Acoustic Scene Classification Using Higher-Order Ambisonic Features

This paper investigates the potential of using higher-order Ambisonic features to perform acoustic scene classification. We compare the performance of systems trained using first-order and fourth-order spatial features extracted from the EigenScape database. Using both Gaussian mixture model and convolutional neural network classifiers, we show that features extracted from higher-order Ambisonics can yield increased classification accuracies relative to first-order features. Diffuseness-based features seem to describe scenes particularly well relative to direction-of-arrival based features. With specific feature subsets, however, differences in classification accuracy between first and fourth-order features become negligible.

acoustic scene classification; ambisonics; spatial audio; convolutional neural networks; gaussian mixture models