Automatic Music Transcription: An Overview <s>  <s> This book serves as an ideal starting point for newcomers and an excellent reference source for people already working in the field. Researchers and graduate students in signal processing, computer science, acoustics and music will primarily benefit from this text. It could be used as a textbook for advanced courses in music signal processing. Since it only requires a basic knowledge of signal processing, it is accessible to undergraduate students. <s> BIB001 </s> Automatic Music Transcription: An Overview <s>  <s> Automatic music transcription is considered by many to be a key enabling technology in music signal processing. However, the performance of transcription systems is still significantly below that of a human expert, and accuracies reported in recent years seem to have reached a limit, although the field is still very active. In this paper we analyse limitations of current methods and identify promising directions for future research. Current transcription methods use general purpose models which are unable to capture the rich diversity found in music signals. One way to overcome the limited performance of transcription systems is to tailor algorithms to specific use-cases. Semi-automatic approaches are another way of achieving a more reliable transcription. Also, the wealth of musical scores and corresponding audio data now available are a rich potential source of training data, via forced alignment of audio to scores, but large scale utilisation of such data has yet to be attempted. Other promising approaches include the integration of information from multiple algorithms and different musical aspects. <s> BIB002
MUSIC SIGNAL PROCESSING T he capability of transcribing music audio into music notation is a fascinating example of human intelligence. It involves perception (analyzing complex auditory scenes), cognition (recognizing musical objects), knowledge representation (forming musical structures), and inference (testing alternative hypotheses). Automatic music transcription (AMT), i.e., the design of computational algorithms to convert acoustic music signals into some form of music notation, is a challenging task in signal processing and artificial intelligence. It comprises several subtasks, including multipitch estimation (MPE), onset and offset detection, instrument recognition, beat and rhythm tracking, interpretation of expressive timing and dynamics, and score typesetting. Given the number of subtasks it comprises and its wide application range, it is considered a fundamental problem in the fields of music signal processing and music information retrieval BIB001 , BIB002 . Because of the very nature of music signals, which often contain several sound sources (e.g., musical instruments and voice) that produce one or more concurrent sound events (e.g., notes and percussive sounds) that are meant to be highly correlated over both time and frequency, AMT is still considered a challenging and open problem in the literature, particularly for music containing multiple instruments and many simultaneous notes (called polyphonic music in the music signal processing literature) BIB002 . The typical data representations used in an AMT system are illustrated in Figure 1 . Usually, an AMT system takes an audio waveform as input [ Figure 1 In this article, we provide a high-level overview of AMT, emphasizing the intellectual merits and broader impacts of this topic and linking AMT to other problems found in the wider field of digital signal processing. We give an overview of approaches to AMT, detailing the methodology used in the two main families of methods, based respectively on deep learning and nonnegative matrix factorization (NMF). Finally, we provide an extensive discussion of open challenges for AMT. Regarding the scope of the article, we emphasize approaches for transcribing polyphonic music produced by pitched instruments and voice. Outside the scope of the article are methods for transcribing nonpitched sounds, such as drums, for which a brief overview is given in the "Percussion and Unpitched Sounds" section, as well as methods for transcribing specific sources within a polyphonic mixture, such as melody and bass lines.