Detection of Hazardous Road Events From Audio Streams: An Ensemble Outlier Detection Approach

Rovetta, Stefano; Mnasri, Zied; Masulli, Francesco

doi:10.1109/eais48028.2020.9122704

Cited by 10 publications

(5 citation statements)

References 13 publications

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“…The authors of Aurino et al (2014) used OCSVM to detect gunshots from audio signals. OCSVM grouped with DNN used to detect road traffic activities by Rovetta et al (2020). Isolation Forest (IF) was used to detect anomalies in smart audio sensors Antonini et al (2018).…”

Section: Related Workmentioning

confidence: 99%

Detecting anomalies within smart buildings using do-it-yourself internet of things

Barhamgi

Heravi²,

Kariyawasam³

et al. 2022

J Ambient Intell Human Comput

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Detecting anomalies at the time of happening is vital in environments like buildings and homes to identify potential cyber-attacks. This paper discussed the various mechanisms to detect anomalies as soon as they occur. We shed light on crucial considerations when building machine learning models. We constructed and gathered data from multiple self-build (DIY) IoT devices with different in-situ sensors and found effective ways to find the point, contextual and combine anomalies. We also discussed several challenges and potential solutions when dealing with sensing devices that produce data at different sampling rates and how we need to pre-process them in machine learning models. This paper also looks at the pros and cons of extracting sub-datasets based on environmental conditions.

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Section: Related Workmentioning

confidence: 99%

Detecting anomalies within smart buildings using do-it-yourself internet of things

Barhamgi

Heravi²,

Kariyawasam³

et al. 2022

J Ambient Intell Human Comput

View full text Add to dashboard Cite

show abstract

“…Thus, an autoencoder score is used to calculate weights, where the inverse of the reconstruction error is used as a sample weight, so that the least represented classes, and thus the worst reconstructed, receive the highest weights. [14] utilized an ensemble one-class SVM parallel to an MLP network to calculate the anomaly score for audio events. The first-stage classifier, a one-class SVM, yields a binary anomaly score (normal if 1 and anomalous if -1), whereas the MLP score, between 0 and 1, indicates the event class.…”

Section: Related Workmentioning

confidence: 99%

Dealing with Uncertainty in Anomalous Audio Event Detection Using Fuzzy Modeling

Mnasri

Rovetta

Masulli

et al. 2021

Advances in Intelligent Systems and Computing

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Surveillance systems are getting more and more multimodal. The availability of audio motivates a method for anomalous audio event detection (anomalous AED) for road traffic surveillance, which is proposed in this paper. The method is based on combining anomaly detection techniques, such as reconstruction deep autoencoders and fuzzy membership functions. A baseline deep autoencoder is used to compute the reconstruction error of each audio segment. The comparison of this error to a preset threshold provides a primary estimation of outlierness. To account for the uncertainty associated to this decision-making step, a fuzzy membership function composed of an optimistic/upper component and a pessimistic/lower component is used. Evaluation results obtained after defuzzification show that with a careful parameter setting, the proposed membership function improves the performance of the baseline autoencoder for anomaly detection, and yields better or at least similar results than other anomaly detection state-of-the-art methods such as one-class SVM.

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“…As a consequence, rendering both the user and the AI model blind to potential road hazards, which frequently originate from areas outside the current FoV. Researchers also explored the use of audio signal to infer the out-of-view objects (Manori et al 2018;Mizumachi et al 2014;Rovetta, Mnasri, and Masulli 2020), yet these approaches often lack precision in locating the position of upcoming vehicles.…”

Section: Introductionmentioning

confidence: 99%

Segment beyond View: Handling Partially Missing Modality for Audio-Visual Semantic Segmentation

Wu,

Wang,

Dayoub

et al. 2024

AAAI

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Augmented Reality (AR) devices, emerging as prominent mobile interaction platforms, face challenges in user safety, particularly concerning oncoming vehicles. While some solutions leverage onboard camera arrays, these cameras often have limited field-of-view (FoV) with front or downward perspectives. Addressing this, we propose a new out-of-view semantic segmentation task and Segment Beyond View (SBV), a novel audio-visual semantic segmentation method. SBV supplements the visual modality, which miss the information beyond FoV, with the auditory information using a teacher-student distillation model (Omni2Ego). The model consists of a vision teacher utilising panoramic information, an auditory teacher with 8-channel audio, and an audio-visual student that takes views with limited FoV and binaural audio as input and produce semantic segmentation for objects outside FoV. SBV outperforms existing models in comparative evaluations and shows a consistent performance across varying FoV ranges and in monaural audio settings.

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Detection of Hazardous Road Events From Audio Streams: An Ensemble Outlier Detection Approach

Cited by 10 publications

References 13 publications

Detecting anomalies within smart buildings using do-it-yourself internet of things

Detecting anomalies within smart buildings using do-it-yourself internet of things

Dealing with Uncertainty in Anomalous Audio Event Detection Using Fuzzy Modeling

Segment beyond View: Handling Partially Missing Modality for Audio-Visual Semantic Segmentation

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