# Illegal Logging Detection Based on Acoustic Surveillance of Forest
## 3 System Framework for Logging Detection using Acoustic Monitoring  
The presented framework for acoustic monitoring of logging in forests is based on a WSN setup of acoustic monitoring stations installed in different locations in a forest. The number of monitoring stations, \(M\) with \(1\leq m\leq M\), can vary, with more stations resulting in more spatial resolution in acoustic monitoring of a given area of a forest. Given that specific locations/areas in a forest are highly suspicious for illegal logging, the forest authorities may select targeted locations to install the monitoring stations, thus minimizing the number of locations. The architecture for logging detection in forests using acoustic monitoring is illustrated in Figure 1.  
As can be seen in Figure 1, the monitoring station has a microphone (which can be expanded to microphone array), a solar panel for energy autonomy, and an antenna for wireless communication with a base station (server). The microphone captures sound events and the acquired audio samples are sent wirelessly to a server for further processing. Any logging sound, at a distance that can be heard, is captured by the microphone, together with additive forest sounds and environmental noise. Regarding the wireless transmission of the acquired audio samples, several technologies can be used. More specifically, based on the special characteristics and parameters of a forest area, data transmission can be performed using Wi-Fi or Zigbee protocols, while in the case of dense vegetation, no direct optical contact, or long distances between the stations, a mobile broadband network can be used. As baseline WSN, we consider \(M\) monitoring stations, with \(1\leq m\leq M\), which transfer the acquired audio data together with any log events to a base server station for further processing. Regarding the server side, the captured audio signal, which is wirelessly transmitted from monitoring station m, is preprocessed and parameterized before being analyzed by machine learning methods for classification to detect logging sounds. The detection is performed using pretrained acoustic models for logging and the classification is binary, i.e., detection of logging sounds or not. Once a logging activity is detected, an alarm is activated to inform forest authorities. This can be  
Figure 1: The overall block diagram of the concept of the logging detection system. done either by direct connection to a forest management/monitoring system and activation of the corresponding alarm or by an automatic phone call or text message to patrolling units. The modular structure of the above architecture allows adaptation of any of its modules, according to the specific needs of a forest management body, without loss of the functionality of the other modules. The audio processing performed at the server station is based on short-time analysis of the acquired recording and decomposition of the signal in sequences of audio feature vectors. In more detail, let us denote as \(x\) the incoming audio signal. Using a window \(w\) of fixed length \(\|w\|\) the audio signal will be segmented to audio frames \(\hat{x}_{i}\), with \(i=1,2,3,\ldots\) and \(\hat{x}_{i}\in\mathbb{R}^{\|w\|}\) and time step between consecutive frames typically being half of the frame length. Audio parameterization is then applied to each of the audio frames \(\hat{x}_{i}\), thus extracting a feature vector \(\hat{v}_{i}\), with \(i=1,\ 2,\ 3,\ldots\) and \(\hat{v}_{i}\in\mathbb{R}^{\|V\|}\) for each audio frame consisting of \(\|\hat{v}_{i}\|=V\) parameters. The sequence of audio feature vectors, \(\hat{v}_{i}\), will then be processed by a machine learning classification model \(G\) in order to assign a binary label, logging or not logging sound, to each of the feature vectors, i.e. \[l_{i}\gets G(\hat{v}_{i}) \tag{1}\]  
where \(l_{i}\), with \(i=1,\ 2,\ 3,\ldots\), is the assigned binary label.