  Audio representation learning based on deep neural networks (DNNs) emerged as
an alternative approach to hand-crafted features. For achieving high
performance, DNNs often need a large amount of annotated data which can be
difficult and costly to obtain. In this paper, we propose a method for learning
audio representations, aligning the learned latent representations of audio and
associated tags. Aligning is done by maximizing the agreement of the latent
representations of audio and tags, using a contrastive loss. The result is an
audio embedding model which reflects acoustic and semantic characteristics of
sounds. We evaluate the quality of our embedding model, measuring its
performance as a feature extractor on three different tasks (namely, sound
event recognition, and music genre and musical instrument classification), and
investigate what type of characteristics the model captures. Our results are
promising, sometimes in par with the state-of-the-art in the considered tasks
and the embeddings produced with our method are well correlated with some
acoustic descriptors.
