A Statistical Approach to Biometric Identity Verification Based on Heart Sounds

“…In [6]- [7], the authors investigated the use of the radio frequency signal, used in the latest generation cellular systems, as a tool for classifying rainfall intensity levels using pattern recognition method. However, in [17] an acoustic rain gauge has been proposed which is able to classify the rainfall levels in 5 classes through rainfall timbre and deep learning techniques, in particular by applying convolutional neural networks [18], [19].…”

Section: Introductionmentioning

confidence: 99%

A CNN-based Differential Image Processing Approach for Rainfall Classification

Avanzato¹,

Beritelli²

2020

Adv. sci. technol. eng. syst. j.

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With the aim of preventing hydro-geological risks and overcoming the problems of current rain gauges, this paper proposes a low-complexity and cost-effective video rain gauge. In particular, in this paper the authors propose a new approach to rainfall classification based on image processing and video matching process employing convolutional neural networks (CNN). The system consists of a plastic shaker, a video camera and a low cost, low power signal processing unit. The use of differential images allows for greater robustness, guaranteeing full background subtraction. As regards precision, speed and ability to adapt to variations in precipitation intensity, the proposed method achieves good performance. In particular, the results obtained from seven classes, ranging from "No rain" to "Cloudburst", applying the Discrete Cosine Transform (DCT) to the differential images on 16x16 sub-blocks show an average accuracy of 75% considering, also, the adjacent missclassification. Furthermore, the analysis of precision and sensitivity parameters yields excellent results. The proposed method is very innovative, in fact, the few studies found in the state of the art use only two classification classes (No rain and Rain), while our method contains seven classification classes and overall delivers very good accuracy performance.

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“…To overcome the limitations present in traditional techniques of classification, recent studies [5]- [9] have adopted advanced neural network approaches and audio identification features [10]- [12]. Further studies have devised rain gauges based on the analysis of rain images to classify rainfall intensity [13]- [16].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Multimodal Rainfall Classification Systems Based on an Audio/Video Dataset

Avanzato¹,

Beritelli²,

Raspanti³

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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In the past few years, there has been an increase in natural disasters due to hydrogeological instability caused by heavy rain. Therefore, to reduce the risk of an imminent occurrence of a disastrous event and reduce the risk to humans, an accurate estimate of the precipitation levels based on advanced machine learning techniques is necessary. In this paper, a new dataset is proposed containing audio/video data recorded via a multimodal rain gauge created ad hoc. The dataset, denominated AVDB-4RC (Audio/Video Database for Rainfall Classification), contains digital audio/video sequences recorded for seven different levels of precipitation intensity. In particular, the database presents a set of audio sequences containing the acoustic timbre produced by the rain and video sequences containing rain videos, both in seven different intensities, i.e., "No rain," "Weak rain," "Moderate rain," "Heavy rain" and "Very heavy rain," "Shower rain" and "Cloudburst rain." For the validation of the dataset, the paper proposes a novel rainfall classification approach based on a video pattern recognition system that uses CNN neural networks. The average classification accuracy is approximately 49% and can reach 75% if the adjacent misclassifications are not considered. Presumably, it is the first open dataset from the new generation acoustic/video rain gauges available for evaluating the estimated rainfall performance. We hope that this new open dataset will encourage a comparison of rainfall estimation/classification algorithms on this common database so that the adopted techniques are objectively assessed and improved.

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A Statistical Approach to Biometric Identity Verification Based on Heart Sounds

Cited by 18 publications

References 22 publications

Human Identity Verification based on Heart Sounds: Recent Advances and Future Directions

Human Identity Verification based on Heart Sounds: Recent Advances and Future Directions

A CNN-based Differential Image Processing Approach for Rainfall Classification

Assessment of Multimodal Rainfall Classification Systems Based on an Audio/Video Dataset

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