  The development of models for learning music similarity and feature
extraction from audio media files is an increasingly important task for the
entertainment industry. This work proposes a novel music classification model
based on metric learning and feature extraction from MP3 audio files. The
metric learning process considers the learning of a set of parameterized
distances employing a structured prediction approach from a set of MP3 audio
files containing several music genres. The main objective of this work is to
make possible learning a personalized metric for each customer. To extract the
acoustic information we use the Mel-Frequency Cepstral Coefficient (MFCC) and
make a dimensionality reduction with the use of Principal Components Analysis.
We attest the model validity performing a set of experiments and comparing the
training and testing results with baseline algorithms, such as K-means and Soft
Margin Linear Support Vector Machine (SVM). Experiments show promising results
and encourage the future development of an online version of the learning
model.
