RhythMiXearch: Searching for Unknown Music by Mixing Known Music

We present a novel method for searching for unknown music. RhythMiXearch is a music search system we developed that can accept two music inputs and mix those inputs to search for music that could reasonably be a result of the mixture. This approach expands the ability of Query-by-Example and allows greater flexibility for users in finding unknown music. Each music piece stored by our system is characterized by text data written by users, i.e., review data. We used Latent Dirichlet Allocation (LDA) to capture semantics from the reviews that were then used to characterize the music by Hevner’s eight impression categories. RhythMiXearch mixes two music inputs in accordance with a probabilistic mixture model and finds music that is the most likely product of the mixture. Our experimental results indicate that the proposed method is comparable to human in searching for music by multiple examples.