## 1 Introduction  
As the global population continues to grow, it is estimated that by 2050, over six billion people--70% of the world's population--will reside in urban areas [1]. This rapid urbanization has brought significant challenges, including increased energy consumption, noise pollution, and environmental degradation. Smart cities aim to address these issues by leveraging advanced information and communication technologies (ICT) to optimize municipal services, enhance urban life quality, and improve governance efficiency [2, 3, 4]. Urban sound recognition systems play a critical role in smart cities by analyzing and classifying diverse acoustic environments such as traffic noise, sirens, and human activities. These systems provide essential tools for monitoring noise pollution, ensuring public safety, and fostering sustainable urban development [5]. The Internet of Things (IoT) underpins smart city applications by enabling seamless data transmission between interconnected sensor devices. This ecosystem facilitates essential tasks, including environmental monitoring and urban sound recognition, by providing a foundation of effective sensing, extensive connectivity, and intelligent data analysis [6; 7; 8; 9]. However, the increasing volume of data generated by IoT devices presents challenges related to latency, bandwidth utilization, and scalability when managed through traditional centralized cloud systems. These limitations necessitate more efficient architectures capable of handling real-time data processing. Fog computing has emerged as a solution to these challenges by decentralizing data processing to nodes closer to IoT devices. Unlike cloud computing, which relies on centralized data centers, fog computing reduces latency, optimizes bandwidth usage, and enhances data privacy. This decentralized paradigm is particularly effective for applications such as urban sound recognition, where low-latency responses are critical for real-time noise monitoring and public safety interventions [10; 11]. By bridging the gap between edge and cloud computing, fog computing combines the computational efficiency of localized processing with the scalability of cloud infrastructures. It also provides intermediate nodes with enhanced processing capabilities, enabling the execution of complex operations such as deep-learning-based sound classification [12; 13]. Traditional urban-sound-recognition approaches have relied heavily on feature engineering methods, such as MFCCs, Chroma features, Spectral Contrast, and Zero-Crossing Rate (ZCR). These features were typically processed using machine learning algorithms like Support Vector Machines (SVMs), Random Forests, and k-Nearest Neighbors (k-NN), or statistical models including Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs) [14; 15; 16]. While these methods demonstrated effectiveness in specific scenarios, they struggled to adapt to noisy, real-world soundscapes due to their reliance on handcrafted features. This limitation has driven the adoption of more advanced methods capable of learning directly from raw data. Deep learning models have marked a significant advancement in urban sound recognition by enabling automated feature extraction and substantially improving classification accuracy. CNNs have proven particularly effective for analyzing spectral features of audio data. By employing convolutional layers, CNNs capture local patterns in spectrograms, making them suitable for identifying intricate soundscapes. This capability is further enhanced by pooling layers, which reduce spatial dimensions and focus on the most relevant features, thereby improving computational efficiency. CNNs are well suited for handling large-scale datasets due to their hierarchical feature learning, enabling a robust classification of diverse sound categories [17]. LSTM networks, on the other hand, are designed for processing sequential data and excel in capturing temporal dependencies in acoustic signals.