# Impact of Acoustic Event Tagging on Scene Classification in a Multi-Task Learning Framework

arXiv: https://arxiv.org/abs/2206.13476 · HTML: https://mlsanity.com/paper/2206.13476/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 27 Jun 2022.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://mlsanity.com/paper/1507.05717/) (2015)
- [A multi-device dataset for urban acoustic scene classification](https://mlsanity.com/paper/1807.09840/) (2018)
- [A Comparison of Five Multiple Instance Learning Pooling Functions for Sound Event Detection with Weak Labeling](https://mlsanity.com/paper/1810.09050/) (2018)
- [General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline](https://mlsanity.com/paper/1807.09902/) (2018)
- [Acoustic Scene Classification](https://mlsanity.com/paper/1411.3715/) (2014)
- [Enhancing Sound Texture in CNN-Based Acoustic Scene Classification](https://mlsanity.com/paper/1901.01502/) (2019)
- [Towards joint sound scene and polyphonic sound event recognition](https://mlsanity.com/paper/1904.10408/) (2019)
- [A Joint Detection-Classification Model for Audio Tagging of Weakly Labelled Data](https://mlsanity.com/paper/1610.01797/) (2016)
- [Acoustic Scene Classification using Audio Tagging](https://mlsanity.com/paper/2003.09164/) (2020)
- [A Joint Framework for Audio Tagging and Weakly Supervised Acoustic Event Detection Using DenseNet with Global Average Pooling](https://mlsanity.com/paper/2008.03350/) (2020)
- [Cross-task pre-training for on-device acoustic scene classification](https://mlsanity.com/paper/1910.09935/) (2019)

## Abstract (arXiv)

Acoustic events are sounds with well-defined spectro-temporal characteristics which can be associated with the physical objects generating them. Acoustic scenes are collections of such acoustic events in no specific temporal order. Given this natural linkage between events and scenes, a common belief is that the ability to classify events must help in the classification of scenes. This has led to several efforts attempting to do well on Acoustic Event Tagging (AET) and Acoustic Scene Classification (ASC) using a multi-task network. However, in these efforts, improvement in one task does not guarantee an improvement in the other, suggesting a tension between ASC and AET. It is unclear if improvements in AET translates to improvements in ASC. We explore this conundrum through an extensive empirical study and show that under certain conditions, using AET as an auxiliary task in the multi-task network consistently improves ASC performance. Additionally, ASC performance further improves with the AET data-set size and is not sensitive to the choice of events or the number of events in the AET data-set. We conclude that this improvement in ASC performance comes from the regularization effect of using AET and not from the network's improved ability to discern between acoustic events.