Acoustic Scene Classification Using Higher-Order Ambisonic Features
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Yksityiskohdat
| Alkuperäiskieli | Englanti |
|---|---|
| Otsikko | 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) |
| Kustantaja | IEEE |
| Sivut | 328-332 |
| Sivumäärä | 5 |
| ISBN (elektroninen) | 978-1-7281-1123-0 |
| ISBN (painettu) | 978-1-7281-1124-7 |
| DOI - pysyväislinkit | |
| Tila | Julkaistu - lokakuuta 2019 |
| OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisussa |
| Tapahtuma | IEEE Workshop on Applications of Signal Processing to Audio and Acoustics - Kesto: 1 tammikuuta 1900 → … |
Julkaisusarja
| Nimi | IEEE Workshop on Applications of Signal Processing to Audio and Acoustics |
|---|---|
| ISSN (painettu) | 1931-1168 |
| ISSN (elektroninen) | 1947-1629 |
Conference
| Conference | IEEE Workshop on Applications of Signal Processing to Audio and Acoustics |
|---|---|
| Ajanjakso | 1/01/00 → … |
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.