Sound event detection (SED) plays a pivotal role in applications ranging from surveillance to healthcare, yet achieving high accuracy in dynamic acoustic environments remains challenging. This paper proposes an enhanced framework for SED by integrating the You Only Hear Once (YOHO) Convolutional Neural Network (CNN) architecture with a Bottleneck Attention Module (BAM) to improve feature localization and boundary detection in audio signals. Leveraging the Urban Sound Detection dataset, raw audio is preprocessed into mel spectrograms to capture spectral-temporal characteristics, which are fed into two models: (1) a baseline YOHO-based CNN and (2) a YOHO variant augmented with BAM. The attention mechanism dynamically weights channel-wise features, enabling the model to focus on salient audio regions while suppressing noise. Experimental results demonstrate that the attention-enhanced model achieves 90% test accuracy, outperforming the baseline YOHO model (87%) and exhibiting faster convergence. Key contributions include a systematic comparison of segmentation-by-classification approaches, a novel integration of BAM into YOHO for SED, and empirical validation of attention mechanisms in improving robustness against acoustic variability. This work underscores the potential of lightweight attention modules to enhance real-time sound event detection in resource-constrained loT and edge computing applications, while future directions explore temporal modeling and multimodal fusion for further gains.