RaveGuard: A Noise Monitoring Platform Using Low-End Microphones and Machine Learning

Abstract

Urban noise is one of the most serious and underestimated environmental problems. According to the World Health Organization, noise pollution from traffic and other human activities, negatively impact the population health and life quality. Monitoring noise usually requires the use of professional and expensive instruments, called phonometers, able to accurately measure sound pressure levels. In many cases, phonometers are human-operated; therefore, periodic fine-granularity city-wide measurements are expensive. Recent advances in the Internet of Things (IoT) offer a window of opportunities for low-cost autonomous sound pressure meters. Such devices and platforms could enable fine time–space noise measurements throughout a city. Unfortunately, low-cost sound pressure sensors are inaccurate when compared with phonometers, experiencing a high variability in the measurements. In this paper, we present RaveGuard, an unmanned noise monitoring platform that exploits artificial intelligence strategies to improve the accuracy of low-cost devices. RaveGuard was initially deployed together with a professional phonometer for over two months in downtown Bologna, Italy, with the aim of collecting a large amount of precise noise pollution samples. The resulting datasets have been instrumental in designing InspectNoise, a library that can be exploited by IoT platforms, without the need of expensive phonometers, but obtaining a similar precision. In particular, we have applied supervised learning algorithms (adequately trained with our datasets) to reduce the accuracy gap between the professional phonometer and an IoT platform equipped with low-end devices and sensors. Results show that RaveGuard, combined with the InspectNoise library, achieves a 2.24% relative error compared to professional instruments, thus enabling low-cost unmanned city-wide noise monitoring.

Sensors on Internet of Things Systems for the Sustainable Development of Smart Cities: A Systematic Literature Review

“…Since low-cost acoustic pressure sensors are not accurate and measurements are highly variable, Monti et al [70] introduced RaveGuard, an unmanned noise monitoring platform that utilizes artificial intelligence strategies to improve the accuracy of low-cost devices. They first deployed RaveGuard along with professional sound intensity meters in the center of Bologna, Italy, for more than two months to collect a large amount of accurate noise pollution data.…”

“…For example, Schürholz et al [56] IoT system combines air quality data, fire incidents, traffic volume, and user data (geolocation, user ID, pollutant sensitivity, timestamps). Monti et al [70]'s RaveGuard platform consists of a noise pollution sensing system (i.e., a USB condenser microphone mounted on a Raspberry Pi 2 Model B) and an environmental sensing system (i.e., a Canarin II system, which can detect temperature, relative humidity, barometric pressure, and particulate matter, specifically PM 1. 0, 2.5, and 10).…”

IoT-based sound-level control for audio amplifiers: mosques as a case study

“…Shah et al [18] used an IoT platform along with artificial intelligence (AI) to classify noises in urban areas and log them into the cloud to be used when deciding to live in a quiet place. The authors in [19], used an IoT based system along with supervised learning algorithms to monitor noise levels in a city. However, the noise source cannot be identified using their proposed platforms.…”

Evaluation of Clustering Algorithms on GPU-Based Edge Computing Platforms

“…The digital revolution is mainly sustained by two main technological trends: Internet of Things (IoT) and Artificial Intelligence (AI) [7]. The integration of both is mandatory to enable the digital transformation that truly generates benefits for society [8]. AI-enabled IoT (AIoT) brings sensors, machines, cloud-edge computing, analytics, and people together to improve productivity and efficiency, which implies revenue growth and operational efficiency [9].…”