WO2019242302A1 - 一种基于声源识别的噪声监测方法与系统 - Google Patents

一种基于声源识别的噪声监测方法与系统 Download PDF

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WO2019242302A1
WO2019242302A1 PCT/CN2019/070961 CN2019070961W WO2019242302A1 WO 2019242302 A1 WO2019242302 A1 WO 2019242302A1 CN 2019070961 W CN2019070961 W CN 2019070961W WO 2019242302 A1 WO2019242302 A1 WO 2019242302A1
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noise
sound
sound source
level
data
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PCT/CN2019/070961
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French (fr)
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余磊
徐勇
梁鸿
张明棣
许愿
邢晨
陶志祥
杨铭
曹涵
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哈尔滨工业大学（深圳）
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Priority to ZA2021/00452A priority Critical patent/ZA202100452B/en

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups

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  • the invention relates to a noise monitoring method, in particular to a noise monitoring method and system based on sound source identification.
  • Noise monitoring is the basic condition for solving urban noise pollution.
  • the noise monitoring technology currently used in China is based on the “Technical Specifications for Environmental Noise Monitoring and Routine Monitoring of Urban Acoustic Environment” (HJ 640-2012) issued by the Ministry of Environmental Protection in 2012.
  • the main indicators of standard monitoring are: equivalent continuous A sound level Leq, cumulative percent sound level L10, L50, L90, Lmax, Lmin and standard deviation (SD) measured at each monitoring point for 10min.
  • the monitoring time is the normal working hours during the day and is monitored once a year. From the monitoring methods and indicators, the current noise monitoring data cannot fully reflect the actual situation of the noise environment. This is mainly because the current noise monitoring indicators are mainly the sound level data of the noisy environment.
  • the ambient sound intensity is used to reflect the noise situation.
  • the existing noise source monitoring technology is the identification of noise signals, and the premise of identification is to recognize the identified sound as noise. Mainly through the recognition of the sound signal pattern to determine the location of the noise, to determine the source of noise pollution.
  • identification technology is mainly for the identification of noise in specific places that have been identified as noise, and the method used is to analyze the physical signal characteristics of the noise. At present, noise in many places cannot be judged where it occurs, which leads to unclear noise targeting.
  • Noise is a description when the acoustic environment has a negative impact. Like the visual environment, the acoustic environment is also a landscape environment. For this reason, Canadian composer Murray Schaffer pioneered the concept of the soundscape, which changed the international community's perception of environmental noise. Under the concept of soundscapes, a lot of research has been carried out in the EU, Japan and other countries to discuss noise issues with soundscape standards. The most important issue is the impact of noise caused by different sounds. Their research criticized the error of reflecting noise status with a single indicator of sound level, and through a lot of research on public space and ecological environment, pointed out the impact of noise, noise, and other factors on noise.
  • the present invention provides a noise monitoring method and system based on sound source identification.
  • the invention provides a noise monitoring method based on sound source identification, which includes the following steps:
  • the invention also provides a noise monitoring system based on sound source identification, which includes a data acquisition system, a data analysis system, a data transmission system, and a terminal receiving system.
  • the output end of the data acquisition system and the data analysis system The input end is connected, and the output end of the data analysis system is connected to the input end of the terminal receiving system through a data transmission system.
  • the beneficial effect of the present invention is that through the above scheme, both the noise sound level and the noise levels of different sound sources are considered, and the actual status of the noise can be more reflected, which is conducive to overcoming the problem of inaccurate noise monitoring.
  • FIG. 1 is a schematic diagram of a sound source neural network model of a noise monitoring method based on sound source recognition according to the present invention.
  • FIG. 2 is a schematic diagram of a sound source neural network model of a noise monitoring method based on sound source recognition according to the present invention.
  • FIG. 3 is a schematic diagram of dividing a noise source level in a noise monitoring method based on sound source recognition according to the present invention.
  • FIG. 4 is a schematic diagram of a noise monitoring system based on sound source recognition according to the present invention.
  • ambient sounds can be divided into four categories: natural sounds, living sounds, mechanical sounds, and mixed sounds; among them, mixed sounds include various mixing modes of the above three types of sounds.
  • mixed sounds include various mixing modes of the above three types of sounds.
  • a large number of research results show that people prefer natural sounds, dislike mechanical sounds, and remain neutral to life sounds (mainly related to the subjective background and behavioral status of the recipient).
  • the technical invention is based on the existing soundscape research results and a large amount of field survey data of environmental sounds. It establishes a neural network learning model, develops noise sound source recognition technology, and applies it to noise monitoring.
  • Figures 1 and 2 show the neural network learning models obtained through research. Because the influencing factors and the number of different types of sound sources are different, the model shown in Figure 1 is mainly used to analyze the more influencing factors, and the sound source identification that needs to be combined by categories; the model shown in Figure 2 is mainly used to analyze the influencing factors. More concentrated, a model that can use a large amount of data for neural network learning directly. Because the influencing factors of three types of single sound sources: natural sound, living sound and mechanical sound are relatively clear, the neural network model of FIG. 2 can be used to judge the sound source category, and the mixed sound can be judged by the model of FIG. 1.
  • Ambient sound noise calibration mainly uses the existing noise assessment standards, and continuous equivalent sound levels mainly based on road traffic sounds are used as noise levels. The noise effects produced by other sound sources are compared with road traffic sounds, and subjective evaluations are performed under laboratory conditions. The adjustment level is determined by experiments, and finally the noise values of different noise sources are given.
  • the invention provides a noise monitoring method based on sound source recognition, which includes: (1) using existing universal sound acquisition equipment to collect ambient sound; (2) using noise analysis software and a computer to control the front-end calculation of the collected ambient sound Acoustic and psychoacoustic data and send it to the sound source analysis module; (3) Based on the sound source neural network model and noise level classification module in the sound source analysis module, determine the noise levels of different noise sources, and at the same time according to the noise level results Calculate the noise correction value (4) Calculate the noise correction value based on the result of the noise level, and sum the correction value with the measured value of the ambient sound level to finally obtain the noise sound level, see the following formula:
  • a noise monitoring system based on sound source recognition includes a data acquisition system 1, a data analysis system 2, a data transmission system 3, and a terminal receiving system 4, wherein the data acquisition system 1 The output end of is connected to the input end of the data analysis system 2, and the output end of the data analysis system 2 is connected to the input end of the terminal receiving system 4 through the data transmission system 3.
  • the invention introduces sound source identification technology in noise monitoring, and through research on the perception of different ambient sound noises, constructs an index system reflecting the characteristics of noise sound sources, adds noise source indicators in the process of noise monitoring, and identifies noise sound sources through neural networks.
  • a noise monitoring technology based on sound source identification.
  • the invention provides a noise monitoring method and system based on sound source identification, which can correct the problems in the existing noise measurement, and make up for the shortcomings that the measurement equipment can only provide the ambient sound level but not the noise level.
  • the calculation model of sound source recognition with the existing noise measurement equipment, it can provide noise measurement values that reflect real noise (sounds that people don't want to hear), and eliminate the current errors that use ambient sound levels as noise sound levels, so that Noise measurement is more scientific, and noise measurement equipment is more accurate and effective.
  • This invention technology will be able to provide scientific data measurement methods for the construction of a healthy and quiet urban environment; provide effective technical tools for the scientific management of urban environmental noise, and make targeted enforcement of urban noise; it can also be used for urban ecological environmental protection construction Provide valuable technical data. Through this technical invention, accurate noise measurement equipment can be provided, so that residents can more accurately understand the noise status of their environment, and meet people's increasing quality of life requirements.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)

Abstract

一种基于声源识别的噪声监测方法，包括以下步骤：S1、通过声音采集设备采集环境声；S2、利用噪声分析软件和电脑控制前端计算所采集的环境声的声学和心理声学等数据，并将其发送至声源解析模块；S3、基于声源解析模块中的声源神经网络模型和噪声等级分类模块，判断不同噪声源的噪声等级，同时根据噪声等级结果计算噪声修正值；S4、将噪声修正值与环境声级测量值进行求和，最终得到噪声声级；S5、将噪声声级发回终端控制系统。既反映了噪声声级的大小也考虑了不同声源所造成的主观感受差异，更能真实反映噪声实际状况，有利于克服噪声监测不精准的问题。

Description

一种基于声源识别的噪声监测方法与系统 技术领域
本发明涉及噪声监测方法，尤其涉及一种基于声源识别的噪声监测方法与系统。
背景技术
噪声监测是解决城市噪声污染的基础条件，我国目前采用的噪声监测技术是根据环保部2012年发布的《环境噪声监测技术规范、城市声环境常规监测》(HJ 640-2012)，根据这一技术规范监测的主要指标有，每个监测点位测量10min的等效连续A声级Leq、累积百分声级L10、L50、L90、Lmax、Lmin和标准偏差(SD)。监测时间为昼间正常工作时段，每年监测1次。从监测的方式和指标来看，目前的噪声监测数据不能完全反映噪声环境实际情况。这主要是因为现行噪声监测指标主要是噪声环境的声级数据，以环境声强弱来反映噪声情况，环境声越高越强，则指代噪声污染越严重。然而事实证明，这样的噪声判断指标存在一定误差，特别是在自然声较多的区域，噪声声级仅是噪声污染判断的一个指标，而声源对噪声的影响不能忽视。当前已有的噪声源监测技术是对噪声信号的识别，识别的前提是认定所识别的声音为噪声。主要通过对声音信号模式的识别来判断噪声所处的方位，确定噪声污染的来源。这样的识别技术主要是针对已认定为噪声的特定场所的噪声识别，采用的方法是分析噪声的物理信号特征。目前很多场所噪声不能判断其发生场所，导致噪声靶向不明确。
噪声是对声环境达到负面影响时的描述。如同视觉环境一样，声环境是也一种景观环境，正因如此，加拿大作曲家默里·沙弗率先提出了声景概念，改变了国际社会对环境噪声的认识。在声景理念下，欧盟、日本等国展开了大量研究，以声景标准来讨论噪声问题，其中最主要的议题是不同声音带来的噪声影响。他们的研究批判了以声级单一指标来反映噪声状况的错误，并通过对公共空间、生态环境的大量研究，指出声音类别、场所环境、受声者主体等因素对噪声的影响。虽然声景研究填补了以声级反映噪声的缺陷，但目前的状况是“一事一议”。针对生态性较高的场所，以项目研究的方式明确其噪声环境状况，提出保护措施。这样的噪声监测与管理方式过于繁琐，缺少指标性描述，不适于我国快速城市化发展面临的问题。
由于人类社会进入工业化时代以后，城市逐步成为人类社会最主要也 是最大的人居环境，由此开启了以交通噪声为主的声环境时代。基于工业时代发展起来的声环境监测技术基本针对交通噪声，因此以声级指数作为噪声指数来发表。然而实际情况是声级指标不等同于噪声指标，噪声是指人们对不愿意听到的声音过高时产生的身心负担。由于机械声是工业时代城市的主要环境声源，一般认为声级越大、声环境越差、噪声污染越严重。然而，随着城市向绿色、生态方向发展，城市中越来越多地出现了自然声以及人们喜闻乐见的生活声，以等效连续A声级Leq作为噪声级的监测方法，没有区分不同声源带来的影响，特别不适宜生态城市环境中存在较多的生态声音的状况。
发明内容
为了解决现有技术中的问题，本发明提供了一种基于声源识别的噪声监测方法与系统。
本发明提供了一种基于声源识别的噪声监测方法，包括以下步骤：
S1、通过声音采集设备采集环境声；
S2、利用噪声分析软件和电脑控制前端计算所采集的环境声的声学和心理声学数据，并将其发送至声源解析模块；
S3、基于声源解析模块中的声源神经网络模型和噪声等级分类模块，判断不同噪声源的噪声等级，同时根据噪声等级结果计算噪声修正值；
S4、将噪声修正值与环境声级测量值进行求和，最终得到噪声声级；
S5、利用数据传输系统，将噪声声级发回终端控制系统。
本发明还提供了一种基于声源识别的噪声监测系统，包括数据采集系统、数据解析系统、数据传输系统和终端接收系统，其中，所述数据采集系统的输出端与所述数据解析系统的输入端连接，所述数据解析系统的输出端通过数据传输系统与所述终端接收系统的输入端连接。
本发明的有益效果是：通过上述方案，既反映了噪声声级大小也考虑了不同声源的噪声程度，更能反映噪声实际状况，有利于克服噪声监测不精准的问题。
附图说明
图1是本发明一种基于声源识别的噪声监测方法的声源神经网络模型示意图。
图2是本发明一种基于声源识别的噪声监测方法的声源神经网络模型示意图。
图3是本发明一种基于声源识别的噪声监测方法的噪声源等级划分示意图。
图4是本发明一种基于声源识别的噪声监测系统的示意图。
具体实施方式
下面结合附图说明及具体实施方式对本发明作进一步说明。
已有研究表明，环境声主要可以分为四大类：自然声、生活声、机械声和混合声；其中，混合声包括以上三类声音的各种混合模式。在现实世界中，混合声存在较多，即便是单类声音环境也或多或少地包含有其他类别的声音。大量研究结果表明，人们偏好自然声、厌恶机械声、对生活声保持中立(主要与受声者的主观背景和行为状态相关)。本技术发明基于已有的声景研究结果以及掌握的大量环境声实地调查数据，通过建立神经网络学习模型，发展噪声声源识别技术，并将其应用在噪声监测工作中。
图1、2表达了通过研究已获得的神经网路学习模型。由于针对不同类别声源的影响因素及其数量有所不同，图1所示模型主要用来分析影响因素较多，需要进行类别合并的声源识别；图2所示模型主要用来分析影响因素较集中，可利用大量数据直接进行神经网络学习的模型。由于自然声、生活声和机械声三类单一声源的影响因素较为明确，可采用图2神经网络模型进行声源类别判断，混合声则采用图1模型进行判断。
在神经网络判断声源类别基础上，通过主观评价实验，将环境声声源进行噪声等级划分，见图3所示。再以噪声等级对环境声声级进行校准，提出环境声的噪声等级。环境声噪声校准主要采用现有噪声评定标准，以道路交通声为主的连续等效声级为噪声级，其他声源产生的噪声效果与道路交通声进行对比，通过实验室条件下的主观评价实验制定调整等级，最终给出不同噪声源的噪声值。
本发明提供的一种基于声源识别的噪声监测方法，包括：(1)利用现有通用的声音采集设备采集环境声；(2)利用噪声分析软件和电脑控制前端计算所采集的环境声的声学和心理声学数据，并将其发送至声源解析模块；(3)基于声源解析模块中的声源神经网络模型和噪声等级分类模块，判断不同噪声源的噪声等级，同时根据噪声等级结果计算噪声修正值(4)根据噪声等级结果计算噪声修正值，同时将修正值与环境声级测量值进行求和，最终得到噪声声级，见如下公式：
F＝x+y   (4)
F——噪声等级值；
x——噪声等级修正值；
y——环境声级测量值。
(5)利用数据传输系统，将噪声声级发回终端控制系统，从而实现对城市噪声环境的有效管理和决策。
如图4所示，本发明提供的一种基于声源识别的噪声监测系统，包括数据采集系统1、数据解析系统2、数据传输系统3和终端接收系统4，其中，所述数据采集系统1的输出端与所述数据解析系统2的输入端连接，所述数据解析系统2的输出端通过数据传输系统3与所述终端接收系统4的输入端连接。
本发明在噪声监测中引入声源识别技术，通过对不同环境声噪声感知的研究，构建反映噪声声源特性的指标体系，在噪声监测过程中加入噪声源指标，通过神经网络识别噪声声源，发展一种基于声源识别的噪声监测技术。
本发明提供的一种基于声源识别的噪声监测方法与系统，可以改正现有噪声测量中的问题，弥补测量设备只能提供环境声级不能提供噪声级的不足。通过将声源识别计算模型结合到现有噪声测量设备上，能够提供反映真正噪声(人们不愿意听到的声音)的噪声测量值，消除当前以环境声级作为噪声声级存在的误差，使噪声测量更加科学，噪声测量设备更加精准、有效。这一发明技术将能够为健康、宁静的城市环境建设提供科学的数据测量方法；为科学地管理城市环境噪声提供有效的技术工具，使城市噪声执法做到有的放矢；还可以为城市生态环境保护建设提供有价值的技术数据。通过这一技术发明，能够提供精确的噪声测量设备，从而能够使居民更准确地了解其身处环境的噪声状况，满足人们日益增长的生活品质要求。
以上内容是结合具体的优选实施方式对本发明所作的进一步详细说明，不能认定本发明的具体实施只局限于这些说明。对于本发明所属技术领域的普通技术人员来说，在不脱离本发明构思的前提下，还可以做出若干简单推演或替换，都应当视为属于本发明的保护范围。

Claims (2)

  1. 一种基于声源识别的噪声监测方法，其特征在于，包括以下步骤：
    S1、通过声音采集设备采集环境声；
    S2、利用噪声分析软件和电脑控制前端计算所采集的环境声的声学和心理声学数据，并将其发送至声源解析模块；
    S3、基于声源解析模块中的声源神经网络模型和噪声等级分类模块，判断不同噪声源的噪声等级，同时根据噪声等级结果计算噪声修正值；
    S4、将噪声修正值与环境声级测量值进行求和，最终得到噪声声级；
    S5、利用数据传输系统，将噪声声级发回终端控制系统。
  2. 一种基于声源识别的噪声监测系统，其特征在于：包括数据采集系统、数据解析系统、数据传输系统和终端接收系统，其中，所述数据采集系统的输出端与所述数据解析系统的输入端连接，所述数据解析系统的输出端通过数据传输系统与所述终端接收系统的输入端连接。
PCT/CN2019/070961 2018-06-22 2019-01-09 一种基于声源识别的噪声监测方法与系统 WO2019242302A1 (zh)

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