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Augmentation Methods on Monophonic Audio for Instrument Classification in Polyphonic Music
Instrument classification is one of the fields in Music Information Retrieval
(MIR) that has attracted a lot of research interest. However, the majority of
that is dealing with monophonic music, while efforts on polyphonic material
mainly focus on predominant instrument recognition. In this paper, we propose
an approach for instrument classification in polyphonic music from purely
monophonic data, that involves performing data augmentation by mixing different
audio segments. A variety of data augmentation techniques focusing on different
sonic aspects, such as overlaying audio segments of the same genre, as well as
pitch and tempo-based synchronization, are explored. We utilize Convolutional
Neural Networks for the classification task, comparing shallow to deep network
architectures. We further investigate the usage of a combination of the above
classifiers, each trained on a single augmented dataset. An ensemble of
VGG-like classifiers, trained on non-augmented, pitch-synchronized,
tempo-synchronized and genre-similar excerpts, respectively, yields the best
results, achieving slightly above 80% in terms of label ranking average
precision (LRAP) in the IRMAS test set.ruments in over 2300 testing tracks
Feature Extraction for Music Information Retrieval
Copyright c © 2009 Jesper Højvang Jensen, except where otherwise stated
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