ScholarWorks@중앙대학교

초록

Sound Event Detection (SED) systems are essential for understanding and classifying the causes and temporal occurrences of sounds in diverse environments. This paper introduces a robust and efficient SED system that integrates a novel Frequency-aware Lightweight Convolutional Attention Module (FLCAM) and semi-supervised learning techniques to address key challenges in audio analysis. The FLCAM enhances 2D convolutional models by preserving critical frequency-domain features and adaptively assigning attention weights, enabling superior performance while maintaining computational efficiency. To fully leverage strongly labeled, weakly labeled, and unlabeled data, our system employs the Mean Teacher framework, which ensures consistency between predictions under different augmentations. Comprehensive experiments on the DESED and L3DAS22 datasets demonstrate the effectiveness of our approach, achieving improvements of approximately 9 percentage points in PSDS and 2 percentage points in F-score metrics, respectively. Despite utilizing significantly fewer parameters, the proposed SED system achieves performance comparable to state-of-the-art models, making it suitable for real-world applications, including resource-constrained environments.

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