Low-Cost Distributed Acoustic Sensor Network for Real-Time Urban Sound Monitoring

Vidaña-Vila, Ester; Navarro, Joan; Borda-Fortuny, Cristina; Stowell, Dan; Alsina-Pagès, Rosa Ma

doi:10.3390/electronics9122119

Cited by 14 publications

(15 citation statements)

References 49 publications

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“…The wavelet components from acoustic data, temperature changes and thermal location from DTS data, low-frequency acoustic signal, spectrogram plot, F-K plot, as well as the mean and variance from a time window of DAS and DTS data, have been used as the main features for modelling distributed fibre optic data with machine learning. The spectrogram plot from acoustic data [97], for example, can provide rich interpretations of different classification schema, depicted in Figure 13. F-K plots, on the other hand, are the representation of the SoS values, which have a strong correlation with the phase-fraction information of multiphase fluid.…”

Section: Feature Engineeringmentioning

confidence: 99%

A Survey on Distributed Fibre Optic Sensor Data Modelling Techniques and Machine Learning Algorithms for Multiphase Fluid Flow Estimation

Arief

Wiktorski

Thomas

2021

Sensors

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Real-time monitoring of multiphase fluid flows with distributed fibre optic sensing has the potential to play a major role in industrial flow measurement applications. One such application is the optimization of hydrocarbon production to maximize short-term income, and prolong the operational lifetime of production wells and the reservoir. While the measurement technology itself is well understood and developed, a key remaining challenge is the establishment of robust data analysis tools that are capable of providing real-time conversion of enormous data quantities into actionable process indicators. This paper provides a comprehensive technical review of the data analysis techniques for distributed fibre optic technologies, with a particular focus on characterizing fluid flow in pipes. The review encompasses classical methods, such as the speed of sound estimation and Joule-Thomson coefficient, as well as their data-driven machine learning counterparts, such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Ensemble Kalman Filter (EnKF) algorithms. The study aims to help end-users establish reliable, robust, and accurate solutions that can be deployed in a timely and effective way, and pave the wave for future developments in the field.

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Section: Feature Engineeringmentioning

confidence: 99%

A Survey on Distributed Fibre Optic Sensor Data Modelling Techniques and Machine Learning Algorithms for Multiphase Fluid Flow Estimation

Arief

Wiktorski

Thomas

2021

Sensors

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“…Deep learning has been applied to urban audio datasets, obtaining encouraging results [15,42]. However, many research studies are limited to datasets that are unrealistic because they are curated from audio libraries rather than real-world urban monitoring, and/or are single-label annotated, neglecting the simultaneous occurrence of sounds [43].…”

Section: Ad Hoc Developed Acoustic Sensor Networkmentioning

confidence: 99%

“…The purpose of this paper is to present an automatic classification system for acoustic events in urban environments able to address the aforementioned challenges. The proposed approach combines and improves (1) the advances of our previous work in the conception of a WASN architecture for single-label classification using physical redundancy of lowcost sensors and synthetically generated audio files [15], and (2) the outlined automatic multilabel classification system for acoustic events that the authors presented in [16]. The resulting system presented in this work features a two-stage classifier that analyzes realworld acoustic frames in real time to distinguish all the events that appear in them-not only on the foreground soundscape but also on the background.…”

mentioning

confidence: 93%

“…The second layer is aimed at aggregating the first-stage classification results from neighboring nodes (i.e., exploiting physical redundancy) to increase the classification reliability of individual sensors. Additionally, the whole system has been designed so it can meet the computing constraints typically found in the potential application domain of this system (i.e., low-cost WASN [15]). In order to assess the classification performance of the presented approach, real-world data have been collected simultaneously at four corners of a traffic intersection in Barcelona.…”

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confidence: 99%

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Multilabel Acoustic Event Classification Using Real-World Urban Data and Physical Redundancy of Sensors

Vidaña-Vila

Navarro

Stowell

et al. 2021

Sensors

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Many people living in urban environments nowadays are overexposed to noise, which results in adverse effects on their health. Thus, urban sound monitoring has emerged as a powerful tool that might enable public administrations to automatically identify and quantify noise pollution. Therefore, identifying multiple and simultaneous acoustic sources in these environments in a reliable and cost-effective way has emerged as a hot research topic. The purpose of this paper is to propose a two-stage classifier able to identify, in real time, a set of up to 21 urban acoustic events that may occur simultaneously (i.e., multilabel), taking advantage of physical redundancy in acoustic sensors from a wireless acoustic sensors network. The first stage of the proposed system consists of a multilabel deep neural network that makes a classification for each 4-s window. The second stage intelligently aggregates the classification results from the first stage of four neighboring nodes to determine the final classification result. Conducted experiments with real-world data and up to three different computing devices show that the system is able to provide classification results in less than 1 s and that it has good performance when classifying the most common events from the dataset. The results of this research may help civic organisations to obtain actionable noise monitoring information from automatic systems.

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“…Before employing a CNN on one-dimensional data, e.g., time series, the data has to be converted using signal processing techniques into a two-dimensional representation in the time-frequency spectrum or using wavelet transforms [22,49,50]. For example, onedimensional signals can be transformed in two-dimensional spectrograms, which in turn can be fed as an image to the CNN.…”

Section: Overview-convolutional Neural Networkmentioning

confidence: 99%

Classifying Multivariate Signals in Rolling Bearing Fault Detection Using Adaptive Wide-Kernel CNNs

2021

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With the developments in improved computation power and the vast amount of (automatic) data collection, industry has become more data-driven. These data-driven approaches for monitoring processes and machinery require different modeling methods focusing on automated learning and deployment. In this context, deep learning provides possibilities for industrial diagnostics to achieve improved performance and efficiency. These deep learning applications can be used to automatically extract features during training, eliminating time-consuming feature engineering and prior understanding of sophisticated (signal) processing techniques. This paper extends on previous work, introducing one-dimensional (1D) CNN architectures that utilize an adaptive wide-kernel layer to improve classification of multivariate signals, e.g., time series classification in fault detection and condition monitoring context. We used multiple prominent benchmark datasets for rolling bearing fault detection to determine the performance of the proposed wide-kernel CNN architectures in different settings. For example, distinctive experimental conditions were tested with deviating amounts of training data. We shed light on the performance of these models compared to traditional machine learning applications and explain different approaches to handle multivariate signals with deep learning. Our proposed models show promising results for classifying different fault conditions of rolling bearing elements and their respective machine condition, while using a fairly straightforward 1D CNN architecture with minimal data preprocessing. Thus, using a 1D CNN with an adaptive wide-kernel layer seems well-suited for fault detection and condition monitoring. In addition, this paper clearly indicates the high potential performance of deep learning compared to traditional machine learning, particularly in complex multivariate and multi-class classification tasks.

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Low-Cost Distributed Acoustic Sensor Network for Real-Time Urban Sound Monitoring

Cited by 14 publications

References 49 publications

A Survey on Distributed Fibre Optic Sensor Data Modelling Techniques and Machine Learning Algorithms for Multiphase Fluid Flow Estimation

A Survey on Distributed Fibre Optic Sensor Data Modelling Techniques and Machine Learning Algorithms for Multiphase Fluid Flow Estimation

Multilabel Acoustic Event Classification Using Real-World Urban Data and Physical Redundancy of Sensors

Classifying Multivariate Signals in Rolling Bearing Fault Detection Using Adaptive Wide-Kernel CNNs

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