轨道交通噪声检测的人工智能方法分析

M. Melnyk, K. Pytel, Mariia Orynchak, V. Tomyuk, V. Havran
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引用次数: 0

摘要

如今,世界上许多城市都饱受噪音污染之苦。噪音是一种无形的危险,会给人类和野生动物带来健康问题。因此,对环境噪声声级进行估算并实施整改措施是十分必要的。有许多噪声识别技术,选择最合适的技术取决于所需的信息及其应用。分析音频数据需要考虑三个关键方面,如时间周期、幅度和频率。根据上述参数,可以识别噪声源。本文建议在交通噪声检测过程中使用人工智能和机器学习算法。计算方法是分析原始数据集和预测结果的最快和最具创新性的方法。在这些方法中识别模式需要大量的数据和计算能力。机器学习模型可以使用三种类型的数据进行训练:实验声音库、从数据提供商处购买的音频数据集和领域专家收集的数据。在研究范围内,使用一个实验数据集来训练一个模型,该模型使用监督学习来预测基于输入的正确结果。开发一个准确的模型需要高质量的数据输入。然而,不正确的数据收集可能会导致特征集中的噪声,就像人为错误或仪器错误一样。真实环境中的交通声事件通常不是孤立发生的,而是往往与其他声音事件有显著的重叠。本文的一部分专门讨论了在交通噪声检测过程中可能出现的问题,如数据处理和数据采集错误。讨论了提高输入数据质量的方法。该研究还指出,基于建设性铁路数据的中央铁路数据库的发展,以及具有铁路特定数据集的中央数据库的发展,将极大地受益于运输噪声检测领域。根据初步的交通噪声分析结果,提出了对有轨电车线路进行现代化改造以降低环境噪声的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of artificial intelligence methods for rail transport traffic noise detection
Nowadays, many cities all over the world suffer from noise pollution. Noise is an invisible danger that can cause health problems for both people and wildlife. Therefore, it is essential to estimate the environmental noise level and implement corrective measures. There are a number of noise identification techniques, and the choice of the most appropriate technique depends upon the information required and its application. Analyzing audio data requires three key aspects to be considered such as time period, amplitude, and frequency. Based on the above parameters, the source of noise can be identified. This research paper suggests the utilization of artificial intelligence and machine learning algorithms for the traffic noise detection process. Computational methods are the fastest and most innovative way to analyze raw data sets and predict results. Identifying patterns in these methods requires a large amount of data and computing power. Machine learning models can be trained using three types of data: experimental sound libraries, audio datasets purchased from data providers, and data collected by domain experts. In the scope of the study, an experimental dataset was used to train a model that predicts the correct outcomes based on the inputs, using supervised learning. Developing an accurate model requires high-quality data input. However, incorrect data collection can cause noise in feature sets, as can human error or instrument error. Traffic sound events in the real environment do not usually occur in isolation but tend to have a significant overlap with other sound events. A part of this paper is dedicated to the problems that may arise during traffic noise detection, like incorrect data processing and data collection. It also discusses the ways to improve the quality of the input data. The study also states that the field of transport noise detection would greatly benefit from the development of a centralized railway database based on constructive railroad data, and from a centralized database with railway-specific datasets. Based on preliminary results of traffic noise analysis, modernization of the tram lines was proposed to reduce the environmental noise.
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