Audio surveillance of roads using deep learning and autoencoder-based sample weight initialization

Mnasri, Zied; Rovetta, Stefano; Masulli, Francesco

doi:10.1109/melecon48756.2020.9140594

Cited by 11 publications

(7 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…outliers, amongst sustained/normal events. [9] addressed the problem of sparse data of the event of interest by balancing the weights of the different classes of audio events. 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.…”

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…Alternative approaches have achieved state-of-the-art performance using either RNNs [16], a combination of CNN and RNN architectures [17] or Transformers [18]. Consequently, SED using deep learning has a number of current and potential applications, including road surveillance [19], human activity monitoring [20], music genre recognition [21], smart wearables and hearables, health care and autonomous navigation [8].…”

Section: Related Workmentioning

confidence: 99%

Audio-Based Aircraft Detection System for Safe RPAS BVLOS Operations

Harana¹,

Alarcón²,

González³

et al. 2020

Preprint

View full text Add to dashboard Cite

For the Remotely Piloted Aircraft Systems (RPAS) market to continue its current growth rate, cost-effective "Detect and Avoid" systems which enable safe beyond visual line of sight (BVLOS) operations are critical. We propose an audio-based "Detect and Avoid" system, composed of microphones and an embedded computer, which performs real-time inferences using a sound event detection (SED) deep learning model. Two state-of-the-art SED models, YAMNet and VGGish, are fine-tuned using our aircraft sounds dataset and their performances are compared for a wide range of configurations. YAMNet, whose MobileNet architecture is designed for embedded applications, outperformed VGGish both in terms of aircraft detection and computational performance. YAMNet's optimal configuration, with > 70% true positive rate and precision, results from combining data augmentation and undersampling with the highest available inference frequency (i.e. 10 Hz). While our proposed "Detect and Avoid" system already allows the detection of small aircraft from sound in real time, a larger dataset, sensor fusion, or the use of cloud-based services for remote computations could further improve its performance.

show abstract

“…Alternative approaches have achieved state-of-the-art performance using either RNNs [20], a combination of CNN and RNN architectures [21], or Transformers [22]. Consequently, SED using deep learning has a number of current and potential applications, including road surveillance [23], human activity monitoring [24], music genre recognition [25], smart wearables and hearables, health care, and autonomous navigation [12].…”

Section: Related Workmentioning

confidence: 99%

Audio-Based Aircraft Detection System for Safe RPAS BVLOS Operations

Harana¹,

Alarcon²,

González³

et al. 2020

Preprint

View full text Add to dashboard Cite

For the Remotely Piloted Aircraft Systems (RPAS) market to continue its current growth rate, cost-effective "Detect and Avoid" systems that enable safe beyond visual line of sight (BVLOS) operations are critical. We propose an audio-based "Detect and Avoid" system, composed of microphones and an embedded computer, which performs real-time inferences using a sound event detection (SED) deep learning model. Two state-of-the-art SED models, YAMNet and VGGish, are fine-tuned using our dataset of aircraft sounds and their performances are compared for a wide range of configurations. YAMNet, whose MobileNet architecture is designed for embedded applications, outperformed VGGish both in terms of aircraft detection and computational performance. YAMNet's optimal configuration, with > 70% true positive rate and precision, results from combining data augmentation and undersampling with the highest available inference frequency (i.e. 10 Hz). While our proposed "Detect and Avoid" system already allows the detection of small aircraft from sound in real time, additional testing using multiple aircraft types is required. Finally, a larger training dataset, sensor fusion, or remote computations on cloud-based services could further improve system performance.

show abstract

Audio surveillance of roads using deep learning and autoencoder-based sample weight initialization

Cited by 11 publications

References 15 publications

Dealing with Uncertainty in Anomalous Audio Event Detection Using Fuzzy Modeling

Dealing with Uncertainty in Anomalous Audio Event Detection Using Fuzzy Modeling

Audio-Based Aircraft Detection System for Safe RPAS BVLOS Operations

Audio-Based Aircraft Detection System for Safe RPAS BVLOS Operations

Contact Info

Product

Resources

About