Environmental Sound Classification Based on Stacked Concatenated DNN using Aggregated Features

In recent years, there has been an increasing interest in Environmental Sound Classification (ESC), and it is a challenging non-speech audio event classification problem because of the complexity of the environment. However, the classification accuracy of the conventional methods is significantly dependent on the robustness of representative features and the effectiveness of the constructed model, which causes the poor adaptability of current models. Considering this, a novel ESC scheme based on stacked Deep Neural Networks with multi-dimensional aggregated features is proposed. Firstly, we use the aggregated features composed of time-domain features and time–frequency (TF) domain features to capture a more comprehensive representation of sounds. Afterward, the feature reduction based on Principal Component Analysis (PCA) is employed to select the most discriminative representations. Finally, a novel Stacked Deep Neural Networks based on ensemble learning and data augmentation is presented to improve the ESC scheme's generalizing capability. The experimental results demonstrate that the proposed method is appropriate for ESC problems, which achieves 96.1% and 98.1% accuracy scores for ESC-10 and UrbanSound8K datasets, respectively, and outperforms most state-of-art methods in ESC tasks at the aspect of both accuracy and computational burden.