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Acoustic Scene Classification Using Higher-Order Ambisonic Features

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Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)
KustantajaIEEE
Sivut328-332
Sivumäärä5
ISBN (elektroninen)978-1-7281-1123-0
ISBN (painettu)978-1-7281-1124-7
DOI - pysyväislinkit
TilaJulkaistu - lokakuuta 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE Workshop on Applications of Signal Processing to Audio and Acoustics -
Kesto: 1 tammikuuta 1900 → …

Julkaisusarja

NimiIEEE Workshop on Applications of Signal Processing to Audio and Acoustics
ISSN (painettu)1931-1168
ISSN (elektroninen)1947-1629

Conference

ConferenceIEEE Workshop on Applications of Signal Processing to Audio and Acoustics
Ajanjakso1/01/00 → …

Tiivistelmä

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.

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