## 2 Feasibility Analysis of Related Work  
In recent decades, the performance of sound detection system in a good acoustic environment has been significantly improved. However, most sound detection systems are still sensitive to the nature of their input data's acoustic environment, and their performance deteriorates sharply in the presence of degraded sources such as additive noise, linear channel distortion and reverberation. In recent years, experts and scholars have introduced dozens of algorithms to solve these problems, and some methods have achieved some results in their field. However, the audio of different things often has different acoustic characteristics. Generally speaking, specific methods only work on specific things. Generally speaking, the features used in the field of sound detection are divided into the following categories:  
* Basic features;
* Time domain features;
* Frequency domain features;
* Time-frequency domain features;
* Other transform domain features;  
In recent years, neural networks have been used to automatically screen out sound features. Some basic features, such as loudness, tone, duration and timbre, are not applicable in this study. Time domain features like short-term energy, short-term average amplitude, short-term average zero-crossing rate and many other features are highly sensitive to noise. These are also not suitable for this study. Frequency domain features include sound signal short-time spectrum, short-time spectrum critical band vector features, etc. Such features are now often used as a process in more advanced features, rather than used alone. Time-frequency domain features are commonly used in Acoustic Event Classification (AEC) and Auditory Scene Analysis (ASA). They included a spectrogram, spectral map, cochlear map and image features of power distribution of offspring (SPD) [15]. In recent years, the robust texture features [16] based on Gamma filter logarithmic spectrum, and Fusion Fisher Vector features (FFV) [17] are also two-dimensional time-frequency image features. From a perceptual point of view, this kind of method seems to be helpful to this  
Figure 1: Photos of typical trunk borers: (**1**) red palm weevil (Rhynchophorus ferrugineus) [13], (**2**) mountain pine beetle, (**3**) red necked longicorn (Aromia bungii) [14], (**4**) Asian longhorn beetle (Anoplophora glabripennis) and (**5**) citrus longhorn beetle (Anoplophora chinensis) in turn. study. In fact, we also tried some more advanced methods, such as SPD and FFV. However, their detection effect is not good. Their performance is even weaker than the traditional MFCC method in the field of trunk-borer audio detection. This is because the vibration time caused by trunk borers is shorter (as shown in Figure 2), but the duration of the sound event signal is often longer. This kind of advanced method usually abandons frame-based recognition and processes the sound event on the basis of two-dimensional time-frequency images. This is effective for the classification of sound events, such as applause, chair moving, footsteps, phone ringing, or a door slamming in the meeting room environment. However, it is bad for this study. In this study, noise reverberation obscures the sound information of trunk borer to a great extent (as shown in Figure 3), and it is difficult to capture useful information in a long time-range. Other transform domain features include the wavelet domain of the wavelet transform and the cepstrum domain of multiple cepstrum coefficients. In addition, the complex cepstrum domain and the complex signal domain represented by empirical mode decomposition (EMD) and variational mode decomposition (VMD) [18] are also included. In the research, we also tried to use advanced digital signal processing methods such as VMD to reduce noise. The results show that this method is effective in the case of weak noise amplitude (here, we give the VMD parameters: the number of modal components is 8 and the penalty factor is 2000).