Name
Abstract | ||
|---|---|---|
Automatic gunshot detection technology allows incidence response system to counteract the potential of crimes. However, the surveillance systems suffer from various detection problems, such as difficulty in differentiating gunshot, fire work and other similar sounds. To improves the accuracy and reduces processing time, we have proposed hybrid algorithm for automatic detection of gunshots in indoor environment. In the proposed approach, we have used pre-processing steps which filters the input audio signals with a threshold. During pre-processing, the signals having smaller energy than the threshold value are discarded because these low energy signals are normal sound signals. When energy of audio signal is more than the threshold value and deemed ambiguous audio, such signal is forwarded to next step for further processing. The second step of the proposed approach is based on features based algorithm, in which antilog energy features are implemented to increase accuracy. These features extend energy band to easily differentiate between gunshot and normal scream. For classification purpose, SVM, Tree and KNN classifiers are used comparatively to differentiate a classifier which will show more accuracy with minimal computational cost. The proposed approach provides 94.97% accuracy for SVM,92.56% accuracy for KNN classifier, and 91.65% accuracy for Tree classifier. The pre-processing step reduces computational time by 5%, 13.61% and 34.56% for KNN, Tree and SVM classifiers respectively. The pre-processing step in the proposed algorithm requires 5.80% processing time of features based approach to filter an audio signal. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s11042-020-09936-w | MULTIMEDIA TOOLS AND APPLICATIONS |
Keywords | DocType | Volume |
Gunshots, Muzzle Blast, Shook Wave, Audio Energy, Antilog Energy | Journal | 80 |
Issue | ISSN | Citations |
3 | 1380-7501 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sami ur Rahman | 1 | 11 | 3.97 |
| Adnan Khan | 2 | 0 | 0.34 |
| Sohail Abbas | 3 | 13 | 3.62 |
| Fakhre Alam | 4 | 0 | 0.34 |
| Nasir Rashid | 5 | 0 | 0.34 |