A Comparison of Deep Learning Methods for Timbre Analysis in Polyphonic Automatic Music Transcription

Hernandez-Olivan, Carlos; Zay Pinilla, Ignacio; Hernandez-Lopez, Carlos; Beltran, Jose R.

doi:10.3390/electronics10070810

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A Comparison of Deep Learning Methods for Timbre Analysis in Polyphonic Automatic Music Transcription

Carlos Hernandez-Olivan

^*,†

Ignacio Zay Pinilla

^†,

Carlos Hernandez-Lopez

^† and

Jose R. Beltran

Department of Electronic Engineering and Communications, University of Zaragoza, 50018 Zaragoza, Spain

Author to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

Academic Editors: Alexander Lerch and Peter Knees

Electronics 2021, 10(7), 810; https://doi.org/10.3390/electronics10070810

Received: 26 February 2021 / Revised: 24 March 2021 / Accepted: 25 March 2021 / Published: 29 March 2021

(This article belongs to the Special Issue Machine Learning Applied to Music/Audio Signal Processing)

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Abstract

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 with different timbres with a second approach based on the Deep Salience model that performs polyphonic transcription based on the Constant-Q Transform. The results of the first method show that the timbre and envelope of the onsets have a high impact on the AMT results and the second method shows that the developed model is less dependent on the strength of the onsets than other state-of-the-art models that deal with AMT on piano sounds such as Google Magenta Onset and Frames (OaF). Our polyphonic transcription model for non-piano instruments outperforms the state-of-the-art model, such as for bass instruments, which has an F-score of 0.9516 versus 0.7102. In our latest experiment we also show how adding an onset detector to our model can outperform the results given in this work. View Full-Text

Keywords: music transcription; music information retrieval; deep learning

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MDPI and ACS Style

Hernandez-Olivan, C.; Zay Pinilla, I.; Hernandez-Lopez, C.; Beltran, J.R. A Comparison of Deep Learning Methods for Timbre Analysis in Polyphonic Automatic Music Transcription. Electronics 2021, 10, 810. https://doi.org/10.3390/electronics10070810

AMA Style

Hernandez-Olivan C, Zay Pinilla I, Hernandez-Lopez C, Beltran JR. A Comparison of Deep Learning Methods for Timbre Analysis in Polyphonic Automatic Music Transcription. Electronics. 2021; 10(7):810. https://doi.org/10.3390/electronics10070810

Chicago/Turabian Style

Hernandez-Olivan, Carlos, Ignacio Zay Pinilla, Carlos Hernandez-Lopez, and Jose R. Beltran. 2021. "A Comparison of Deep Learning Methods for Timbre Analysis in Polyphonic Automatic Music Transcription" Electronics 10, no. 7: 810. https://doi.org/10.3390/electronics10070810

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A Comparison of Deep Learning Methods for Timbre Analysis in Polyphonic Automatic Music Transcription

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