Paper Info
Title
Using voice suppression algorithms to improve beat tracking in the presence of highly predominant vocals
Abstract
Beat tracking estimation from music signals becomes difficult in the presence of highly predominant vocals. We compare the performance of five state-of-the-art algorithms on two datasets, a generic annotated collection and a dataset comprised of song excerpts with highly predominant vocals. Then, we use seven state-of-the-art audio voice suppression techniques and a simple low pass filter to improve beat tracking estimations in the later case. Finally, we evaluate all the pairwise combinations between beat tracking and voice suppression methods. We confirm our hypothesis that voice suppression improves the mean performance of beat trackers for the predominant vocal collection.
Year
DOI
Venue
2013
10.1109/ICASSP.2013.6637607
Acoustics, Speech and Signal Processing
Keywords
Field
DocType
audio signal processing,low-pass filters,music,beat tracking estimation,low pass filter,music signal,voice suppression algorithm,Beat tracking,evaluation,source separation,voice suppression
Computer science,Low-pass filter,Beat (music),Artificial intelligence,Audio signal processing,Source separation,BitTorrent tracker,Pairwise comparison,Pattern recognition,Algorithm,Speech recognition,Time–frequency analysis,Hidden Markov model
Conference
ISSN
Citations 
PageRank 
1520-6149
2
0.37
References 
Authors
0
2
Name
Order
Citations
PageRank
José R. Zapata11127.57
Emilia Gómez218925.90