Deep Residual Convolutional Neural Networks for Robust Environmental Sound Classification Using Optimised Mel-Spectrogram Representations

Environmental sound classification (ESC) is a fundamental machine-audition problem in the context of smart-city sensing, industrial monitoring, healthcare, and consumer devices. In this study, a convolutional classifier is shown to be a powerful approach for single-channel Mel-spectrogram representations of the ESC-50 benchmark and is evaluated on it in a controlled empirical setting using ResNet-34 architecture. The contribution is not an architectural family, but rather an optimisation and reproducibility study that aims to highlight the influence of residual shortcuts, batch normalisation, dropout, Mel-filter resolution, masking/augmenting with `Spec Augment`-style, mixup, and learning rate scheduling on a consistent training pipeline. The model performance on the ESC-50 benchmark was 83.0% (five-fold CV) and 84.0% (best single-fold validated) at epoch 88. The revised analysis includes the computation cost estimation, per-class performance metrics, confusion matrix analysis, modern benchmark positioning, and confidence intervals. Results show that residual CNNs still provide a salient and interpretable baseline for small-data ESC, despite the current state of the art of large pre-trained transformer and attention base networks.

Environmental sound classification · residual networks · Mel spectrogram · convolutional neural networks · ESC-50 · data augmentation · deep learning

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