A HYBRID HMM RNN MODEL FOR OPTICAL MUSIC RECOGNITION
Optical music recognition (OMR) serves as one of the key technologies in Music Information Retrieval by mining symbolic knowledge directly from images of scores. A fullfledged OMR system encompasses both image recognition and music interpretation to convert image data to symbolic representations. This process has proved to be remarkably challenging, so the state of the art OMR systems still leave much to be desired. The development of deep learning in recent years has brought great success in different domains, e.g. object recognition and scene understanding, and aroused researcher's interest to address unsolved problems with this new tool. Other work has introduced the first attempts of using deep learning to address OMR, but the models apply more appropriately to text than music scores. In this paper we propose a hybrid model that combines the power of Hidden Markov Models (HMM) and Recurrent Neural Networks (RNN) for end to end score recognition.