A content-based music search system using query by multi-tags with multi-levels of preference

This demonstration paper presents a novel content-based music search system that accepts a query containing multiple tags with multiple levels of preference (denoted as an MTML query) to search music from an untagged music database. We select a limited number of most frequently used music tags to form the tag space and design an interface for users to input queries by operating the scroll bars. To effect MTML content-based music retrieval, we introduce a tagbased music aspect model that jointly models the auditory features and tag labels of a song. Two indexing methods and their corresponding matching methods, namely pseudo song-based matching and tag affinity-based matching, are incorporated into the pre-learned tag-based music aspect model. The content-based music search system is implemented on the MajorMiner dataset, which consists of 2,472 10-second music clips and their associated human labeled tags crawled from the MajorMiner website. The MTML query interface contains 36 top tags used in the dataset. We randomly select 1,648 music clips with their tag labels for training the tag-based music aspect model and 824 clips without using their tag labels for building the untagged music database for content-based retrieval.