# Illegal Logging Detection Based on Acoustic Surveillance of Forest
## 5 Results
In Figure 4, the effect of the smoothing window on the wood logging sound classification performance for the best performing algorithm (i.e., the support vector machines) and for several SNR values is shown in percentages. As we can see in Figure 4 above, the impact of the postprocessing step is significant for all the signal-to-noise ratios and, especially, it is very assistive in the case of a very noisy environment, i.e., where we have low signal-to-noise ratio values. More specifically, we can see that the window length equal to three offers the best performance across all the evaluated signal-to-noise ratio values. In addition, after we employed the postprocessing with \(L=3\), we could see that the achieved  
Figure 4: The classification accuracy (in percentages) of the acoustic wood logging utilizing the postprocessing for the best performing support vector machine (SVM) classifier. classification accuracy was better and improved by almost 1% in terms of absolute improvement for all signal-to-noise ratio values. In the case of a noisy environment (i.e., for SNR value of \(-6\) dB), we could see that performance improvement was up to 2% as compared with the case in which no postprocessing was applied (\(L=1\)). Late-stage fusion of classifiers with postprocessing of the corresponding results was also evaluated. Specifically, there can be logging sound events that are correctly detected by one classifier but not by others. For these cases, the best performing support vector machines classifier can misrecognize a logging sound event, however, it is correctly recognized by another classification algorithm, and the fusion of their recognition outputs can potentially improve performance. To evaluate this, we applied late fusion of the recognized and postprocessed, as described in Figure 4, classifiers' outputs for the three top-performing classification methods, namely the support vector machines, the MLP neural network, and the C4.5 decision tree (J48). Moreover, the late fusion logging sound recognition results, after postprocessing, are illustrated in Figure 5.  
As can be seen in Figure 5, the late fusion of the postprocessed recognition outputs of the three classifiers resulted in further improvement of the logging sound detection accuracy. In particular, the accuracy and the performance for SNR equal to 20 dB was increased by almost 2% to 94.42% as compared with the postprocessed accuracy results of SVM. For the noisy conditions of SNR equal to \(-6\) dB and 0 dB, the improvement, when using late fusion of the postprocessed outputs of the three classifiers, was slightly higher than 2%, resulting in an accuracy equal to 81.88% and 85.03%, respectively. The improvement, in terms of classification accuracy from the late fusion of postprocessed results of the three classifiers, indicates the complementary information carried by the outcomes of the  
Figure 5: The classification accuracy (in percentages) of the acoustic wood logging using late fusion of the postprocessed outputs of the three top-performing classifiers (SVM, support vector machine (MLP), and C4.5 decision tree (J48)). different classification algorithms that were evaluated, despite the overall outperforming accuracy of the support vector machines.