# Acoustic scene classification using auditory datasets

arXiv: https://arxiv.org/abs/2112.13450 · HTML: https://mlsanity.com/paper/2112.13450/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 26 Dec 2021.

## Builds on (most cited first, 7 of 7)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Data augmentation approaches for improving animal audio classification](https://mlsanity.com/paper/1912.07756/) (2019)
- [Acoustic Scene Classification](https://mlsanity.com/paper/1411.3715/) (2014)
- [Mixup-Based Acoustic Scene Classification Using Multi-Channel Convolutional Neural Network](https://mlsanity.com/paper/1805.07319/) (2018)
- [SpecAugment++: A Hidden Space Data Augmentation Method for Acoustic Scene Classification](https://mlsanity.com/paper/2103.16858/) (2021)
- [Receptive-field-regularized CNN variants for acoustic scene classification](https://mlsanity.com/paper/1909.02859/) (2019)
- [An evaluation of data augmentation methods for sound scene geotagging](https://mlsanity.com/paper/2110.04585/) (2021)

## Abstract (arXiv)

The approach used not only challenges some of the fundamental mathematical techniques used so far in early experiments of the same trend but also introduces new scopes and new horizons for interesting results. The physics governing spectrograms have been optimized in the project along with exploring how it handles the intense requirements of the problem at hand. Major contributions and developments brought under the light, through this project involve using better mathematical techniques and problem-specific machine learning methods. Improvised data analysis and data augmentation for audio datasets like frequency masking and random frequency-time stretching are used in the project and hence are explained in this paper. In the used methodology, the audio transforms principle were also tried and explored, and indeed the insights gained were used constructively in the later stages of the project. Using a deep learning principle is surely one of them. Also, in this paper, the potential scopes and upcoming research openings in both short and long term tunnel of time has been presented. Although much of the results gained are domain-specific as of now, they are surely potent enough to produce novel solutions in various different domains of diverse backgrounds.