Improving mood classification in music digital libraries by combining lyrics and audio
Proceedings of the 10th annual joint conference on Digital libraries
Exploring the music similarity space on the web
ACM Transactions on Information Systems (TOIS)
Exploiting online music tags for music emotion classification
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special section on ACM multimedia 2010 best paper candidates, and issue on social media
A musical mood trajectory estimation method using lyrics and acoustic features
MIRUM '11 Proceedings of the 1st international ACM workshop on Music information retrieval with user-centered and multimodal strategies
Machine Recognition of Music Emotion: A Review
ACM Transactions on Intelligent Systems and Technology (TIST)
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Machine learning as an objective approach to understanding music
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
Personalization in multimodal music retrieval
AMR'11 Proceedings of the 9th international conference on Adaptive Multimedia Retrieval: large-scale multimedia retrieval and evaluation
Leveraging viewer comments for mood classification of music video clips
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Unsupervised tagging of spanish lyrics dataset using clustering
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
A survey of music similarity and recommendation from music context data
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Capturing the workflows of music information retrieval for repeatability and reuse
Journal of Intelligent Information Systems
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Time-series segmentation in the fully unsupervised scenario in which the number of segment-types is a priori unknown is a fundamental problem in many applications. We propose a Bayesian approach to a segmentation model based on the switching linear Gaussian ...