LAUSR

With advancement of various information processing and storage techniques, the scale of digital music collections has been growing at very fast speed during recent decades. To support high-quality content-based retrieval over such a large volume of music data, how to develop indexing structure with good effectiveness, efficiency and scalability becomes an important research issue. However, existing techniques mainly focus on improving query efficiency. Very few approaches have been proposed to address issues related to scalability and accuracy. In this study, we address the problem via introducing a novel indexing technique called effective music indexing framework (EMIF) to facilitate scalable and accurate music retrieval. It is designed based on a “classification-and-indexing” principle and consists of two main functionality modules: (1) music classification—a novel semantic-sensitive classification to identify an input song’s category and (2) indexing module—multiple local indexing structures, one for each semantic category to reduce query response time significantly. In particular, the classification model combining linear discriminative mixture model (LDMM) and advanced score fusion scheme has been applied to estimate category of music accurately. Layered architecture enables EMIF to enjoy superior scalability and efficiency. To evaluate the approach, a set of experimental studies has been carried out using two large music test collections and the results demonstrate various advantages of EMIF over state-of-the-art approaches including efficiency, scalability and effectiveness.