Research of Music Recognition Based on Ensemble Learning
Cheng Qiu, Wang Da-hai, Weijia Ren, Quan Zou · 2012
With the development of information and multimedia technologies,music data beoame more and more diversity.It is a challenge to retrieve and manage the music data.Music classification is the key issue of music information retrieval and can help us manage different kinds of music data.The static selective strategy of circulating combination based on K-Means clustering is a double-layer selective ensemble model.In the first layer,clustering algorithm is employed to choose the classifiers with high diversity.Then,via the circulating framework,classifier candidates are generated by the static selective strategy and voted for the last result.Experiments on two bench music datasets verify the performance of the proposed strategy.