Machine Learning Techniques in Automatic Music Transcription: A Systematic Survey

TL;DR

This paper systematically reviews machine learning methods in automatic music transcription, highlighting progress, challenges, and future directions to improve accuracy in converting complex audio signals into musical notation.

Contribution

It provides a comprehensive analysis of existing ML techniques in AMT, identifies current limitations, and suggests future research pathways for fully automated, accurate music transcription systems.

Findings

Current systems lag behind human transcription accuracy

Deep learning models have advanced AMT but still face challenges with complex harmonies

Future research should focus on reducing manual intervention and improving model robustness

Abstract

In the domain of Music Information Retrieval (MIR), Automatic Music Transcription (AMT) emerges as a central challenge, aiming to convert audio signals into symbolic notations like musical notes or sheet music. This systematic review accentuates the pivotal role of AMT in music signal analysis, emphasizing its importance due to the intricate and overlapping spectral structure of musical harmonies. Through a thorough examination of existing machine learning techniques utilized in AMT, we explore the progress and constraints of current models and methodologies. Despite notable advancements, AMT systems have yet to match the accuracy of human experts, largely due to the complexities of musical harmonies and the need for nuanced interpretation. This review critically evaluates both fully automatic and semi-automatic AMT systems, emphasizing the importance of minimal user intervention and examining various methodologies proposed to date. By addressing the limitations of prior techniques and suggesting avenues for improvement, our objective is to steer future research towards fully automated AMT systems capable of accurately and efficiently translating intricate audio signals into precise symbolic representations. This study not only synthesizes the latest advancements but also lays out a road-map for overcoming existing challenges in AMT, providing valuable insights for researchers aiming to narrow the gap between current systems and human-level transcription accuracy.