Performance analysis of multiple aggregated acoustic features for environment sound classification

Accuracy cognition in the complex and dynamic environment plays a pivotal part in artificial intelligence. Accurate classification of acoustic events is one of the foundations of environment acoustic awareness that has a strong correlation with the selected features. In this paper, the objective is to present a performance analysis of the of different acoustic features aggregation schemes on environment sound classification (ESC) tasks to find the best feature aggregate strategies to overcome the challenging problem of elevating the classification accuracy of environment sounds. With a considerable number of experiments, the feature combination including MFCC, Log-mel Spectrogram, Chroma, Spectral Contrast and Tonnetz achieves the state-of-art classification accuracy on the ESC dataset (85.6%) and 93.4% on the UrbanSound8K dataset.