The ShuBAM-TFNet: A Lightweight Temporal–Frequency Attention Network for Environmental Sound Recognition
Kata Kunci:
EnvironmentalSound Classification, Audio Signal Processing, Edge Computing, Lightweight Deep Learning, Temporal Frequency AttentionAbstrak
Efficient environmental sound recognition is essential for smart-city monitoring and edge-based acoustic sensing, yet practical deployment is often constrained by the computational cost and parameter size of deep learning models. This paper presents ShuBAM-TFNet, a lightweight convolutional neural network designed to achieve high recognition accuracy while remaining suitable for resource-limited devices. The proposed architecture processes Log-Mel spectrograms using dilated and time-distributed convolutional layers to capture long-range temporal dependencies and localized temporal patterns without increasing model complexity. A lightweight temporal–frequency attention mechanism is introduced to adaptively emphasize informative regions across time and frequency dimensions, while Bottleneck Attention Modules are incorporated to refine channel and spatial representations. The model is evaluated on the UrbanSound8K and ESC-10 benchmark datasets using standard evaluation protocols. On UrbanSound8K, the full ShuBAM-TFNet achieves an average accuracy of 90.16 ± 1.05% with 72,390 trainable parameters, converging in approximately 27 epochs. Ablation studies show that removing the temporal–frequency attention module results in a 2–3% accuracy drop, confirming its central contribution, while eliminating dilated convolutions leads to slower convergence despite stable accuracy. Notably, removing Bottleneck Attention Modules yields the best-performing variant, achieving 91.76 ± 0.90% accuracy with a reduced parameter count of 70,754. Cross-dataset evaluation on ESC-10 using 5-fold cross-validation achieves a mean accuracy of 84.00 ± 2.41% and a macro F1-score of 0.838, demonstrating good generalization. These results indicate that attention-guided lightweight architectures provide a practical alternative to search-based or heavily parameterized models for real-time environmental sound recognition on edge devices.
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