Head and stem extraction from printed music scores using a neural network approach

In an automatic music score recognition system, it is very important to extract heads and stems of notes, since these symbols are most ubiquitous in a score and musically important. The purpose of our system is to present an accurate and high-speed extraction of note heads (except the whole notes) and stems according to the following procedure. (1) We extract all regions which are considered as candidates of stems or heads. (2) To identify heads from the candidates, we use a three-layer neural network. (3) The weights for the network are learned by the back propagation method. In the learning, the network learns the spatial constraints between heads and surroundings rather than the shapes of heads. (4) After the learning process is completed we use this network to identify a number of test head candidates (5) The stem candidates touching the detected heads are extracted as true stems. As an experimental result, we obtained high recognition rates of 99.0% and 99.2% for stems and note heads, respectively. It took between 40 to 100 seconds to process a printed piano score on A4 sheet using a workstation. Therefore, our system can analyze it at least 10 times as fast as manual methods