The time and expense required to collect and label audio data has been a
prohibitive factor in the availability of domain specific audio datasets. As
the predictive specificity of a classifier depends on the specificity of the
labels it is trained on, it follows that finely-labelled datasets are crucial
for advances in machine learning. Aiming to stimulate progress in the field of
machine listening, this paper introduces AeroSonicDB (YPAD-0523), a dataset of
low-flying aircraft sounds for training acoustic detection and classification
systems. This paper describes the method of exploiting ADS-B radio
transmissions to passively collect and label audio samples. Provides a summary
of the collated dataset. Presents baseline results from three binary
classification models, then discusses the limitations of the current dataset
and its future potential. The dataset contains 625 aircraft recordings ranging
in event duration from 18 to 60 seconds, for a total of 8.87 hours of aircraft
audio. These 625 samples feature 301 unique aircraft, each of which are
supplied with 14 supplementary (non-acoustic) labels to describe the aircraft.
The dataset also contains 3.52 hours of ambient background audio ("silence"),
as a means to distinguish aircraft noise from other local environmental noises.
Additionally, 6 hours of urban soundscape recordings (with aircraft
annotations) are included as an ancillary method for evaluating model
performance, and to provide a testing ground for real-time applications.

本论文描述了利用 ADS-B 无线电传输来被动采集和标记音频样本的方法，介绍了 AeroSonicDB（YPAD-0523）数据集，该数据集包含用于训练声学检测和分类系统的低空飞行器声音。文中提供了所收集的数据集的摘要，并展示了三个二元分类模型的基准结果，讨论了当前数据集的局限性和未来的潜力。

AeroSonicDB (YPAD-0523) 数据集的航空器声学检测与分类

The AeroSonicDB (YPAD-0523) Dataset for Acoustic Detection and  Classification of Aircraft

Factory machinery is prone to failure or breakdown, resulting in significant
expenses for companies. Hence, there is a rising interest in machine monitoring
using different sensors including microphones. In the scientific community, the
emergence of public datasets has led to advancements in acoustic detection and
classification of scenes and events, but there are no public datasets that
focus on the sound of industrial machines under normal and anomalous operating
conditions in real factory environments. In this paper, we present a new
dataset of industrial machine sounds that we call a sound dataset for
malfunctioning industrial machine investigation and inspection (MIMII dataset).
Normal sounds were recorded for different types of industrial machines (i.e.,
valves, pumps, fans, and slide rails), and to resemble a real-life scenario,
various anomalous sounds were recorded (e.g., contamination, leakage, rotating
unbalance, and rail damage). The purpose of releasing the MIMII dataset is to
assist the machine-learning and signal-processing community with their
development of automated facility maintenance. The MIMII dataset is freely
available for download at: this https URL

该研究介绍了一个名为 MIMII 的声音数据集，其中包括正常和异常操作下四种不同类型的工业机器声音，以协助机器学习和信号处理领域的自动化设备维护的发展。