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A Comparison of Deep Learning Methods for Timbre Analysis in Polyphonic Automatic Music Transcription
2021
Electronics
Automatic music transcription (AMT) is a critical problem in the field of music information retrieval (MIR). When AMT is faced with deep neural networks, the variety of timbres of different instruments can be an issue that has not been studied in depth yet. The goal of this work is to address AMT transcription by analyzing how timbre affect monophonic transcription in a first approach based on the CREPE neural network and then to improve the results by performing polyphonic music transcription
doi:10.3390/electronics10070810
fatcat:cpogzlgwofcuniram2vxqejofq