Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Affective computing
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
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Popular music retrieval by detecting mood
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
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MARSYAS: a framework for audio analysis
Organised Sound
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Organised Sound
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A tutorial on support vector regression
Statistics and Computing
Music emotion classification: a fuzzy approach
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
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Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
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ECML '07 Proceedings of the 18th European conference on Machine Learning
Multimodal Music Mood Classification Using Audio and Lyrics
ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
IEEE Transactions on Knowledge and Data Engineering
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Proceedings of the 10th annual joint conference on Digital libraries
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Music Emotion Recognition
Ranking-Based Emotion Recognition for Music Organization and Retrieval
IEEE Transactions on Audio, Speech, and Language Processing
Introduction to the Special Issue on Music Information Retrieval
IEEE Transactions on Audio, Speech, and Language Processing
A Regression Approach to Music Emotion Recognition
IEEE Transactions on Audio, Speech, and Language Processing
Automatic mood detection and tracking of music audio signals
IEEE Transactions on Audio, Speech, and Language Processing
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The online repository of music tags provides a rich source of semantic descriptions useful for training emotion-based music classifier. However, the imbalance of the online tags affects the performance of emotion classification. In this paper, we present a novel data-sampling method that eliminates the imbalance but still takes the prior probability of each emotion class into account. In addition, a two-layer emotion classification structure is proposed to harness the genre information available in the online repository of music tags. We show that genre-based grouping as a precursor greatly improves the performance of emotion classification. On the average, the incorporation of online genre tags improves the performance of emotion classification by a factor of 55% over the conventional single-layer system. The performance of our algorithm for classifying 183 emotion classes reaches 0.36 in example-based f-score.