###### Abstract
In addition to the efficient sound sampling and transmission network, we identified sound classification as a critical component of urban sound monitoring, which many researchers have widely agreed upon [15; 16; 17]. Effective sound classification is essential for distinguishing various sources and providing valuable insights into urban sound patterns and their associated information. Numerous sound classification systems have been developed, most relying on feature extraction. Feature extraction algorithms typically convert temporal sound signals into time-frequency representations, such as spectrograms and Mel-frequency cepstral coefficients (MFCC), which are then fed into classifiers. This process has been commonly used in many studies [18; 19; 20; 21]. The selection of classifiers has also been well studied, ranging from traditional machine learning models, such as Support Vector Machines (SVM) and Gaussian Mixture Models (GMM), to more advanced deep learning models, such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. Recent studies have explored various combinations of features and deep learning models to enhance classification accuracy. For example, [PERSON] et al. [22] compared Short-Time Fourier Transform (STFT) spectrogram, MFCC, and audio embeddings for indoor-outdoor sound classification, with STFT and ShuffleNet achieving the highest accuracy (93.1%). Similarly, [PERSON] et al. [23] applied CNN-LSTM models with MFCC features for sound classification in [PERSON]'s dementia, achieving superior accuracy compared to other feature extraction methods. [PERSON] et al. [21] demonstrated that CNN-LSTM models utilizing MFCC features outperformed other classifiers, including ResNet18, ShuffleNetV1, and GMM. Despite the high classification accuracies reported in these studies, their robustness under noisy conditions remains a crucial challenge, limiting their effectiveness in real-world urban environments. To overcome these limitations, a novel classification system was proposed that integrates the Smoothed Pseudo Wigner-Ville Distribution (SPWVD) spectrogram with MFCC, referred to as SPWVD-MFCC, as input features [24]. This system leverages a dual-stream ResNet50-CNN-LSTM model designed explicitly for urban sound classification. The approach significantly improved classification accuracy, particularly in noisy environments with low signal-to-noise ratios (SNRs). However, the system's high computational demands led to processing delays, limiting its practicality for real-time edge-based applications, where low latency and efficient resource management are essential. Although numerous studies have aimed to enhance urban sound monitoring systems, several key challenges still need to be addressed. On the one hand, while HWASN has demonstrated excellent scalability, stability, and sound classification potential at a low cost, its performance remains unproven in real-world urban environments, particularly under battery-powered operation without direct access to power sources. Laboratory tests alone cannot fully validate its reliability in such practical settings. On the other hand, although advanced classification models like ResNet50-CNN-LSTM, utilizing SPWVD-MFCC features, have shown superior accuracy and noise robustness, their high computational demands render them unsuitable for real-time deployment on edge devices. This limitation hampers the development of real-time, on-board sound classification. Addressing these challenges requires developing a comprehensive urban sound monitoring system featuring HWASN tested and validated under real-world conditions alongside an optimized sound classification model. Therefore, in this study, we introduce CNN-CNN-BiLSTM-Attention (CCBA), an end-to-end sound classification model optimized for real-time sound recognition on the Jetson Nano.