Augmentation Methods on Monophonic Audio for Instrument Classification in Polyphonic Music
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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 or multi-instrument recognition for entire tracks. We present an approach for instrument classification in polyphonic music using monophonic training data that involves mixing-augmentation methods. Specifically, we experiment with pitch and tempo-based synchronization, as well as mixes of tracks with similar music genres. Further, a custom CNN model is proposed, that uses the augmented training data efficiently and a plethora of suitable evaluation metrics are discussed as well. The tempo-sync and genre techniques stand out, achieving an 81 over 2300 testing tracks.
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