Accelerating query by singing/humming on GPU: Optimization for web deployment

This paper presents the use of GPU for implementing a parallelized comparison method of linear scaling in a query by singing/humming system, which can compare a user's acoustic input to the database containing about 13,000 songs. We focus on the comparison from anywhere in a song, and the optimum setting is found through 3 different schemes of parallelization. With a speedup factor of 66, the proposed scheme with the optimum setting has been successfully implemented in a public QBSH system that is available from the internet.