Autotagging music with conditional restricted Boltzmann machines
CoRR(2011)
Abstract
This paper describes two applications of conditional restricted Boltzmann
machines (CRBMs) to the task of autotagging music. The first consists of
training a CRBM to predict tags that a user would apply to a clip of a song
based on tags already applied by other users. By learning the relationships
between tags, this model is able to pre-process training data to significantly
improve the performance of a support vector machine (SVM) autotagging. The
second is the use of a discriminative RBM, a type of CRBM, to autotag music. By
simultaneously exploiting the relationships among tags and between tags and
audio-based features, this model is able to significantly outperform SVMs,
logistic regression, and multi-layer perceptrons. In order to be applied to
this problem, the discriminative RBM was generalized to the multi-label setting
and four different learning algorithms for it were evaluated, the first such
in-depth analysis of which we are aware.
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