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首页> 外文期刊>Building and Environment >Use of back-propagation neural networks to predict both level and temporal-spectral composition of sound pressure in urban sound environments
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Use of back-propagation neural networks to predict both level and temporal-spectral composition of sound pressure in urban sound environments

机译：使用反向传播神经网络预测城市声环境中声压的水平和时间谱组成

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摘要

One of the main challenges of urban planning is to create soundscapes capable of providing inhabitants with a high quality of life. Urban planners need tools that enable them to approach the final goal of designing, planning, and assessing soundscapes in order to adapt them to the needs of the population. Nowadays, authorities have models for predicting the A-weighted equivalent sound-pressure level (L_(Aeq)) Nevertheless, it is necessary to analyze not only the (L_(aeq)) parameter but also the temporal and spectral composition of the sound pressure in the soundscape considered. The problem of modelling and predicting environmental noise in urban settings is a complex and non-linear problem. Therefore, in the present study, a prediction model based on a back-propagation neural network to solve this problem is proposed and examined. This model (STACO model) is intended to predict the short-term (5-min integration period) level and temporal-spectral composition of the sound pressure of urban sonic environments. Here, it is shown that the proposed model yields a precise and accurate prediction. Moreover, the results in this work demonstrate the validity of generalization of the STACO model, being applicable not only for the situations/locations measured, but also for any situation/location of a medium-sized urban setting, with some prior adjustment In summary, the prediction model proposed in this study may serve as a tool for the integration of acoustical variables in city planning.
机译：城市规划的主要挑战之一是创建能够为居民提供高质量生活的音景。城市规划人员需要使他们能够接近设计，规划和评估音景的最终目标的工具，以使其适应人群的需求。如今，当局拥有用于预测A加权等效声压级（L_（Aeq））的模型。尽管如此，不仅有必要分析（L_（aeq））参数，而且还需要分析声压的时间和频谱组成。在考虑的音景中。在城市环境中对环境噪声进行建模和预测的问题是一个复杂且非线性的问题。因此，在本研究中，提出并检验了基于反向传播神经网络的预测模型来解决该问题。该模型（STACO模型）旨在预测城市声环境的声压的短期（5分钟积分期）水平和时谱组成。在这里，表明所提出的模型产生了精确的预测。此外，这项工作的结果证明了STACO模型泛化的有效性，不仅适用于所测量的情况/位置，而且还适用于中等城市环境的任何情况/位置，并且需要进行一些预先调整。在这项研究中提出的预测模型可以用作在城市规划中整合声学变量的工具。

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