  In this paper, we adapt triplet neural networks (TNNs) to a regression task,
music emotion prediction. Since TNNs were initially introduced for
classification, and not for regression, we propose a mechanism that allows them
to provide meaningful low dimensional representations for regression tasks. We
then use these new representations as the input for regression algorithms such
as support vector machines and gradient boosting machines. To demonstrate the
TNNs' effectiveness at creating meaningful representations, we compare them to
different dimensionality reduction methods on music emotion prediction, i.e.,
predicting valence and arousal values from musical audio signals. Our results
on the DEAM dataset show that by using TNNs we achieve 90% feature
dimensionality reduction with a 9% improvement in valence prediction and 4%
improvement in arousal prediction with respect to our baseline models (without
TNN). Our TNN method outperforms other dimensionality reduction methods such as
principal component analysis (PCA) and autoencoders (AE). This shows that, in
addition to providing a compact latent space representation of audio features,
the proposed approach has a higher performance than the baseline models.
