Paper Info
Title
Two systems for automatic music genre recognition: what are they really recognizing?
Abstract
We re-implement two state-of-the-art systems for music genre recognition, and closely examine their behavior. First, we find specific excerpts each system consistently and persistently mislabels. Second, we test the robustness of each system to spectral adjustments to audio signals. Finally, we expose the internal genre models of each system by testing if human can recognize the genres of music excerpts composed by each system to be highly genre-representative. Our results suggest that, though they have high mean classification accuracies, neither system is recognizing music genre.
Year
DOI
Venue
2012
10.1145/2390848.2390866
MIRUM
Keywords
Field
DocType
music genre recognition,internal genre model,state-of-the-art system,specific excerpt,persistently mislabels,high mean classification accuracy,audio signal,music excerpt,automatic music genre recognition,music genre
Audio signal,Computer science,Speech recognition,Robustness (computer science),Multimedia
Conference
Citations 
PageRank 
References 
14
0.56
9
Authors
1
Name
Order
Citations
PageRank
Bob L. Sturm124129.88