## 2 Related Work
### OMR on Contemporary Notation  
[PERSON] et al. [8] use (bidirectional) LSTM networks to predict the pitch and rhythm of notes and other musical symbols for example rests and clefs in images, each containing a single line of monophonic music. The outputs of the network are two sequences. The first one aims to predict the pitch of notes or the type of other symbols like clefs (54 classes in total), while the second one indicates the rhythm of notes or a _no note_ label (26 classes). For training the network, they implement two different loss functions that consider both target sequences. One computes the weighted euclidean loss, the other implements a multi-label soft margin loss. To evaluate their algorithm, two different data sets are used which both consist of monophonic music in modern notation, however one is printed and one handwritten. Experiments on the first dataset which comprises incipts from the RISM data set [9] yield a symbol/pitch error rate of 1.5% and a rhythm error rate of 2.0%. The total error for both properties to be correct is 2.8%. As a second data set, they use one page of the CVC-MUSICMA dataset [10]. After manually labelling six staves, they extend the number of different staves by varying the order of bars within a single staff. During training, data augmentation is used and all lines from the printed data set were added. The error of the symbol/pitch and rhythm detection is 47% and 43%, respectively, yielding a total error rate of 65%. A similar attempt to learn music recognition on modern monophonic printed music was proposed by [PERSON] and [PERSON] [11]. However, compared to Reference [8], they use preliminary CNN layers and an encoder/decoder structure for their neural network. The CNN/LSTM based encoder maps a line image into one fixed size state representation by processing the line sequentially. The decoder consecutively decodes this state and predicts a sequence of pitches, duration, and finally a stop mark. This overall procedure is based on, and very similar to, the sequence to sequence task used for machine translation [12]. In total there are 108 pitch and 48 duration categories in their data set, which is compiled from MusicXML [1] scores from the MuseScore ([[https://musescore.org/](https://musescore.org/)]([https://musescore.org/](https://musescore.org/))) sheet music archive. In contrast to the normalised edit-distance, a rather strict metric is used that cannot handle insertions or deletions. They align the prediction and ground truth (GT) sequence label by label and count the number of correct predictions. Thus, if a label is deleted or inserted all subsequent notes are usually false. Their final model which is trained with data augmentation, yields a pitch and duration accuracy of 81%, and 94%, respectively. The total accuracy of notes is 80%. A third very promising attempt to predict monophonic music was made by [PERSON] and [PERSON] [13]. They use CNN/LSTM hybrids combined with a CTC-loss-function [14] as model architecture, a technique that already succeeded for handwritten and printed optical character recognition (OCR) [15; 16], or speech recognition [17; 18], which are both sequence to sequence tasks. The advantage of this loss function is that it does not require position accurate labelling in the GT. Only the target sequence and input data are obligatory, out of which the network automatically learns the alignment. The drawback of this method is that each combination of pitch and rhythm semantically requires a distinct label. Moreover, a key-signature can, for instance, either be a single symbol or be dissolved in individual accidentals. The first so-called semantic representation requires 1781 classes in total, while the second agnostic representation only uses 758 classes.