Resumé
| Originalsprog | Engelsk |
|---|---|
| Titel | Proceedings of 4th International Workshop on Cognitive Information Processing (CIP 2014) |
| Redaktører | Lars Kai Hansen, Søren Holdt Jensen, Jan Larsen |
| Antal sider | 6 |
| Vol/bind | 1 |
| Forlag | IEEE |
| Publikationsdato | 2014 |
| Sider | 1-6 |
| ISBN (Trykt) | 978-1-4799-3696-0 |
| DOI | |
| Status | Udgivet - 2014 |
| Begivenhed | International Workshop on Cognitive Information Processing - Bella Sky Hotel, Copenhagen, Danmark Varighed: 26 maj 2014 → 28 maj 2014 |
Konference
| Konference | International Workshop on Cognitive Information Processing |
|---|---|
| Lokation | Bella Sky Hotel |
| Land | Danmark |
| By | Copenhagen |
| Periode | 26/05/2014 → 28/05/2014 |
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A Closer Look at Deep Learning Neural Networks with Low-level Spectral Periodicity Features. / Sturm, Bob L.; Kereliuk, Corey; Pikrakis, Aggelos.
Proceedings of 4th International Workshop on Cognitive Information Processing (CIP 2014). red. / Lars Kai Hansen; Søren Holdt Jensen; Jan Larsen. Bind 1 IEEE, 2014. s. 1-6.Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review
TY - GEN
T1 - A Closer Look at Deep Learning Neural Networks with Low-level Spectral Periodicity Features
AU - Sturm, Bob L.
AU - Kereliuk, Corey
AU - Pikrakis, Aggelos
PY - 2014
Y1 - 2014
N2 - Systems built using deep learning neural networks trained on low-level spectral periodicity features (DeSPerF) reproduced the most “ground truth” of the systems submitted to the MIREX 2013 task, “Audio Latin Genre Classification.” To answer why this was the case, we take a closer look at the behavior of a DeSPerF system we create and evaluate using the benchmark dataset BALLROOM. We find through time stretching that this DeSPerF system appears to obtain a high figure of merit on the task of music genre recognition because of a confounding of tempo with “ground truth” in BALLROOM. This observation motivates several predictions.
AB - Systems built using deep learning neural networks trained on low-level spectral periodicity features (DeSPerF) reproduced the most “ground truth” of the systems submitted to the MIREX 2013 task, “Audio Latin Genre Classification.” To answer why this was the case, we take a closer look at the behavior of a DeSPerF system we create and evaluate using the benchmark dataset BALLROOM. We find through time stretching that this DeSPerF system appears to obtain a high figure of merit on the task of music genre recognition because of a confounding of tempo with “ground truth” in BALLROOM. This observation motivates several predictions.
U2 - 10.1109/CIP.2014.6844511
DO - 10.1109/CIP.2014.6844511
M3 - Article in proceeding
SN - 978-1-4799-3696-0
VL - 1
SP - 1
EP - 6
BT - Proceedings of 4th International Workshop on Cognitive Information Processing (CIP 2014)
A2 - Hansen, Lars Kai
A2 - Holdt Jensen, Søren
A2 - Larsen, Jan
PB - IEEE
ER -