A distributed sensor management for large-scale IoT indoor acoustic surveillance

Hilal, Allaa R.; Sayedelahl, Aya; Tabibiazar, Arash; Kamel, Mohamed S.; Basir, Otman

doi:10.1016/j.future.2018.01.020

Cited by 16 publications

(7 citation statements)

References 34 publications

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“…They used long-range audio features to classify four predefined classes and then localize them. Similarly, Hilal et al [15] used linear discriminate analysis (LDA) and SVM classifiers to identify and localize a set of predefined environmental sound events.…”

Section: B Audio Detection/recognitionmentioning

confidence: 99%

Open Set Audio Recognition for Multi-Class Classification With Rejection

Jleed

Bouchard

2020

IEEE Access

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Most supervised audio recognition systems developed to this point have used a testing set which includes the same categories as the training set database. Such systems are called closed-set recognition (CSR). However, audio recognition in real applications can be more complicated, where the datasets can be dynamic, and novel categories can ceaselessly be detected. Hence, in practice, the usual methods will assign to these novel classes labels which are often incorrect. This work aims to investigate audio open-set recognition (OSR) suitable for multi-classes classification recognition, with a rejection option for classes never seen by the system. A probabilistic calibration of a support vector machine classifier is utilized and formulated under the open-set scenario. For this, it is proposed to apply a threshold technique called peak side ratio (PSR) to the audio recognition task. A candidate label is first examined by a Platt-calibrated support vector machine (SVM) to produce posterior probabilities. The PSR is then used to characterize the distribution of posterior probabilities values. This process helps to determine a threshold in order to reject or accept a particular class. Our proposed method is evaluated on different variations of open sets, using wellknown metrics. Experimental results reveal that our proposed method outperforms previous OSR approaches over a wide range of openness values.

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Section: B Audio Detection/recognitionmentioning

confidence: 99%

Open Set Audio Recognition for Multi-Class Classification With Rejection

Jleed

Bouchard

2020

IEEE Access

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“…Then, in order to discriminate between normal events and anomalous ones, these datasets have to be labeled either manually or automatically. In this context, widely applied algorithms are multi-class Support Vector Machines (SVMs) [15], Bayesian classifiers [16], Neural Networks and Deep Neural Networks, Extreme Learning Machines [17], Gaussian Mixture Models (GMM) [18,19], and Decision Trees [20].…”

Section: Related Workmentioning

confidence: 99%

“…AD algorithms have been adopted also in IoT contexts, by creating more intelligent, reactive and secure environments. Hilal et al [15] present and describe a Sensor Management framework called IntelliSurv. It realize an acoustic surveillance system that follows the pervasive IoT paradigm, being it able to detect and localize anomalous audio events using different kinds of distributed devices: smart sensors for environmental monitoring, and delegate sensors devoted to sensor management, localization and identification of anomalous events.…”

Section: Audio Anomaly Detection In Iot Contextsmentioning

confidence: 99%

IRESE: An intelligent rare-event detection system using unsupervised learning on the IoT edge

Janjua

Vecchio

Antonini

et al. 2019

Engineering Applications of Artificial Intelligence

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In the recent years, a rapid growth of IoT devices has been observed, which in turn results in a huge amount of data produced from multiple sources towards the most disparate cloud platforms or the Internet in general. In a typical cloud-centric approach, the data produced by these devices is simply transmitted over the Internet, for consumption and/or storage. However, with the exponential growth in data production rates, the available network resources are becoming the actual bottleneck of this huge data flowing. Therefore, several challenges are appearing in the coming years, which are mainly related to data transmission, processing, and storage along the so-called cloud-to-thing continuum. In fact, one of the most critical requirements of several IoT applications is low latency, which often hinders raw data consumption to happen at the opposite endpoint with respect to its production. In the context of IoT data stream analytics, for instance, the detection of anomalies or rare-events is one of the most demanding tasks, as it needs prompt detection to increase its significance. In this respect, Fog and Edge Computing seem to be the correct paradigms to alleviate these stringent demands in terms of latency and bandwidth as, by leveraging on re-configurable IoT gateways and smart devices able to support the distribution of the overall computational task, they envisage to liquefy data processing along the way from the sensing device to a cloud endpoint. In this paper, we will present IRESE, that is a rare-event detection system able to apply unsupervised machine learning techniques on the incoming data, directly on affordable gateways located in the IoT edge. Notwithstanding the proposed approach enjoys the benefits of a fully unsupervised learning approach, such as the ability to learn from unlabeled data, it has been tested against various audio rare-event categories, such as gunshot, glass break, scream, and siren, achieving precision and recall measures above 90% in detecting such events.

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“…For pervasive IoT acoustic surveillance, it is necessary to detect and localize abnormal acoustic events in a distributed collaborative manner [18]. In fact, we can find in the literature research works aimed at preventing hazardous situations such as might be indicated by the sound of human screams [19,20] or other kinds of sounds such as gunshots, explosions, machine sounds or children voices, among others [21].…”

Section: Acoustic Source Localization In Iot 21 Sound Source Localizmentioning

confidence: 99%

Practical Considerations for Acoustic Source Localization in the IoT Era: Platforms, Energy Efficiency, and Performance

Belloch

Badía

Igual

et al. 2019

IEEE Internet Things J.

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The rapid development of the Internet of Things (IoT) has posed important changes in the way emerging acoustic signal processing applications are conceived. While traditional acoustic processing applications have been developed taking into account high-throughput computing platforms equipped with expensive multichannel audio interfaces, the IoT paradigm is demanding the use of more flexible and energy-efficient systems. In this context, algorithms for source localization and ranging in wireless acoustic sensor networks can be considered an enabling technology for many IoT-based environments including security, industrial and health-care applications. This paper is aimed at evaluating important aspects dealing with the practical deployment of IoT systems for acoustic source localization. Recent Systems-On-Chip (SoC) composed of low-power multicore processors, combined with a small graphics accelerator (or GPU), yield a notable increment of the computational capacity needed in intensive signal processing algorithms while partially retaining the appealing low power consumption of embedded systems. Different algorithms and implementations over several state-of-the-art platforms are discussed, analyzing important aspects such as the trade-offs between performance, energy efficiency and exploitation of parallelism by taking into account real-time constraints.

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A distributed sensor management for large-scale IoT indoor acoustic surveillance

Cited by 16 publications

References 34 publications

Open Set Audio Recognition for Multi-Class Classification With Rejection

Open Set Audio Recognition for Multi-Class Classification With Rejection

IRESE: An intelligent rare-event detection system using unsupervised learning on the IoT edge

Practical Considerations for Acoustic Source Localization in the IoT Era: Platforms, Energy Efficiency, and Performance

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