Abstract:
This paper presents an effective re-ranking method that uses learning-to-rank paradigms to improve the accuracy of landmark-based audio fingerprinting (AFP) for audio mus...Show MoreMetadata
Abstract:
This paper presents an effective re-ranking method that uses learning-to-rank paradigms to improve the accuracy of landmark-based audio fingerprinting (AFP) for audio music retrieval. The re-ranking mechanism is invoked whenever the returned ranking from an AFP system does not have a high enough confidence measure. We propose that use of new features for re-ranking, and employ the popular learning-to-rank paradigms, including pairwise and listwise approaches for modeling the behavior from queries to desired ranking. Experimental results indicate that the proposed re-ranking method can effectively improve the top-1 recognition rate of our AFP system, with only small extra overhead of overall response time.
Published in: Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific
Date of Conference: 09-12 December 2014
Date Added to IEEE Xplore: 16 February 2015
Electronic ISBN:978-6-1636-1823-8