## 2 Materials and Methods
### The Proposed Model
#### 2.2.3. Deep Learning Framework  
In the field of insect sound classification, a long-standing challenge has been how to accurately extract useful features from complex insect sound recordings for classification. To address this challenge, we conducted a study and introduced the dual-tower network, which comprises two main components: the EfficientNet-b7 module and the \"dual-frequency and spectral fusion module (DFSM)\". In our research, we adopted the EfficientNet-b7 model as the foundational network. Its distinctive network architecture and parameter optimization techniques equip the EfficientNet model with superior feature learning capabilities, enabling it to capture intricate data features efficiently. The design concept of DFSM comes from how the insect brain processes sound signals and the mechanism of the insect auditory system. This module amalgamates some technical elements to achieve efficient audio feature classification. By employing depthwise separable convolutions [37], the model becomes proficient at learning diverse frequency and temporal features. Additionally, the utilization of pooling operations aids in reducing data dimensions while preserving critical information. The incorporation of skip connections fosters interaction and integration among features at different levels, enabling the model to attain a thorough understanding of the complexity of audio signals. Through experimental comparisons with conventional methods, we have demonstrated that the DFSM can improve the accuracy of insect sound classification. The architectural layout of the dual-tower network is illustrated in Figure 5. This research not only introduces an innovative approach to insect sound classification but also imparts valuable insights into the principles of audio feature extraction, offering robust support for future studies in audio classification. Unlike traditional image data processing, for audio transformation using Mel spectrograms, we consider the size in terms of the number of Mel frequency bands multiplied by the number of time steps as the input dimensions (as presented in Table 2). To better adapt to the input of Mel spectrograms, in 'Stage 1', we modify the number of channels to 2, and the output channel count is set to 64, while the remaining parts follow the original framework of EfficientNet-b7. We position the head module at the output layer of EfficientNet-b7, connecting it to the DFSM. In the first convolution layer of both tower1 and tower2, we set the output channel count to 2 and establish a skip connection, leaving the final FC layer with an _\"in_features\"_value of 6. Furthermore, since the _'tower1_pool'_ and _'tower2_pool'_ methods employ 'AdaptiveAvgPool2d' for adaptive average pooling, the dimensions of the feature maps are reduced to 1 in length and width. Figure 4: Effect of SpecAugment: Frequency masking and time masking with horizontal bars and vertical lines. EfficientNet [24] represents a series of convolutional neural network models that rely on automated network scaling techniques. The distinctive feature of it is its network structure, which is determined through an automated search for the optimal configuration. This process involves a delicate balance between complexity and computational