# A Comprehensive Review on Music Transcription
## 1 Introduction  
The complex components of music have the power to evoke emotions which can communicate ideas that words alone often struggle to convey. Modern technology has opened up new channels for the expression and preservation of music. One central aspect of this evolution is music transcription, which is the process of converting auditory musical signals into symbolic representations that can be studied, analyzed, and shared. The development of music notation itself is connected to the history of music transcription, which dates back to Mesopotamia and Ancient Greece, where rudimentary notational systems were used to record melodies and rhythms [1; 2; 3; 4]. These techniques developed over the years, eventually leading to the complex and standardized music notation that we have today [5; 6; 7]. Additionally, music exhibits diversity across various genres and cultures [8; 9], each with its unique music notation systems. To accommodate these diversities, different transcription methods are required.  
The technological advancements in recent decades have assisted in a new era of music transcription. From the pioneering work of [PERSON] in the 18 th century [10]to the advent of computers in the 20 th century [11], numerous scholars and inventors have contributed to the development of transcription techniques. Today, with the rise of digital signal processing, machine learning, and artificial intelligence [12; 13; 14; 15; 16; 17], there is a significant and substantial change occurring in the field of music transcription [18; 19; 20; 21; 22].  
The aim of this comprehensive review paper is to examine an in-depth exploration of the various methodologies, techniques, and advancements that have influenced the area of music transcription. By synthesizing a wide range of research studies, we seek to offer a holistic understanding of the challenges, opportunities, and future directions in music transcription. To achieve this goal, we have organized the paper as follows.  
In Section 2, we provide an overview of music transcription by tracing from frame-level to note-level, then to stream-level, and finally to notation-level transcription. Section 3 delves into the theoretical foundations of vocal melody transcription, highlighting the complexities of monophonic and polyphonic vocals in both frame-level and note-level transcription tasks. In addition to this, we also explore the various insights and advances in music transcription areas like multi-pitch estimation and single-instrument as well as multi-instrument transcription. In Section 4, we list the current challenges of music transcription methodologies into several main factors: singing techniques, instrumentation, pitch ambiguity, tempo changes, and subjectivity. Section 5 discusses various available open-source as well as commercial music transcription tools. Section 6 describes the various python libraries that are useful for music transcription. In Section 7, we explore the applications of music transcription in various fields like music education, music creation, and music production. Evaluating the effectiveness of transcription systems is a critical aspect. So, we review the established benchmark datasets in Section 8 ranging from vocal melody to general melody, from monophonic vocals to polyphonic vocals, and from single-instrumentation to multi-instrumentation. The future directions are explained in Section 9. Finally, the review concludes in Section 10, with a discussion on the broader implications of music transcription.  
Throughout this review paper, we draw upon a diverse array of references encompassing music theory, signal processing, machine learning, and human-computer interaction. The contributions of scholars, researchers, and innovators from around the world have collectively shaped the trajectory of music transcription. By synthesizing these contributions, we aspire to offer a comprehensive resource that inspires further advancements and collaborations in this dynamic field.