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.

音乐信息检索领域的自动音乐转录（AMT）是一项核心挑战，旨在将音频信号转换为音乐符号表示，本文扼要回顾了 AMT 在音乐信号分析中的关键作用，强调了由于音乐和谐的复杂和相互叠加的频谱结构而对 AMT 的重要性，通过对 AMT 中现有的机器学习技术的彻底研究，我们探讨了当前模型和方法的进展和限制。尽管有可观的进展，AMT 系统尚未达到人类专家的准确度，这在很大程度上是由于音乐和谐的复杂性和对细致解释的需求。本综述批判性评估了全自动和半自动的 AMT 系统，强调了最小用户干预的重要性，并研究了迄今为止提出的各种方法。通过解决先前技术的限制并提出改进的途径，我们的目标是引导未来的研究朝着能够准确且高效地将复杂的音频信号转化为精确的符号表示的全自动 AMT 系统。本研究不仅综合了最新的进展，而且为克服 AMT 中的现有挑战提供了一个路线图，为研究人员提供了有价值的洞察，旨在缩小当前系统和人类级转录准确性之间的差距。