# A Baseline for General Music Object Detection with Deep Learning
## 1 Introduction  
Optical Music Recognition (OMR) is the field of research that investigates how to computationally read music notation in documents. Having accurate OMR technology would enable fully integrating written music into the ecosystem of digital music processing. In recent years, diverse initiatives have been launched to digitize musical heritage in the written form, such as the The Digital Image Archive of Medieval Music project [1] on the academic side, or at the same time the crowd-sourced International Music Score Library Project (IMSLP) repository of public-domain and openly available music [2] which has grown to become a primary provider of sheet music worldwide. However, making not only the digital images of all these compositions, but also their structured representation accessible at scale, as attempted e.g., by the Single Interface for Music Score Searching and Analysis (SIMSSA) project [3], would constitute a breakthrough in interacting with written music, and making it accessible to both the professional and the general public in previously unseen ways: content-based search in large sheet music libraries including cross-modal retrieval, digital musicology at scale and with access to structured representations of music that only exists in written form, renotation of early notation to modern notation, manuscript transcription and part-matching to directly cut costs of music directorsand composers. These (and more) applications have been envisioned in OMR literature for a long time [4; 5]; however, results have not been forthcoming [6]. In order to be able to apply Music Information Retrieval (MIR) algorithms on music scores and enable this wide range of applications, it is first necessary to bring them into this symbolic, machine-readable format. Manually creating such symbolic representations by means of specialized music typesetting software is an expensive effort, and constitutes the bottleneck to digitally encoding music at large scales--which is, in turn, a bottleneck both for digital musicology, subsequent MIR applications, and music accessibility. OMR is expected to provide the enabling technology for scalable structured encoding. From this perspective, OMR can be seen as the key to diversifying the available symbolic music sources in reasonable time and cost. Crucially, OMR has seen a shift in paradigms in the last few years, mainly triggered by advances in the field of computer vision and machine learning through deep learning [7; 8; 9; 10]. This development is further fueled by the availability of large annotated datasets (e.g., MUSCIMA++, DeepScores) and sufficient computational power to work with such large datasets. This new paradigm, combined with a better understanding of the challenges [11; 12], allow approaching the problem of OMR somewhat differently. The entire process of OMR can be broken down into the following steps [6; 13; 14; 15]:  
1. **Preprocessing**: Standard techniques to ease further steps, e.g., contrast enhancement, binarization, skew-correction or noise removal. Additionally, the layout should be analyzed to allow subsequent steps to focus on actual content and ignore the background. 2. **Music Object Detection**: This step is responsible for finding and classifying all relevant symbols or glyphs in the image. Note that music object detection is sometimes referred to as music symbol recognition, but we use the former term because of its relation to \"object detection\", which is commonly used in computer vision to refer to the very same localization and classification task in (natural) images, answering the question \"What is where in this image?\". 3. **Relational Understanding**: From the detected and classified symbols, a music notational graph (MuNG) can be constructed that holds both the symbols and their relationships to each other. Note that, for a complete and unambiguous reconstruction, two kinds of relations are necessary: a logical relationship (e.g., between a notehead and a stem) and a temporal relationship to guarantee the correct order of the symbols.