A two-stage query by singing/humming system on GPU

This paper proposes the use of GPU (graphic processing unit) to implementing a two-stage comparison method for a QBSH (query by singing/humming) system. The system can take a user´s singing or humming and retrieve the top-10 most likely candidates from a database of 8431 songs. In order to speed up the comparison, we apply linear scaling in the first stage to select candidate songs from the database. These candidate songs are then re-ranked by dynamic time warping to achieve better recognition accuracy in the second stage. With the optimum setting, we can achieve a speedup factor of 7 (compared to dynamic time warping on GPU) and an accuracy of 77.65%.