Performance of Deep Neural Networks in Audio Surveillance

Arslan, Yüksel; Canbolat, Huseyin

doi:10.1109/ceit.2018.8751822

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…The comparison details are shown in Table X. Our proposed method performs better than the method from Sammarco et al [8] and Gatto et al [9] but is unable to beat the methods presented by Arslan et al [11].…”

Section: Comparisonmentioning

confidence: 81%

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Design of Audio-Based Accident and Crime Detection and Its Optimization

Pratama

Sukaridhoto

Purnomo

et al. 2023

JOIV : Int. J. Inform. Visualization

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The development of transportation technology is increasing every day; it impacts the number of transportation and their users. The increase positively impacts the economy's growth but also has a negative impact, such as accidents and crime on the highway. In 2018, the number of accidents in Indonesia reached 109,215 cases, with a death rate of 29,472 people, which was mostly caused by the late treatment of the casualties. On the other hand, in the same year, there were 8,423 mugs, and 90,757 snitches cases in Indonesia, with only 23.99% of cases reported. This low reporting rate is mostly caused by the lack of awareness and knowledge about where to report. Therefore, a quick response surveillance system is needed. In this study, an audio-based accident and crime detection system was built using a neural network. To improve the system's robustness, we enhance our dataset by mixing it with certain noises which likely to occur on the road. The system was tested with several parameters of segment duration, bandpass filter cut-off frequency, feature extraction, architecture, and threshold values to obtain optimal accuracy and performance. Based on the test, the best accuracy was obtained by convolutional neural network architecture using 200ms segment duration, 0.5 overlap ratio, 100Hz and 12000Hz as bandpass cut-off frequency, and a threshold value of 0.9. By using mentioned parameters, our system gives 93.337% accuracy. In the future, we hope to implement this system in a real environment.

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Section: Comparisonmentioning

confidence: 81%

“…We compare our proposed method with methods from Sammarco et al [8], Gatto et al [9], and Arslan et al [11] in terms of accuracy. The comparison details are shown in Table X.…”

Section: Comparisonmentioning

confidence: 99%

Design of Audio-Based Accident and Crime Detection and Its Optimization

Pratama

Sukaridhoto

Purnomo

et al. 2023

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

show abstract

“…For their research, authors developed deep neural network (DNN) models for scream and traffic accident recognition. Tests of their models showed that they can be reliably used in real-world applications and in transportation [10].…”

Section: Discussionmentioning

confidence: 99%

Safety in Cities and Transport through Sound Monitoring

Smažinka¹,

Hrinko²

2022

Communications

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This paper deals with the use of modern technology for security purposes, focusing on the situation in cities (urban noise and shooting) and in transport (passenger rail). The authors aimed to highlight the possibilities and capabilities of installation of technology in the field of applied physical acoustics (sensors, evaluation system with the possibility of setting sensitive sensing filters, end output) and the possibilities of monitoring, including the response of security forces or security measures to the detected danger. In the paper, the authors also include graphical representations of the energynormalized spectra of the different sound backgrounds and classification classes of acoustic events and types of acoustic events to clarify the differences of the selected monitored events.

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“…This process is termed Frame by Frame analysis.The audio recordings are sampled at 32 KHz and segmented into frame steps of 75 ms in length. A frame overlapping of 50% of the frame step is implemented (Arslan and Canbolat, 2018). Hamming window technique is employed for dividing the sampled signal into frames using a window length equal to sum of the frame step and frame overlap.…”

Section: Proposed Methodologymentioning

confidence: 99%

Soft computing based audio signal analysis for accident prediction

Valiveti¹,

Kumar²,

Duggineni³

et al. 2021

IJPCC

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Purpose Road accidents, an inadvertent mishap can be detected automatically and alerts sent instantly with the collaboration of image processing techniques and on-road video surveillance systems. However, to rely exclusively on visual information especially under adverse conditions like night times, dark areas and unfavourable weather conditions such as snowfall, rain, and fog which result in faint visibility lead to incertitude. The main goal of the proposed work is certainty of accident occurrence. Design/methodology/approach The authors of this work propose a method for detecting road accidents by analyzing audio signals to identify hazardous situations such as tire skidding and car crashes. The motive of this project is to build a simple and complete audio event detection system using signal feature extraction methods to improve its detection accuracy. The experimental analysis is carried out on a publicly available real time data-set consisting of audio samples like car crashes and tire skidding. The Temporal features of the recorded audio signal like Energy Volume Zero Crossing Rate 28ZCR2529 and the Spectral features like Spectral Centroid Spectral Spread Spectral Roll of factor Spectral Flux the Psychoacoustic features Energy Sub Bands ratio and Gammatonegram are computed. The extracted features are pre-processed and trained and tested using Support Vector Machine (SVM) and K-nearest neighborhood (KNN) classification algorithms for exact prediction of the accident occurrence for various SNR ranges. The combination of Gammatonegram with Temporal and Spectral features of the validates to be superior compared to the existing detection techniques. Findings Temporal, Spectral, Psychoacoustic features, gammetonegram of the recorded audio signal are extracted. A High level vector is generated based on centroid and the extracted features are classified with the help of machine learning algorithms like SVM, KNN and DT. The audio samples collected have varied SNR ranges and the accuracy of the classification algorithms is thoroughly tested. Practical implications Denoising of the audio samples for perfect feature extraction was a tedious chore. Originality/value The existing literature cites extraction of Temporal and Spectral features and then the application of classification algorithms. For perfect classification, the authors have chosen to construct a high level vector from all the four extracted Temporal, Spectral, Psycho acoustic and Gammetonegram features. The classification algorithms are employed on samples collected at varied SNR ranges.

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Performance of Deep Neural Networks in Audio Surveillance

Cited by 7 publications

References 12 publications

Design of Audio-Based Accident and Crime Detection and Its Optimization

Design of Audio-Based Accident and Crime Detection and Its Optimization

Safety in Cities and Transport through Sound Monitoring

Soft computing based audio signal analysis for accident prediction

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