Music information retrieval by detecting mood via computational media aesthetics

It is well known that music can convey emotion and modulate mood, to retrieve music by mood is sometimes the exclusive manner people select music to enjoy. We concentrate on music retrieval by detecting mood. Mood detection is implemented on the viewpoint of computational media aesthetics, that is, by analyzing two music dimensions, tempo and articulation, in the procedure of making music, we derive four categories of mood, happiness, anger, sadness and fear. Concretely, with regard to music in the format of raw audio, after tempo is detected using a multiple agent approach, a feature called relative tempo is calculated, and after the mean and standard deviation of the feature called average silence ratio in the presented computational articulation model are calculated, a simple BP neural network classifier is trained to detect mood. Users retrieval music by browsing the 3D visualization of feature space associated with specific mood. We report the experimental result on a test corpus of 353 pieces of popular music with various genres.