Polyphonic music, on the other hand, can be seen as having multiple rhythmic sequences, or voices, concurrently.
Monophonic and homophonic music can be described as homorhythmic, or having a single musical rhythm. However, piano and orchestral scores frequently exhibit polyphonic passages, which add a second dimension to the task. Previous work has shown that neural architectures are able to perform optical music recognition (OMR) on monophonic and homophonic music with high accuracy. Additionally, we study several considerations about the codification of the output musical sequences, the convergence and scalability of the neural models, as well as the ability of this approach to locate symbols in the input score.
In our experiments, it is demonstrated that this formulation can be carried out successfully. We also present the Printed Music Scores dataset, containing more than 80,000 monodic single-staffreal scores in common western notation, that is used to train and evaluate the neural approach. Thanks to the use of the the so-called Connectionist Temporal Classification loss function, these models can be directly trained from input images accompanied by their corresponding transcripts into music symbol sequences. This is achieved by using a neural model that combines the capabilities of convolutional neural networks, which work on the input image, and recurrent neural networks, which deal with the sequential nature of the problem. In this work, we study the use of neural networks that work in an end-to-end manner. Despite the efforts made so far, there are hardly any complete solutions to the problem.
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Optical Music Recognition is a field of research that investigates how to computationally decode music notation from images.
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