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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.
|Title of host publication||Proceedings of 4th International Workshop on Cognitive Information Processing (CIP 2014)|
|Editors||Lars Kai Hansen, Søren Holdt Jensen, Jan Larsen|
|Number of pages||6|
|Publication status||Published - 2014|
|Event||International Workshop on Cognitive Information Processing - Bella Sky Hotel, Copenhagen, Denmark|
Duration: 26 May 2014 → 28 May 2014
|Conference||International Workshop on Cognitive Information Processing|
|Location||Bella Sky Hotel|
|Period||26/05/2014 → 28/05/2014|
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- 1 Finished
Christensen, M. G., Tan, Z., Jensen, S. H. & Sturm, B. L.
01/01/2012 → 31/12/2015