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Acoustic Scene Classification Using Higher-Order Ambisonic Features

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Details

Original languageEnglish
Title of host publication2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)
PublisherIEEE
Pages328-332
Number of pages5
ISBN (Electronic)978-1-7281-1123-0
ISBN (Print)978-1-7281-1124-7
DOIs
Publication statusPublished - Oct 2019
Publication typeA4 Article in a conference publication
EventIEEE Workshop on Applications of Signal Processing to Audio and Acoustics -
Duration: 1 Jan 1900 → …

Publication series

NameIEEE Workshop on Applications of Signal Processing to Audio and Acoustics
ISSN (Print)1931-1168
ISSN (Electronic)1947-1629

Conference

ConferenceIEEE Workshop on Applications of Signal Processing to Audio and Acoustics
Period1/01/00 → …

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.

Keywords

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

Publication forum classification

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