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Acoustic Scene Classification Using Higher-Order Ambisonic Features

Marc C. Green,Sharath Adavanne,D. Murphy,Tuomas Virtanen

Published 2019 in IEEE Workshop on Applications of Signal Processing to Audio and Acoustics

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.

PUBLICATION RECORD

  • Publication year

    2019

  • Venue

    IEEE Workshop on Applications of Signal Processing to Audio and Acoustics

  • Publication date

    2019-10-01

  • Fields of study

    Physics, Computer Science, Engineering

  • Identifiers
    DOI 10.1109/WASPAA.2019.8937282
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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