# Urban Sound Tagging using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1909.12699 · HTML: https://mlsanity.com/paper/1909.12699/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2019.

## Built on by (most cited first, 7 of 7)

- [PSLA: Improving Audio Tagging with Pretraining, Sampling, Labeling, and Aggregation](https://mlsanity.com/paper/2102.01243/) (2021)
- [CMKD: CNN/Transformer-Based Cross-Model Knowledge Distillation for Audio Classification](https://mlsanity.com/paper/2203.06760/) (2022)
- [Detection of Audio-Video Synchronization Errors Via Event Detection](https://mlsanity.com/paper/2104.10116/) (2021)
- [Deep Generative Fixed-filter Active Noise Control](https://mlsanity.com/paper/2303.05788/) (2023)
- [A Visual Domain Transfer Learning Approach for Heartbeat Sound Classification](https://mlsanity.com/paper/2107.13237/) (2021)
- [Responding to Challenge Call of Machine Learning Model Development in Diagnosing Respiratory Disease Sounds](https://mlsanity.com/paper/2111.14354/) (2021)
- [Performance Evaluation of Selective Fixed-filter Active Noise Control based on Different Convolutional Neural Networks](https://mlsanity.com/paper/2208.08440/) (2022)

## Abstract (arXiv)

In this paper, we propose a framework for environmental sound classification in a low-data context (less than 100 labeled examples per class). We show that using pre-trained image classification models along with the usage of data augmentation techniques results in higher performance over alternative approaches. We applied this system to the task of Urban Sound Tagging, part of the DCASE 2019. The objective was to label different sources of noise from raw audio data. A modified form of MobileNetV2, a convolutional neural network (CNN) model was trained to classify both coarse and fine tags jointly. The proposed model uses log-scaled Mel-spectrogram as the representation format for the audio data. Mixup, Random erasing, scaling, and shifting are used as data augmentation techniques. A second model that uses scaled labels was built to account for human errors in the annotations. The proposed model achieved the first rank on the leaderboard with Micro-AUPRC values of 0.751 and 0.860 on fine and coarse tags, respectively.