## 2 Materials and Methods
### The Proposed Model
#### 2.2.3. Deep Learning Framework
These sounds, produced by these diminutive organisms, serve as a medium of communication, yet they are also influenced by environmental noise and intricate acoustic characteristics. It's in this context that the research team began contemplating whether inspiration could be derived from insect biology to improve the classification of insect sounds. A comprehensive exploration of the auditory organs and systems of insects [39] revealed that they predominantly consist of auditory hairs, Johnston's organs, and tympanic organs. These systems employ a hierarchical approach when processing sound. Insect brains [27] contain distinct groups of neurons, each responsible for processing different  
Figure 6: MBConv Model Structure: BN stands for BatchNormalization, which is used for normalization processing. Swish is used as the activation function, \(1\times 1\) represents the convolution kernel size, s1s2 represents the step size, and dropout represents random discarding, which is used to solve the problem of model overfitting. Figure 7: Squeeze-and-Excitation (SE) Module Illustration. aspects of sound, such as frequency, temporal, and spectral characteristics. This allows insects to efficiently recognize sounds from companions or potential threats while filtering out noisy background sounds. Taking inspiration from this hierarchical processing approach, we designed the DFSM. This module comprises two independent \"towers\". Tower 1 consists of three convolutional layers, activation functions, and pooling layers, which function similarly to an insect's temporal neuron group, focusing on capturing time features. It exhibits multiple dark features, enabling it to keenly discern various sounds. On the other hand, Tower 2 consists of one convolutional layer, an activation function, and a pooling layer; it emulates an insect's spectral-perceiving neuron group, featuring only one or two dark areas in the CAM (class activation mapping) image [41]. It concentrates on capturing subtle differences in sound spectra (as shown in Figure 8), and spectral processing in insects effectively captures the hierarchical nature of insect sound perception. These two towers, along with their skip connections, enable the model to extract audio information from different perspectives, similar to insect neuron groups [39]. Furthermore, we designed a head module to connect EfficientNet and DFSM. The DFSM not only offers efficient feature extraction (hidden in the DNN(deep neural networks) layers and not accessible to the user) but also helps distinguish the time-frequency locations where subtle differences in insect sounds were extracted by the model. The design of this module draws inspiration from insect auditory systems, aiming to blend biology and deep learning to tackle the challenges of insect sound classification. The dual-tower network, as proposed in this paper, standardizes insect sounds during the data preprocessing stage using a dual-channel configuration and a 44,100 Hz sampling rate. Furthermore, we introduce Gaussian noise with a standard deviation of 0.004 to enhance data diversity, ensuring experiment reproducibility with a specific random seed. To fine-tune the dual-tower network, we employ the Adam optimizer and conduct 400 epochs of training. During the training process, the batch size is set to 10, while the learning rate remains at 0.001. All experiments are carried out utilizing an NVIDIA RTX 3070 GPU (NVIDIA, Santa Clara, CA, USA) and an Intel server, thereby fully harnessing computational resources to ensure the stability and reliability of the experiments. These settings and configurations contribute to the good performance of our sound classification tasks. Specific experimental parameters are outlined in Table 3: