/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 26 references that are in the map.
- First posted on arXiv 13 Jun 2022.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Data Augmentation using Random Image Cropping and Patching for Deep CNNs](https://mlsanity.com/paper/1811.09030/) (2018)
- [Learning from Between-class Examples for Deep Sound Recognition](https://mlsanity.com/paper/1711.10282/) (2017)
- [Mixup-Based Acoustic Scene Classification Using Multi-Channel Convolutional Neural Network](https://mlsanity.com/paper/1805.07319/) (2018)
- [SubSpectralNet - Using Sub-Spectrogram based Convolutional Neural Networks for Acoustic Scene Classification](https://mlsanity.com/paper/1810.12642/) (2018)
- [Robust Acoustic Scene Classification using a Multi-Spectrogram Encoder-Decoder Framework](https://mlsanity.com/paper/2002.04502/) (2020)
- [Multi-view Audio and Music Classification](https://mlsanity.com/paper/2103.02420/) (2021)
- [Wider or Deeper Neural Network Architecture for Acoustic Scene Classification with Mismatched Recording Devices](https://mlsanity.com/paper/2203.12314/) (2022)
- [Spatio-Temporal Attention Pooling for Audio Scene Classification](https://mlsanity.com/paper/1904.03543/) (2019)
- [Acoustic Scene Classification by Implicitly Identifying Distinct Sound Events](https://mlsanity.com/paper/1904.05204/) (2019)
- [Deep Feature Embedding and Hierarchical Classification for Audio Scene Classification](https://mlsanity.com/paper/2002.04857/) (2020)
- [Sound Context Classification Basing on Join Learning Model and Multi-Spectrogram Features](https://mlsanity.com/paper/2005.12779/) (2020)
- [Deep Learning Frameworks Applied For Audio-Visual Scene Classification](https://mlsanity.com/paper/2106.06840/) (2021)
- [A Low-Compexity Deep Learning Framework For Acoustic Scene Classification](https://mlsanity.com/paper/2106.06838/) (2021)

## Built on by (most cited first, 1 of 1)

- [Robust, General, and Low Complexity Acoustic Scene Classification Systems and An Effective Visualization for Presenting a Sound Scene Context](https://mlsanity.com/paper/2210.08610/) (2022)

## Abstract (arXiv)

In this report, we presents low-complexity deep learning frameworks for acoustic scene classification (ASC). The proposed frameworks can be separated into four main steps: Front-end spectrogram extraction, online data augmentation, back-end classification, and late fusion of predicted probabilities. In particular, we initially transform audio recordings into Mel, Gammatone, and CQT spectrograms. Next, data augmentation methods of Random Cropping, Specaugment, and Mixup are then applied to generate augmented spectrograms before being fed into deep learning based classifiers. Finally, to achieve the best performance, we fuse probabilities which obtained from three individual classifiers, which are independently-trained with three type of spectrograms. Our experiments conducted on DCASE 2022 Task 1 Development dataset have fullfiled the requirement of low-complexity and achieved the best classification accuracy of 60.1%, improving DCASE baseline by 17.2%.