Improving query by singing/humming systems over GPUs
Journal
International Conference on Parallel Processing
Pages
561-567
Date Issued
2012
Author(s)
Abstract
This paper presents the use of GPUs for implementing a parallelized comparison engine in a query-by-singing/humming (QBSH) system, which takes a user's singing or humming input and returns the most likely song from a database of about 13,000 song tracks. To speed up the comparison, we employ repeating pattern removal to retain only unique tunes in the database. Moreover, we explore different parallel schemes in GPU for achieving the best efficiency without sacrificing the retrieval accuracy. With an optimum speedup factor of 16, we have successfully implemented a QBSH system that is publicly available from the internet.
Type
conference paper