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Noise Directivity
Improved Methods Of Noise Sources Identification

Addressing the role of noise directivity in environmental noise monitoring, this study presents a comparative analysis of measurement site selection effects, illuminated by an unusual discovery: a dog’s bark perceived as originating from above a noise monitoring setup. This incident not only poses a humorous query regarding the flying capabilities of dogs but also serves as a pivotal example of the challenges in noise source identification, emphasizing the importance of strategic site selection as guided by ISO 1996-2:2017.

Utilizing the SV 200A noise monitoring station, designed to assess noise directivity across horizontal and vertical planes, the study examines two distinct locations near an airport’s flight paths. This comparative approach highlights the influence of environmental and physical site characteristics on the accuracy of noise data collection and the potential for automating event identification processes.

What is a noise directivity?

Noise directivity refers to the pattern or direction in which sound waves emanate from a noise source. Unlike omnidirectional sounds, which spread uniformly in all directions, directional noises have a specific orientation, meaning they propagate more strongly or are more intense in one direction than in others. This characteristic is crucial in various applications, including acoustical engineering, environmental noise monitoring, and audio technology, as it affects how sound is perceived and measured in different environments.

Understanding noise directivity is essential for accurately identifying, measuring, and mitigating unwanted sounds, especially in complex environments like urban areas or near airports. For example, by analyzing the directivity patterns of aircraft noise, engineers can design more effective noise barriers or implement noise control measures that minimize the impact on residential areas. Similarly, in audio technology, knowing the directivity of speakers helps in optimizing room acoustics and sound system configurations for better listening experiences.

noise directivity

 Figure 1. Svantek SV 200A

Table of Contents

How the noise directivity is measured?

The SV 200A noise monitoring station measures noise directivity using a sophisticated setup that goes beyond the capabilities of traditional single-microphone systems. It incorporates a primary condenser microphone at its center for general sound level measurements and is augmented with an additional four MEMS microphones positioned equidistantly around the sides of the device. This configuration enables the SV 200A to assess sound from different directions.

The device utilizes a technique involving pairs of signal and phase differences to determine the direction of a dominant noise source, both in vertical and horizontal axes. By comparing the sound pressure levels and phase information collected by the side microphones with that from the central microphone, the SV 200A can discern the direction from which the sound is predominantly coming.

This approach allows for the creation of a sound level distribution in angle sectors, which is recorded over time. Such detailed data on noise directivity is invaluable, as it not only enhances the accuracy of environmental noise measurements but also aids in the filtering and analysis of the data. This method is particularly useful in environments where it is critical to identify the direction of incoming noise, such as in urban soundscapes or in the vicinity of transportation infrastructure like airports and highways.

Location of side microphones in relation to the main microphone in SV 200A

Figure 2. Location of side microphones in relation to the main microphone in SV 200A 

Role of measurement location in noise directivity analysis

Selecting the optimal location for noise measurement is essential for accurately determining the directivity of noise sources. This decision significantly influences the precision with which the direction and intensity of sound waves can be mapped, which is vital for understanding how noise propagates through different environments.

A strategic measurement point allows for an unobstructed capture of sound data, essential for analyzing the directivity patterns of noise. This is particularly important in environments where noise needs to be meticulously managed and mitigated, such as in urban planning or noise pollution studies. Proper site selection ensures that the measurements reflect true noise directivity, free from distortions caused by environmental factors like reflections off buildings or terrain, enabling effective noise control solutions tailored to the specific characteristics of the noise source and its surrounding area.

Study: Analysis of Aircraft Noise Data

The experiment aimed to assess how the choice of measurement site affects the accuracy and utility of aircraft noise data. By conducting two noise measurements near aircraft flight paths, this study sought to understand the impact of location on capturing precise noise information.

Utilizing the SV 200A noise monitoring station from Svantek, designed to meet the IEC 61672-1:2013 Class 1 specification for sound level meters, the experiment compared noise data from two different sites near the same airport. The choice of sites, both in close vicinity to aircraft routes, was critical in evaluating the influence of location on noise measurement accuracy. The SV 200A, equipped for continuous automated noise monitoring and capable of remote data transmission via the SvanNET cloud server, was mounted on a 4-meter mast, ensuring consistent and reliable data collection across both measurement locations.

Leq Directivity Detection

The SV 200A noise monitoring station is adept at tracking noise directivity along both horizontal and vertical planes. It features a central condenser microphone for general sound level measurements, augmented by four additional microphones placed symmetrically around the device’s perimeter. This configuration leverages signal and phase difference techniques to accurately determine the directionality of dominant noise sources across both axes. This innovative method allows for the recording of equivalent continuous sound levels (Leq) across various angular sectors over time, facilitating nuanced data analysis and enhancing reporting capabilities through effective noise source identification and data filtering.

Measurement Settings

The measurement settings have been set to record data containing a 1s time history of LAeq, LAmax, and  1/3 octave analysis, noise directivity in XY and Z directions, and audio recording for listening (24 kHz). The built-in GPS has been used for time synchronization and localization purposes.

Measurement points were located in proximity to an airport in two locations:

  • Measurement point A: near a household and a road, located near the trajectory
  • Measurement point B: in an open field located near the trajectory

The measurements were carried out on different days during the operational time of the airport.

In both cases, the microphone has been located at a height of 4 meters. However, in the first location, the nearest reflections came from the building wall in the proximity of around 3 m from the microphone and a tree distanced around 4 m from the microphone.

the monitoring point A near a household

Figure 3. Location of the monitoring point A near a household. 

the monitoring point B in the open field

Figure 4. Location of the monitoring point B in the open field.

Noise Event extraction from time-history

Following ISO 1996-2:2017, environmental noise measurements require post-processing of measured data. The method described in ISO 20906 distinguishes three stages of data postprocessing: event extraction, event classification, and event identification.

  • Noise Event extraction is based on acoustic criteria such as A-weighted sound pressure levels. Usually, post-processing software offers tools for data searching for a given query, e.g. LAeq above 55 dBA.
  • Noise Event classification is based on additional acoustical information, for example, an event duration, e.g. LAeq above 55 dBA with a minimum duration of 10 s. Modern monitoring systems use information about the direction of the noise to automate the event classification process; in addition to the threshold and minimum duration, events are classified based on the direction of the noise. For example, the noise of an aircraft is expected to come from above the station’s microphone.
  • Noise Event identification uses non-acoustic data such as information from a radar or an operational flight plan from an airport.

During the evaluation of the measurement results, it is necessary to remove unwanted events. Depending on the actual circumstances, different methods of eliminating unwanted sounds can be used. Audio recording is an important tool in the stage of event identification. In the analyzed case, listening to the actual noise helped identify the dog barking as the unwanted noise source that was excluded from further analysis.

noise event extraction

Figure 5. Selection of aircraft passages in point A

selection of aircraft passages

Figure 6. Selection of aircraft passages in point B

Noise Events classification and identification

With the use of SvanPC++ software, aircraft noise passages have been extracted from the time history. During the data analysis in location A, suspicious events were detected. The analysis of directivity showed clearly that a dominant source was above the microphone. Although the shape of slopes differs from aircraft ones, the distance from a background is similar to aircraft events. Listening to the audio recordings allowed us to identify that the source of the noise was a dog barking.

The verification of the noise direction was possible thanks to the SV 200A built-in GPS and embedded Google Maps function in SvanPC++. Figure 7 shows both vertical and horizontal directions where the noise came from, but still, the question remained: how could a dog be above the station located at 4m?  Can dogs fly?

To investigate further, a function of Google Maps Street View has been used. The XY noise directional analysis clearly and further zoom in the Street View mode made it possible to assume that the dog has been running near the gate barking and the noise has been reflected by the roof of the house (Figure 7).

noise directivity identification with Google Maps

Figure 7. Noise directivity identification with SvanPC++ software in a Google Maps Satellite mode

Conclusions

The study conducted proved that the selection of the measurement location has a great effect on the automatization of event identification in measurement data post-processing. The localization of measurement point B in an open field with a non-reflecting surface enabled the automatic and accurate extraction of aircraft passages. The localization of measurement point A, however, by measurement practice standards, caused difficulties due to noise reflections from the building wall located around 3 meters from the microphone.

Tools used in the data post-processing, such as audio recording, GPS localization, and noise directivity, enabled precise event verification and confirmed, at this stage, that dogs can’t fly; it’s the noise that can.

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