Affective computing
Popular music retrieval by detecting mood
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Personalization of user profiles for content-based music retrieval based on relevance feedback
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
A tutorial on support vector regression
Statistics and Computing
Automatic playlist generation based on tracking user's listening habits
Multimedia Tools and Applications
Lifetrak: music in tune with your life
Proceedings of the 1st ACM international workshop on Human-centered multimedia
Music emotion classification: a fuzzy approach
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Human-centered computing: a multimedia perspective
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Towards musical query-by-semantic-description using the CAL500 data set
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Some issues on detecting emotions in music
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Automatic mood detection and tracking of music audio signals
IEEE Transactions on Audio, Speech, and Language Processing
Mr. Emo: music retrieval in the emotion plane
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Detecting Emotions in Classical Music from MIDI Files
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Machine Recognition of Music Emotion: A Review
ACM Transactions on Intelligent Systems and Technology (TIST)
Personalized music emotion classification via active learning
Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies
Hi-index | 0.00 |
It has been realized in the music emotion recognition (MER) community that personal difference, or individuality, has significant impact on the success of an MER system in practice. However, no previous work has explicitly taken individuality into consideration in an MER system. In this paper, the group-wise MER approach (GWMER) and personalized MER approach (PMER) are proposed to study the role of individuality. GWMER evaluates the importance of each individual factor such as sex, personality, and music experience, whereas PMER evaluates whether the prediction accuracy for a user is significantly improved if the MER system is personalized for the user. Experimental results demonstrate the effect of personalization and suggest the need for a better representation of individuality and for better prediction accuracy.