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14.08 Noise Monitoring

GAZEX avalanche control system noise & vibration study
Time: 12:00 pm

Author: Paul Bollard

Abstract ID: 1468

Bollard Acoustical Consultants, Inc. was retained by the Placer County Planning Dept. to quantify noise and vibration levels resulting from the Gazex avalanche control system usage during the winter of 2018-2019.  The primary objective of the monitoring program was to obtain a statistically representative sample of noise and vibration data during Gazex usage for comparison against criteria for potential damage to structures and human hearing.  During the survey period, 75 discrete discharges of Gazex cannons occurred.  Each discharge was monitored at five fixed monitoring sites in the Alpine Meadows residential community.  At the completion of the survey, 1,079 of the possible 1,125 possible data points of interest had successfully been captured.  The results of the surveys indicated that, although noise and vibration levels generated by the Gazex system were elevated to the point of being considered highly annoying to local residents, criteria for damage to hearing and structures were not exceeded during the survey period.

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ConvTasNet-based anomalous noise separation for intelligent noise monitoring
Time: 11:00 am

Author: Han Li

Abstract ID: 2035

Noise pollution has become a growing concern in public health. The availability of low-cost wireless acoustic sensor networks permits continuous monitoring of noise. However, real acoustic scenes are composed of irrelevant sources (anomalous noise) that overlap with monitored noise, causing biased evaluation and controversy. One classical scene is selected in our study. For road traffic noise assessment, other possible non-traffic noise (e.g., speech, thunder) should be excluded to obtain a reliable evaluation. Because anomalous noise is diverse, occasional, and unpredictable in real-life scenes, removing it from the mixture is a challenge. We explore a fully convolutional time-domain audio separation network (ConvTasNet) for arbitrary sound separation. ConvTasNet is trained by a large dataset, including environmental sounds, speech, and music over 150 hours. After training, the scale-invariant signal-to-distortion ratio (SI-SDR) is improved by 11.40 dB on average for an independent test dataset. ConvTasNet is next applied to anomalous noise separation of traffic noise scenes. We mix traffic noise and anomalous noise at random SNR between -10 dB to 0 dB. Separation is especially effective for salient and long-term anomalous noise, which smooth the overall sound pressure level curve over time. Results emphasize the importance of anomalous noise separation for reliable evaluation.

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Status – International Space Station (ISS) Crewmembers’ Noise Exposures
Time: 12:40 pm

Author: Jose Limardo

Abstract ID: 2219

Environmental noise in space vehicles, caused by onboard equipment and crew activities, has generated concerns for crew health and safety since early U.S. space missions. The International Space Station (ISS) provides a unique environment where acoustic conditions can be monitored while crewmembers from the U.S. and their international partners work and live for as long as 6 to 12 consecutive months.  This review of acoustic dosimetry data collected to date reveals that the noise exposure limits of NASA’s stringent noise constraint flight rule have been exceeded in 41% of these dosimetry measurements since ISS Increment 17 (2008), with undefined impacts to crew.  These measurements do not take into account the effects of hearing protection devices worn by the crew.  The purpose of this paper is to provide an update on ISS noise exposure monitoring approaches and hearing conservation strategies that include acoustic dosimetry data collected since the ISS Increment 55 mission (April 2018). Future directions and recommendations for the ISS noise exposure monitoring program will also be presented, including research initiatives aimed at better defining the impact of ISS noise on crew health and performance.

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Exploiting data from the NoiseCapture application for environmental noise measurements with a smartphone
Time: 11:20 am

Author: Juddicaël Picaut

Abstract ID: 2316

NoiseCapture is a smartphone application initially developed as part of a participative approach for environmental noise mapping. After more than 3 years, the database produced from all over the world contributions is considerable (more than 77k contributors, nearly 300k tracks representing about 72 million 1-second measurements, in nearly 200 countries). Beyond the initial objective, other uses of the application have emerged: individually by users for their own needs, by associations of people in charge of the fight against noise pollution, within the framework of educational activities, by researchers for the realization of their own research, by communities to address the subject of noise pollution. As these new applications emerged, the development team of NoiseCapture was led to extend the possibilities of exploitation of these data. Thus, in this paper, we present different possibilities for a user to perform his own data analysis, namely: a local export of data from the smartphone, access to raw data and pre-processed data from the NoiseCapture server, access to formatted GIS layers from OGC standard service. All these methods are enabled thanks to the open source ecosystem, such as Python libraries, R software suite and GIS tools.

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MEMS digital microphone and Arduino compatible microcontroller: an embedded system for noise monitoring
Time: 7:00 am

Author: Felipe Ramos de Mello

Abstract ID: 2557

Noise assessment and monitoring are essential parts of an acoustician's work since it helps to understand the environment and propose better solutions for noise control and urban noise management. Traditionally, equipment to carry out this task is standardized, and, eventually, expensive for the early career professional. This work develops a high-quality (and cost-effective) prototype for an embedded noise monitoring device based upon a digital I2S MEMS microphone and an Arduino compatible microcontroller, named Teensy. Its small size and low power consumption are also advantages designed for the project. The system captures and processes sound in real-time, computes A and C frequency-weighted equivalent sound levels, along with time-weighted instant levels with a logging interval of 125 ms. Part of the software handles the audio environment, while the biquadratic IIR filters present in the Cortex Microcontroller library are responsible for the frequency- and time-weightings — using floating-point for enhanced precision. The prototype results were compared against a Class 2 Sound Level Meter, rendering very similar results for the tested situations, proving a powerful and reliable tool. Improvements and further testing are also being conducted to refine its functioning and characterization. Ultimately, the prototype achieved promising performance, confirming as a solution for noise monitoring.

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Accurate and controled vehicle pass-by noise emission quantification in real life traffic
Time: 11:40 am

Author: Lucille PINEL LAMOTTE

Abstract ID: 3018

Noise emission from individual vehicle largely contributes to city pollution and has serious health impact. The standards towards vehicle manufactures consists in pass-by testing with specific acceleration conditions which are not representative of all real driving. For the 2015/996 EU directive, the vehicle source model is inspired of the preceding pass-by standard with derived data represented the propulsion and rolling noise sources. Anyhow, those sources are underestimated due to driving behavior, aged and modified vehicle, road surface, meteorological conditions… The true data collection of vehicle pass-by would be interesting. Moreover, some of the countries are reflecting on how to fight against those extremely noisy vehicle exceeding noise limit with efficient monitoring systems. This paper presents an innovative tool able to detect, identify and quantify the noise emission of individual pass-by vehicle in real life traffic. It is based on the combination of array and video processing. Compared to the state of the art and thanks to MEMs technology, the system is optimized and designed to quantify the individual noise vehicle emission regarding standard with controlled measurement and accurate processing. If the conditions are not respected to properly qualify the pass-by regarding the system limits, the data are ignored. It aims at constructing large and accurate database useful to determine average noise levels and/or acceptable noise limits per vehicle category.

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Applicability of MEMS microphones for environmental sound level monitoring
Time: 12:20 pm

Author: James Oatley

Abstract ID: 1672

This paper explores the challenges associated with the integration of MEMS microphone technology into IEC 61672 classified or type-approved environmental sound level monitors. A comparison is drawn between MEMS microphones and electret condenser capsule microphones to highlight key performance differences within the technologies, and a basic integration method for both technologies is suggested. A review of the IEC 61672 and type-approval standards is conducted against the suggested integration method for a MEMS microphone; key shortcomings are reported and objectively reviewed. Development trends for MEMS microphones are explored, providing key insights into the progression of the technology against electret condenser capsule microphones. Furthermore, the evolution of environmental sound level monitoring systems is explored with a key focus on networked and sound localisation technology. The importance of MEMS microphones within the evolution of environmental sound level monitoring systems is presented alongside key arguments for the practical suitability of MEMS technology over electret condenser capsule technology.

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Audio recording analysis in an urban park of the city of Milan (Italy)
Time: 12:00 pm

Author: Roberto Benocci

Abstract ID: 2350

A noise monitoring campaign has been performed in an urban park of Milan (Italy) called Parco Nord. The area of study is a large peri-urban park in the northern part of the city, characterized by wooded land rich in biodiversity and exposed to different sources and degrees of anthropogenic disturbances, such as road traffic noise and artificial light. The acoustic environment is rather complex due to the contemporary presence of different noise sources, leading to the difficult task of discriminating them in audio data. Due to these multifactorial characteristics, we evaluated different eco-acoustic indices in the attempt to derive a methodology to evaluate the potential of sound ecology indicators to discriminate the different types of sounds present in medium-large urban parks. Time series of about two-week recordings have been transformed into eco-acoustics indices and statistically analysed. The results show a redistribution of recordings into each cluster associated with different sound components and different period of the day. This allowed the identification of different degree of biophonic and/or anthropogenic activities throughout the day.

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