Multimodal Sparsity-Eager Support Vector Machines for Music Classification

As the demand for multimedia grows, the development of information retrieval systems utilizing all available data modalities becomes of paramount importance. The provision of multiple modalities is motivated by usability, presence of noise in one modality and non-universality of a single modality. Radio stations and music TV channels hold archives of millions of music tapes and lyrics. Gigabytes of music files are also spread over the web along with the lyrics and metadata for each file. Searching and organizing large scale multimodal datasets is a challenging task. Supervised methods such as support vector machine (SVM) achieve state of the art performance for music classification on single modality, but suffer from over-fitting on training examples and limitations of single modality approaches. In this paper, we introduce a classifier fusion of multimodal audio and lyrics data to address these single modality classification limitations. We introduce the multimodal l₁-SVM classifier, that utilizes sparse methods to deal with over-fitting for music classification. We compare the classification accuracy of the fusion classifier for a genre classification task in a large public dataset with single modality l₁-SVM.