Automatic music classification has been of interest since digital data about music became available within the Web. For this task, different automatic classification approaches have been proposed but all existing approaches are based on the analysis of sounds. To the best of our knowledge, there is no automatic solution that considers only the sheet music for classification. Therefore, within the following study, we introduce a machine-learning based approach in order to assign an author to new sheet music. Different features, that best represent the style of a writer has been extracted, and are given in input for training to a kNN algorithm. In addition, the article discusses the results and cases when the classifier fails to assign the right author.
Towards Automatic Classification of Sheet Music
Ducange P.;
2020-01-01
Abstract
Automatic music classification has been of interest since digital data about music became available within the Web. For this task, different automatic classification approaches have been proposed but all existing approaches are based on the analysis of sounds. To the best of our knowledge, there is no automatic solution that considers only the sheet music for classification. Therefore, within the following study, we introduce a machine-learning based approach in order to assign an author to new sheet music. Different features, that best represent the style of a writer has been extracted, and are given in input for training to a kNN algorithm. In addition, the article discusses the results and cases when the classifier fails to assign the right author.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
