Several recent polyphonic music transcription systems have utilized deep neural networks to achieve state of the art results on various benchmark datasets, pushing the envelope on framewise and note-level performance measures. Unfortunately we can observe a sort of glass ceiling effect. To investigate this effect, we provide a detailed analysis of the particular kinds of errors that state of the art deep neural transcription systems make, when trained and tested on a piano transcription task. We are ultimately forced to draw a rather disheartening conclusion: the networks seem to learn combinations of notes, and have a hard time generalizing to unseen combinations of notes. Furthermore, we speculate on various means to alleviate this situation.

Authors: Kelz, Rainer; Widmer, Gerhard
Affiliation: Johannes Kepler University, Linz, Austria
AES Conference: 2017 AES International Conference on Semantic Audio (June 2017)
Paper Number: 5-1
Publication Date: June 13, 2017
Subject: Deep Learning

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