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. 2018 Jun 6;18(6):1858.
doi: 10.3390/s18061858.

Automatic Detection and Classification of Audio Events for Road Surveillance Applications

Noor Almaadeed  1 , Muhammad Asim  2 , Somaya Al-Maadeed  3 , Ahmed Bouridane  4 , Azeddine Beghdadi  5
Affiliations

Automatic Detection and Classification of Audio Events for Road Surveillance Applications

Noor Almaadeed et al. Sensors (Basel). .

Abstract

This work investigates the problem of detecting hazardous events on roads by designing an audio surveillance system that automatically detects perilous situations such as car crashes and tire skidding. In recent years, research has shown several visual surveillance systems that have been proposed for road monitoring to detect accidents with an aim to improve safety procedures in emergency cases. However, the visual information alone cannot detect certain events such as car crashes and tire skidding, especially under adverse and visually cluttered weather conditions such as snowfall, rain, and fog. Consequently, the incorporation of microphones and audio event detectors based on audio processing can significantly enhance the detection accuracy of such surveillance systems. This paper proposes to combine time-domain, frequency-domain, and joint time-frequency features extracted from a class of quadratic time-frequency distributions (QTFDs) to detect events on roads through audio analysis and processing. Experiments were carried out using a publicly available dataset. The experimental results conform the effectiveness of the proposed approach for detecting hazardous events on roads as demonstrated by 7% improvement of accuracy rate when compared against methods that use individual temporal and spectral features.

Keywords: car crashes; event detection; hazardous events; tire skidding; visual surveillance.

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Conflict of interest statement

The authors declare that there are no conflicts of interest.

Figures

Figure 1
Figure 1
Proposed methodology for detecting acoustic anomalies. The input signal is subdivided into small frames, and features are extracted in the time, frequency, and joint time-frequency domains. The highest ranked features among the computed features are selected.
Figure 2
Figure 2
Time-frequency (TF) approach for pattern classification.
Figure 3
Figure 3
Time, frequency, and TF representations of a Background noise (BN) segment (1st row), a Car Crash (CC) sound (2nd row), and a Tire Skidding (TS) sound (3rd row). The TF representations were generated using the extended modified-B distribution (EMBD) with as σ = 0.9 and β = 0.01, with a lag window length of 355.
Figure 4
Figure 4
Comparison of performance results.
Figure 5
Figure 5
Illustration of higher entropy and flux measures for event (a) CC than event (b) TS.
Figure 6
Figure 6
Receiver operating characteristic (ROC) curves of the proposed system configured with Mel-frequency cepstral coefficient (MFCC) features.
See this image and copyright information in PMC

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