@conference{13e0209e5b77456eb1fc1060c6658b8f,
author = "{Marc C.} Green and  Sharath Adavanne and  Damian Murphy and  Tuomas Virtanen",
abstract = "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.",
booktitle = "2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)",
doi = "10.1109/WASPAA.2019.8937282",
isbn = "978-1-7281-1124-7",
keywords = "acoustic scene classification; ambisonics; spatial audio; convolutional neural networks; gaussian mixture models",
month = "10",
pages = "328--332",
publisher = "IEEE",
series = "IEEE Workshop on Applications of Signal Processing to Audio and Acoustics",
title = "{A}coustic {S}cene {C}lassification {U}sing {H}igher-{O}rder {A}mbisonic {F}eatures",
year = "2019",
}