Title: Modeling of the Latent Embedding of Music using Deep Neural Network,
Abstract: While both the data volume and heterogeneity of the digital music content is
huge, it has become increasingly important and convenient to build a
recommendation or search system to facilitate surfacing these content to the
user or consumer community. Most of the recommendation models fall into two
primary species, collaborative filtering based and content based approaches.
Variants of instantiations of collaborative filtering approach suffer from the
common issues of so called "cold start" and "long tail" problems where there is
not much user interaction data to reveal user opinions or affinities on the
content and also the distortion towards the popular content. Content-based
approaches are sometimes limited by the richness of the available content data
resulting in a heavily biased and coarse recommendation result. In recent
years, the deep neural network has enjoyed a great success in large-scale image
and video recognitions. In this paper, we propose and experiment using deep
convolutional neural network to imitate how human brain processes hierarchical
structures in the auditory signals, such as music, speech, etc., at various
timescales. This approach can be used to discover the latent factor models of
the music based upon acoustic hyper-images that are extracted from the raw
audio waves of music. These latent embeddings can be used either as features to
feed to subsequent models, such as collaborative filtering, or to build
similarity metrics between songs, or to classify music based on the labels for
training such as genre, mood, sentiment, etc.