# Illegal Logging Detection Based on Acoustic Surveillance of Forest
## 5 Results  
The evaluation of the acoustic detection of logging activity presented in Section 2 was performed based on the experimental implementation presented in Section 3. For all experiments, a common protocol was followed and, in particular, the audio data were split, using 10-fold cross-validation as a means to prevent overlap between the training and the test data. The efficiency and the performance of the tested machine learning methods for binary classification, i.e., for detection of logging sound activity, were tested in terms of their accuracy for different levels of SNR. The experimental results are depicted in Figure 3.  
In Figure 3, we observe that the classification algorithm with the highest performance across all the evaluated SNR levels, from 6 dB to 20 dB values, is the support vector machine algorithm. Specifically, the support vector machine classification algorithm reported a classification accuracy that was equal to 81.65% for a sound-to-noise ratio that was equal to 0 dB, an accuracy of 84.32% for a sound-to-noise ratio that was equal to 12 dB, and an accuracy of 89.45% for a sound-to-noise ratio that was equal to 16 dB, while for the noise-free conditions with SNR = 20 dB the accuracy was equal to 91.07% and dropped to 77.04% when noise increased to SNR = \(-\)6 dB. In general, the two discriminative algorithms, namely the SVM and MLP neural networks, achieved the highest classification accuracy for almost all levels of the SNR. From the results, we see that the accuracy of MLP was approximately 3% lower than that of the support vector machines and was followed by the J48 (i.e., C4.5 decision tree) which had a classification accuracy which was 80.75% and 86.02% for SNR levels 0 dB and 20 dB, respectively. We observed that the IBk algorithm and the Bayes network algorithm did not achieve good or competitive performance. On the basis of the results, it is worth noting that in very noisy conditions, such as when the SNR level is 0 dB or \(-\)6 dB, the C4.5 decision tree method performs well and it is equally effective with the  
Figure 3: The accuracy (in percentages) of the acoustic wood logging classification for various ratios of signal-to-noise and different classification algorithms. support vector machine method. This is a behavior that is in agreement with [28], in which the J48 was also observed to have a good performance. However, regarding the present evaluation, the support vector machine method outperformed all other evaluated machine learning methods regardless of the SNR level. This points out the advantage that support vector machines can offer in forest environments, where the presence of non-stationary interfering noises from the forest environment are widespread. In addition to forest sounds and noises, low levels of signal-to-noise ratio are expected during the acquisition of the audio when the sound source (the wood logging sounds in our case) is not close to the microphone sensors of the monitoring stations set up in the forest. In the next step, we applied a postprocessing sliding window filter to the recognized labels of each frame in order to reduce or remove erroneous sporadic labeling of audio frames, for example, because of a momentary burst of interference, and thus contribute to improving the classification performance. More specifically, during the postprocessing step, we applied a decision-smoothing rule to each frame, \(\hat{o}_{i}\), i.e., when the \(k\) preceding and the \(k\) successive audio frames were classified to one class (either wood logging sound or not), then the current frame was also (re)labeled as of this sound class. The length, \(L\), of the smoothing window was subject to investigation and, in the general case, was set equal to \(L=2\cdot k+1\). The case \(L=1\) corresponded to baseline setup, i.e., without any use of postprocessing of the classified labels.