近年來，環境聲音分類（Environmental Sound Classification, ESC）逐漸受到學術界的廣泛關注。然而，由於環境聲音源的多樣性與複雜性——其受眾多參數驅動——使得聲音分類成為一項頗具挑戰性的任務。與此同時，推薦系統已成為現代數位平臺（如電子商務、社交媒體、流媒體服務、音樂應用程式和學術搜尋引擎）的核心組成部分。這些系統通常採用二分圖結構來建模使用者與物品之間的關係。在本研究中，環境音訊信號被轉換為圖像形式（如頻譜圖），從而可以利用卷積神經網路進行基於圖像的聲音事件分類。該方法充分發揮了視覺模式識別的優勢，更有效地提取了環境聲音的時域和頻域特徵。 借助強大的結構化資料處理能力和高階資訊建模能力，圖神經網路（Graph Neural Networks, GNNs），特別是圖卷積網路（Graph Convolutional Networks, GCNs）已成為眾多推薦問題中的先進解決方案。GCN能夠有效融合結構關係（例如使用者-物品交互）與節點特徵（例如使用者偏好和物品屬性），從而實現更為準確和個性化的推薦。在本研究中，還引入了眼動追蹤技術，用於分析使用者的視覺注意模式，從而在圖結構中增強使用者偏好行為的建模。為應對超參數調整與模型優化的挑戰，本研究採用簡化群體演算法（Simplified Swarm Optimization, SSO）對卷積神經網路和二分圖卷積網路的超參數進行優化。通過在圖像分類任務（基於ESC資料集生成的頻譜圖）和圖結構推薦任務上的大量實驗驗證，SSO訓練的模型在性能和準確率方面均顯著優於過去傳統的多數研究方法。這些結果突顯了所提出的基於SSO的優化框架在電腦視覺和圖結構資料處理等多個深度學習領域中的有效性、適應性和廣泛適用性。

Noise is an ever-present aspect of daily life, and ambient sounds play a crucial role in a variety of domains, including urban intelligence, spatial localization, security monitoring, mechanical auditory systems, and ecological observation. In recent years, Environmental Sound Classification (ESC) has garnered increasing attention in the academic community. However, the diversity and complexity of environmental sound sources—driven by numerous parameters—make sound classification a particularly challenging task. Simultaneously, recommendation systems have become integral to modern digital platforms, such as e-commerce, social media, streaming services, music applications, and academic search engines. These systems commonly employ bipartite graphs to model relationships between users and items.
In this study, environmental audio signals are converted into image representations (e.g., spectrograms), allowing the use of convolutional neural networks for image-based classification of sound events. This approach leverages the strengths of visual pattern recognition to better capture the temporal and spectral features of environmental sounds. With their strong capacity to process structured data and capture high-order interactions, Graph Neural Networks (GNNs)—particularly Graph Convolutional Networks (GCNs)—have become state-of-the-art solutions for many recommendation problems. GCNs effectively integrate structural relationships (e.g., user-item interactions) with node features (e.g., user preferences and item attributes), enabling more accurate and personalized recommendations. In our research, eye-tracking technology is incorporated to analyze visual attention patterns and enhance the modeling of user preference behaviors within the graph structure.
To address the challenge of hyperparameter tuning and model optimization, this study optimizes the hyperparameters of both convolutional neural networks and bipartite GCNs by Simplified Swarm Optimization algorithm. Through extensive experiments on both image classification tasks (using spectrograms derived from ESC datasets) and graph-based recommendation tasks, the models trained via SSO demonstrate consistently high performance and improved accuracy compared to traditional research methods. These results highlight the effectiveness, adaptability, and broad applicability of the proposed SSO-based optimization framework across distinct deep learning domains, including computer vision and graph-structured data processing.

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