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Abstract
We propose and demonstrate a simple method to determine if
a music information retrieval (MIR) system is
using factors irrelevant to the task for which it is designed.
This is of critical importance to certain use cases,
but cannot be accomplished using standard approaches to evaluation in MIR.
Akin to the controlled experiments
designed to test the intellect of
the famous horse ``Clever Hans'',
we perform two experiments to show
how three state-of-the-art music genre recognition (MGR)
and music emotion recognition (MER) systems
are relying on factors confounded with the ``ground truth'' labels of a dataset.
We make available a reproducible research package
so that others can perform the same experiments
with other MIR systems.
a music information retrieval (MIR) system is
using factors irrelevant to the task for which it is designed.
This is of critical importance to certain use cases,
but cannot be accomplished using standard approaches to evaluation in MIR.
Akin to the controlled experiments
designed to test the intellect of
the famous horse ``Clever Hans'',
we perform two experiments to show
how three state-of-the-art music genre recognition (MGR)
and music emotion recognition (MER) systems
are relying on factors confounded with the ``ground truth'' labels of a dataset.
We make available a reproducible research package
so that others can perform the same experiments
with other MIR systems.
Original language | English |
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Journal | I E E E Transactions on Multimedia |
Volume | 16 |
Issue number | 6 |
Pages (from-to) | 1636-1644 |
Number of pages | 9 |
ISSN | 1520-9210 |
DOIs | |
Publication status | Published - Oct 2014 |
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Greedy Sparse Approximation and the Automatic Description of Audio and Music Data
Sturm, B. L.
Technology and Production Independent Postdoc Center for Independent Research
01/01/2012 → …
Project: Research