Enhancing Insect Sound Classification Using Dual-Tower Network: A Fusion of Temporal and Spectral Feature Perception  
[PERSON]  
1 College of Information Engineering, Sichuan Agricultural University, Ya can 625014, China; [EMAIL_ADDRESS] (H.H. ); 2020586@98 tsu.sciau.edu.cn (J.C.); [EMAIL_ADDRESS] (H.C.); [EMAIL_ADDRESS] (B.Z.) [EMAIL_ADDRESS] (Y.H. ); [EMAIL_ADDRESS] (Y.Z.) [EMAIL_ADDRESS] (Y.Z.) [EMAIL_ADDRESS] (Y.Z.) [EMAIL_ADDRESS] (Y.Z.) [EMAIL_ADDRESS] (Y.H.) [EMAIL_ADDRESS] (Y.H.) [EMAIL_ADDRESS] (Y.Z.) [EMAIL_ADDRESS] (Y.  
method is the analysis of insect acoustic signals. Insect acoustic analysis [5], as a pivotal tool in biological control, provides a non-invasive [6] and highly efficient [7] means of monitoring and identifying various insect species. The \"insect-against-insect\" strategy stands as a crucial element of biological pest control, employing native predatory insects from the ecosystem as biological control agents [8] to curtail both the population and the damage inflicted by crop pests. In this approach, sound assumes the role of a communication method within the realm of biological pest control. On one hand, some predatory insects emit sound signals featuring specific frequencies and amplitudes [9] to allure pest insects, enticing them into the predator's territory for an effective ambush, consequently reducing the pest population. On the other hand, some predatory insects imitate the mating or egg-laying sounds of pest insects to divert them away from their habitual reproductive and egg-laying locations [10], disrupting their conventional reproductive behavior. This action diminishes the pests' reproductive success and, in turn, alleviates their adverse impact on crops. Despite the substantial theoretical potential of the \"insect-against-insect\" strategy, its practical application encounters a multitude of challenges. These challenges encompass the comprehension of acoustic communication mechanisms [11] between pests and their natural adversaries and integrating this understanding within the specific environmental conditions of agricultural ecosystems. This involves factors such as sound frequency, amplitude, the significance of sounds, and the physiological and ecological contexts of sound production. At the same time, it is also crucial to consider the distinctive characteristics of diverse agricultural ecosystems. In addition to these considerations, continuous enhancements in technical tools and methods are of paramount importance for accurately monitoring and identifying sound signals. Acoustic analysis requires highly sensitive sensors [12] and precise analytical tools [13]. Recording equipment must be capable of capturing the subtle sound signals exchanged between pests and their natural enemies. Moreover, deep learning techniques offer increased accuracy and operability for sound analysis, helping researchers to improve the implementation of the \"insect-against-insect\" strategy. By employing a probabilistic neural network (PNN) trained on these features, a viable scheme to identify insect sounds automatically is demonstrated by [PERSON] [14] using sound parameterization techniques. Drawing inspiration from this work, we incorporate Mel-Scale transformations to characterize insect sounds, enhancing our processing methods. [PERSON] [15] proposes a novel insect sound recognition system using an enhanced spectrogram and convolutional neural network. Leveraging these insights, we devised the dual-frequency and spectral fusion module (DFSM) to bolster our insect species classification efforts. Ongoing improvements in this technology hold the potential to advance the field of sound analysis, enabling farmers and ecologists to gain a deeper understanding of the dynamic changes in insect populations. This, in turn, facilitates the development of targeted pest management strategies and propels research and applications in the field of biological pest control, with broad potential for applications in agriculture, forestry, and ecology.