Music Tag Annotation and Clustering Using Latent Music Semantic Analysis

Music tags include different types of musical information. The tags of same or different types can be assigned together by human to a specific song. This may lead to some specific tag co-occurrence patterns among auditorily similar songs. In this paper, we propose a novel generative approach via Latent Music Semantic Analysis (LMSA) to model and predict the tag co-occurrence pattern of a song. The LMSA-based approach jointly models two types of features, namely, auditory music features and tag-based text features. We employ a Gaussian mixture model (GMM) or a codebook to represent the auditory feature references and a tag-based music semantic model to model the tag co-occurrence patterns given the GMM-based or vector quantized auditory feature representation. We demonstrate the capability of the LMSA-based approach in music semantic exploration and music tag clustering. In addition, the results of music tag annotation experiments show that our method outperforms the baseline Codeword Bernoulli Average (CBA) method.