# Fusion Classification Method Based on Audiovisual Information Processing
## 1 Introduction  
A growing number of researchers are directing their attention towards the field of object classification due to its excellent potential for practical applications. Autonomous driving [1], for instance, facilitates vehicles in making accurate driving decisions through the detection and classification of traffic signs. Image retrieval [2] is enhanced by identifying and classifying image content, resulting in improved retrieval efficiency. Security monitoring [3] involves the process of detecting, recognizing, and categorizing individuals or items that are deemed suspicious to ensure public safety. Medical imaging [4] is used by clinicians to classify and diagnose lesions. Agricultural applications [5] involve the identification and classification of field crops, as well as the detection of the growth of crops. With the continuous development of machine learning, single-modal target classification technologies have also advanced rapidly. Deep learning has revolutionized the field of target classification through its end-to-end feature learning mechanism. In image classification, Convolutional Neural Networks (CNNs) [6] automatically extract hierarchical features (from edge textures to semantic information) through multiple layers of non-linear transformations. Models such as ResNet [7], EfficientNet [8], and VGG [9] have surpassed human classification accuracy on the ImageNet dataset, significantly improving classification efficiency. In the field of sound classification, end-to-end models based on Recurrent Neural Networks (RNNs) and Transformers (such as WaveNet [10]) can directly learn the temporal dependencies from raw audio signals, exhibiting stronger capabilities in sound classification. The core breakthrough of deep learning lies in eliminating the bottleneck of manual feature design by obtaining more essential feature representations through data-driven methods and improving overall performance by jointly optimizing feature extraction and classification processes. However, single-modal deep learning target classification has significant limitations. First, its semantic representation is incomplete, as a single modality can only capture partial attributes of the target. Second, it is highly sensitive to the environment, with a single modality being susceptible to interference from other data sources (e.g., visual models affected by lighting changes, speech models sensitive to background noise). Third, it has weak generalization with small sample sizes, and model performance significantly degrades when data for a single modality is scarce. The rapid development of multimodal fusion technology [11; 12; 13] can be attributed to several key factors. First, multimodal fusion technology enables a more accurate description of the same entity through different modalities. Second, multimodal fusion technology can fully utilize the correlated information between modalities when classifying objects, thus complementing each other to classify the same object. Third, in multimodal fusion technology, if one modality's information is missing or incomplete, the robust nature of multimodal fusion ensures that the system remains stable and operates without being affected. Multimodal fusion effectively overcomes the bottleneck of single-modal classification by leveraging the synergistic complementarity of heterogeneous data. Firstly, joint learning of vision and hearing can mutually correct environmental interference (e.g., using emotional features to compensate for noise in speech classification [14]). Secondly, multimodal pre-training constructs a unified semantic space through cross-modal alignment, enhancing fine-grained classification capabilities (e.g., combining pathological images and clinical text to improve disease diagnosis accuracy [15]). Moreover, through feature sharing between modalities (e.g., fusion of infrared and visible light images [16]), the reliance on labeled data from a single modality is reduced.