Sound Event Detection in Underground Parking Garage Using Convolutional Neural Network

Ciaburro, Giuseppe

doi:10.3390/bdcc4030020

Cited by 37 publications

(23 citation statements)

References 34 publications

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“…Classification results by different classifiers are presented in Table 4 , which shows the accuracy of the algorithms used. Accuracy can easily be defined as

, where TP is the true positive rate, and TN is the true negative rate (TN = 1 − FP) [ 80 ]. Ensemble bagged trees (EBT) outperform all other algorithms with 97% accuracy.…”

Section: Resultsmentioning

confidence: 99%

“…In several studies, CNNs classify spectrograms for musical onset detection, classification of acoustic scenes and events, emotion recognition, or identification of dangerous situations in underground car parking to activate an automatic alert from sound [ 80 , 81 , 82 , 83 , 84 ]. Convolutional neural networks (CNNs) have become popular in machine learning research.…”

Section: Methodsmentioning

confidence: 99%

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Classification of the Acoustics of Loose Gravel

Saeed

Nyberg

Alam

et al. 2021

Sensors

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Road condition evaluation is a critical part of gravel road maintenance. One of the assessed parameters is the amount of loose gravel, as this determines the driving quality and safety. Loose gravel can cause tires to slip and the driver to lose control. An expert assesses the road conditions subjectively by looking at images and notes. This method is labor-intensive and subject to error in judgment; therefore, its reliability is questionable. Road management agencies look for automated and objective measurement systems. In this study, acoustic data on gravel hitting the bottom of a car was used. The connection between the acoustics and the condition of loose gravel on gravel roads was assessed. Traditional supervised learning algorithms and convolution neural network (CNN) were applied, and their performances are compared for the classification of loose gravel acoustics. The advantage of using a pre-trained CNN is that it selects relevant features for training. In addition, pre-trained networks offer the advantage of not requiring days of training or colossal training data. In supervised learning, the accuracy of the ensemble bagged tree algorithm for gravel and non-gravel sound classification was found to be 97.5%, whereas, in the case of deep learning, pre-trained network GoogLeNet accuracy was 97.91% for classifying spectrogram images of the gravel sounds.

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“…Classification results by different classifiers are presented in Table 4 , which shows the accuracy of the algorithms used. Accuracy can easily be defined as

, where TP is the true positive rate, and TN is the true negative rate (TN = 1 − FP) [ 80 ]. Ensemble bagged trees (EBT) outperform all other algorithms with 97% accuracy.…”

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Classification of the Acoustics of Loose Gravel

Saeed

Nyberg

Alam

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Machine Learning-based algorithms autonomously develop their knowledge thanks to the data patterns received, without the need to have specific initial inputs from the developer [78]. In these models, the machine can establish by itself the patterns to follow to obtain the desired result, therefore, the real factor that distinguishes artificial intelligence is autonomy.…”

Section: Machine Learning Methods For Time Series Data Analysismentioning

confidence: 99%

Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review

Ciaburro

Iannace

2021

Data

Self Cite

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To predict the future behavior of a system, we can exploit the information collected in the past, trying to identify recurring structures in what happened to predict what could happen, if the same structures repeat themselves in the future as well. A time series represents a time sequence of numerical values observed in the past at a measurable variable. The values are sampled at equidistant time intervals, according to an appropriate granular frequency, such as the day, week, or month, and measured according to physical units of measurement. In machine learning-based algorithms, the information underlying the knowledge is extracted from the data themselves, which are explored and analyzed in search of recurring patterns or to discover hidden causal associations or relationships. The prediction model extracts knowledge through an inductive process: the input is the data and, possibly, a first example of the expected output, the machine will then learn the algorithm to follow to obtain the same result. This paper reviews the most recent work that has used machine learning-based techniques to extract knowledge from time series data.

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“…Deep-learning technology has developed rapidly and made great success in object detection [ 61 ], intelligent robot [ 62 ], saliency detection [ 63 ], parking garage sound event detection [ 64 ], sound event detection for smart city safety [ 65 , 66 ], UAV blade fault diagnosis [ 67 , 68 , 69 ] and other fields [ 70 , 71 , 72 ]. Deep learning has a kind of deep neural network structure with multiple convolutions layer.…”

Section: Survey Of Deep-learning Defect-detection Technologiesmentioning

confidence: 99%

Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges

et al. 2020

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The detection of product defects is essential in quality control in manufacturing. This study surveys stateoftheart deep-learning methods in defect detection. First, we classify the defects of products, such as electronic components, pipes, welded parts, and textile materials, into categories. Second, recent mainstream techniques and deep-learning methods for defects are reviewed with their characteristics, strengths, and shortcomings described. Third, we summarize and analyze the application of ultrasonic testing, filtering, deep learning, machine vision, and other technologies used for defect detection, by focusing on three aspects, namely method and experimental results. To further understand the difficulties in the field of defect detection, we investigate the functions and characteristics of existing equipment used for defect detection. The core ideas and codes of studies related to high precision, high positioning, rapid detection, small object, complex background, occluded object detection and object association, are summarized. Lastly, we outline the current achievements and limitations of the existing methods, along with the current research challenges, to assist the research community on defect detection in setting a further agenda for future studies.

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Sound Event Detection in Underground Parking Garage Using Convolutional Neural Network

Cited by 37 publications

References 34 publications

Classification of the Acoustics of Loose Gravel

Classification of the Acoustics of Loose Gravel

Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review

Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges

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