Evaluation of Jamendo Database as Training Set for Automatic Genre Recognition

Research on automatic music classification has gained significance in the recent years due to a significant increase in music collections size. Music is available very easily through the mobile and internet domain, so there is a need to manage music by categorizing it for search and discovery. This paper focuses on music classification by genre which is a type of supervised learning oriented problem. That means in order to build a formal classifier model it is necessary to train it using annotated data. Researchers have to build their own training sets or rely on others that are usually limited with regards to size or due to copyright restrictions. The approach described in this paper is to use the public Jamendo database for training the chosen classifier for genre recognition task.