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Research Article Open access CC BY 4.0

Security Detection in Audio Events: A Comparison of Classification Methods

Alissar Nasser

Journal of Advances in Mathematics and Computer Science · pp. 25–41 · Published 14 Mar 2020

10.9734/jamcs/2020/v35i230247

Abstract

The security of public places is becoming important with the increased rate of violence and subversion. Recently, several types of research have been proposed to automatically detect abnormal behavior in public places like a car crash, violence or other hazardous events in an attempt to improve security and save lives. Furthermore, most of the researches are using supervised classifications techniques to classify the audio signals. This paper proposes the use of the kernel principal component analysis (KPCA) to reduce the number of MFCC features extracted from the audio signal and then apply an unsupervised classification algorithm. Moreover, this paper presents the results of several supervised and unsupervised classification methods for audio events detection and compares these results with the result of the proposed approach. Experiments are done using a real data set recorded at the mean of public transportation. The obtained results reveal that K-means on 2 KPCA components gave good results for triggering a true alarm as well as detecting a false alarm; where the percentages of false and missed alarms were 4.5% and 7.8% respectively; whereas these values were 0.8% and 9.3% respectively for kernel k-means. Notwithstanding the DNN network gave the best results with a false alarm rate of 0% and 1.4% missed alarm.

Audio event detection MFCC classification unsupervised supervised kernel PCA K-means DNN kernel Davies and Bouldin index.

References (17)

  1. 1 Learning with Kernels [DOI]
  2. 2 Scream and gunshot detection and localization for audio-surveillance systems [DOI]
  3. 3 End-to-End Audiovisual Speech Recognition [DOI]
  4. 4 Audio-Visual Event Recognition in Surveillance Video Sequences [DOI]
  5. 5 An overview of applications and advancements in automatic sound recognition [DOI]
  6. 6 Automatic Detection and Classification of Audio Events for Road Surveillance Applications [DOI]
  7. 7 An Adaptive Framework for Acoustic Monitoring of Potential Hazards [DOI]
  8. 8 A Multi-Class Audio Classification Method With Respect To Violent Content In Movies Using Bayesian Networks [DOI]
  9. 9 Assessing the performances of different neural network architectures for the detection of screams and shouts in public transportation [DOI]
  10. 10 Sound Detection and Classification for Medical Telesurvey
  11. 11 Exemplar-based speech enhancement for deep neural network based automatic speech recognition [DOI]
  12. 12 Random forest algorithm for improving the performance of speech/non-speech detection [DOI]
  13. 13 The use of kernel methods for audio events detection [DOI]
  14. 14 Kernel PCA as a Visualization Tools for Clusters Identifications [DOI]
  15. 15 Structuring Sport Video through Audio Event Classification [DOI]
  16. 16 Investigating K-means and Kernel K-means Algorithms with Internal Validity Indices for Cluster Identification [DOI]
  17. 17 Music Genre Classification using Machine Learning Techniques [DOI]

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