Projekter pr. år
Abstract
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.
Originalsprog | Engelsk |
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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 (Institute of Electrical and Electronics Engineers) |
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 |
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Lokation | Bella Sky Hotel |
Land/Område | Danmark |
By | Copenhagen |
Periode | 26/05/2014 → 28/05/2014 |
Fingeraftryk
Dyk ned i forskningsemnerne om 'A Closer Look at Deep Learning Neural Networks with Low-level Spectral Periodicity Features'. Sammen danner de et unikt fingeraftryk.Projekter
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CoSound
Christensen, M. G. (Projektdeltager), Tan, Z.-H. (Projektdeltager), Jensen, S. H. (Projektdeltager) & Sturm, B. L. (Projektdeltager)
01/01/2012 → 31/12/2015
Projekter: Projekt › Forskning