Music auto-tagging based on the unified latent semantic modeling

We proposed a music auto-tagging approach based on the latent space modeling both for music context and content. First, we introduce the latent semantic analysis for music tags with Sparse Nonnegative Matrix Factorization. Then the music contents semantics will be learnt by decomposing the music content into a pre-trained dictionary and an adaptive dictionary learning algorithm is proposed. Finally, the two latent spaces will be associated with a certain subspace mapping algorithm. The experimental results show that our proposed approach outperforms the state-of-the-art auto-tagging systems when applied to the CAL500 dataset in the 5-fold cross-validation experiments.