Spatial Statistical Analysis of Urban Noise Data from a WASN Gathered by an IoT System: Application to a Small City

Segura-García, Jaume; Pérez-Solano, Juan J.; Serrano, Máximo Cobos; Navarro, Enrique A.; Felici-Castell, Santiago; Asensi, Antonio Soriano; Suay, Francisco Montes

doi:10.3390/app6120380

Cited by 44 publications

(33 citation statements)

References 21 publications

(24 reference statements)

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“…In Table 2 you can see an average of these 2 average times, in order to have a baseline of a generic PC performance calculating the general psycho-acoustic annoyance indicator, PA, and compare it with an IoT device, such as the Raspberry Pi. The Raspberry Pi platform was used to estimate the average computing time in a real node, as it is a device widely used in WASN applications [8,13,19]. The specifications are: We used 1000 audio clips of one second duration to carry out the computing tests.…”

Section: Signal Processing and Computing Timementioning

confidence: 99%

“…A problem arising from the use of such parameters is the significant computing time required, which presently does not allow calculation of them in real time. To lower the computing time for IoT-based systems, different approaches have been developed [8,12,13], but no one was efficient enough to develop an autonomous real-time application for a WASN.…”

Section: Introductionmentioning

confidence: 99%

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Computation of Psycho-Acoustic Annoyance Using Deep Neural Networks

et al. 2019

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Psycho-acoustic parameters have been extensively used to evaluate the discomfort or pleasure produced by the sounds in our environment. In this context, wireless acoustic sensor networks (WASNs) can be an interesting solution for monitoring subjective annoyance in certain soundscapes, since they can be used to register the evolution of such parameters in time and space. Unfortunately, the calculation of the psycho-acoustic parameters involved in common annoyance models implies a significant computational cost, and makes difficult the acquisition and transmission of these parameters at the nodes. As a result, monitoring psycho-acoustic annoyance becomes an expensive and inefficient task. This paper proposes the use of a deep convolutional neural network (CNN) trained on a large urban sound dataset capable of efficiently predicting psycho-acoustic annoyance from raw audio signals continuously. We evaluate the proposed regression model and compare the resulting computation times with the ones obtained by the conventional direct calculation approach. The results confirm that the proposed model based on CNN achieves high precision in predicting psycho-acoustic annoyance, predicting annoyance values with an average quadratic error of around 3%. It also achieves a very significant reduction in processing time, which is up to 300 times faster than direct calculation, making CNN designed a clear exponent to work in IoT devices.

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Section: Signal Processing and Computing Timementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computation of Psycho-Acoustic Annoyance Using Deep Neural Networks

et al. 2019

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“…al in [12] describes a distributed noise monitoring system and the practical application of a geo-statistical methodology for statistical spatial-temporal prediction of noise levels in semi-open areas, such as in a typical, small Mediterranean city. The authors in [12] also developed geo-statistical time model that allows the estimation of specific noise levels and characterization of the spatial-temporal variation of the noise pollution. The results confirm usability of the model as a good approximation of the experimental measurements.…”

Section: Related Workmentioning

confidence: 99%

Accurate Indoor Sound Level Measurement on a Low-Power and Low-Cost Wireless Sensor Node

Risojević¹,

Rozman²,

Pilipović³

et al. 2018

Preprint

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Wireless sensor networks can provide a cheap and flexible infrastructure to support the measurement of noise pollution. However, the processing of the gathered data is challenging to implement on resource-constrained nodes, because each node has its own limited power supply, low-performance and low-power micro-controller unit and other limited processing resources, as well as limited amount of memory. We propose a sensor node for monitoring of indoor ambient noise. The sensor node is based on a hardware platform with limited computational resources and utilizes a number of simplifications to approximate more complex and costly signal processing stage. Furthermore, to reduce the communication between the sensor node and a sink node, as well as the power consumed by the IEEE 802.15.4 (ZigBee) transceiver, we perform digital A-weighting filtering and non-calibrated calculation of the sound pressure level on the node. According to experimental results, the proposed sound level meter can accurately measure the noise levels of up to 100 dB, with the mean difference of less than 2 dB compared to Class 1 sound level meter. The proposed device can continuously monitor indoor noise for several days. Despite the limitations of the used hardware platform, the presented node is a promising low-cost and low-power solution for indoor ambient noise monitoring.

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“…In [15], a distributed noise measurement system based on IoT technology was developed. The sensor node is based on a Raspberry Pi with an electret omnidirectional microphone and a sound card in order to record the audio.…”

Section: Introductionmentioning

confidence: 99%

A Low Cost Wireless Acoustic Sensor for Ambient Assisted Living Systems

et al. 2017

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Ambient Assisted Living (AAL) has become an attractive research topic due to growing interest in remote monitoring of older people. Development in sensor technologies and advances in wireless communications allows to remotely offer smart assistance and monitor those people at their own home, increasing their quality of life. In this context, Wireless Acoustic Sensor Networks (WASN) provide a suitable way for implementing AAL systems which can be used to infer hazardous situations via environmental sounds identification. Nevertheless, satisfying sensor solutions have not been found with the considerations of both low cost and high performance. In this paper, we report the design and implementation of a wireless acoustic sensor to be located at the edge of a WASN for recording and processing environmental sounds which can be applied to AAL systems for personal healthcare because it has the following significant advantages: low cost, small size, audio sampling and computation capabilities for audio processing. The proposed wireless acoustic sensor is able to record audio samples at least to 10 kHz sampling frequency and 12-bit resolution. Also, it is capable of doing audio signal processing without compromising the sample rate and the energy consumption by using a new microcontroller released at the last quarter of 2016. The proposed low cost wireless acoustic sensor has been verified using four randomness tests for doing statistical analysis and a classification system of the recorded sounds based on audio fingerprints.

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Spatial Statistical Analysis of Urban Noise Data from a WASN Gathered by an IoT System: Application to a Small City

Cited by 44 publications

References 21 publications

Computation of Psycho-Acoustic Annoyance Using Deep Neural Networks

Computation of Psycho-Acoustic Annoyance Using Deep Neural Networks

Accurate Indoor Sound Level Measurement on a Low-Power and Low-Cost Wireless Sensor Node

A Low Cost Wireless Acoustic Sensor for Ambient Assisted Living Systems

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