MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Multimedia content personalization based on peer-level annotation
Proceedings of the seventh european conference on European interactive television conference
Music emotion classification and context-based music recommendation
Multimedia Tools and Applications
Statistical models of music-listening sessions in social media
Proceedings of the 19th international conference on World wide web
An integrated music video browsing system for personalized television
Expert Systems with Applications: An International Journal
The Journal of Supercomputing
| Hi-index | 0.00 |
It is well known that music can convey emotion and modulate mood, to retrieval music by mood is sometimes the exclusive manner people select music to enjoy. This paper concentrates 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. This paper reports the experimental result on a test corpus of 353 pieces of popular music with various genres.