Abstract: To address the issues of low signal-to-noise ratio (SNR) in dataset samples and insufficient feature representation capability of Log-Mel spectrograms in environmental sound classification (ESC) tasks, this paper proposes an improved model for environmental sound classification based on high- and low-frequency separation. Firstly, phase spectrum is incorporated as a supplement to Log-Mel spectrograms in the input features, constructing a multi-feature parameter input comprising phase, Log-Mel, and  spectrogram spectrum, thereby enhancing the expressive power of the input features. Secondly, an attention mechanism is added to the input section of the neural network to improve its resistance to noise interference, enhancing the network’s robustness and generalization capability. Experiments demonstrate that this proposed model effectively improves the recognition accuracy of environmental sounds, achieving classification accuracies of 97.25%, 89.00%, and 83.45% on ESC10, ESC50, and UrbanSound8K datasets, respectively. Compared to the original model, the accuracy improvements are 2.25%, 10.50%, and 2.22%, respectively.

Key words: environmental sound classification, multi-feature, attention mechanism