# GPLA-12: An Acoustic Signal Dataset of Gas Pipeline Leakage

arXiv: https://arxiv.org/abs/2106.10277 · HTML: https://mlsanity.com/paper/2106.10277/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 29 references that are in the map.
- First posted on arXiv 19 Jun 2021.

## Builds on (most cited first, 3 of 3)

- [Zero-Shot Audio Classification via Semantic Embeddings](https://mlsanity.com/paper/2011.12133/) (2020)
- [Deep Convolutional Neural Network with Mixup for Environmental Sound Classification](https://mlsanity.com/paper/1808.08405/) (2018)
- [An Ensemble of Convolutional Neural Networks for Audio Classification](https://mlsanity.com/paper/2007.07966/) (2020)

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

- [MGSB: Manifold Gated Signature Branch Pressure-Domain Baseline Architecture for Two-Phase Pipeline Flows Under Distributional Shift](https://mlsanity.com/paper/2608.04805/) (2026)

## Abstract (arXiv)

In this paper, we introduce a new acoustic leakage dataset of gas pipelines, called as GPLA-12, which has 12 categories over 684 training/testing acoustic signals. Unlike massive image and voice datasets, there have relatively few acoustic signal datasets, especially for engineering fault detection. In order to enhance the development of fault diagnosis, we collect acoustic leakage signals on the basis of an intact gas pipe system with external artificial leakages, and then preprocess the collected data with structured tailoring which are turned into GPLA-12. GPLA-12 dedicates to serve as a feature learning dataset for time-series tasks and classifications. To further understand the dataset, we train both shadow and deep learning algorithms to observe the performance. The dataset as well as the pretrained models have been released at both www.daip.club and github.com/Deep-AI-Application-DAIP