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Exploiting Online Music Tags for Music Emotion Classi cation YU-CHING LIN, National Taiwan University YI-HSUAN YANG, Academia Sinica HOMER H. CHEN, National Taiwan University The online repository of music tags provides a rich source of semantic descriptions useful for training emotion-based music classi er. However, the imbalance of the online tags affects the performance of emotion classi cation. 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 classi cation 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 classi cation. On the average, the incorporation of online genre tags improves the performance of emotion classi cation 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. Categories and Subject Descriptors: H.3.1 [Information Storage and Retrieval]: Information Search and Retrieval Retrieval models; H.5. [Information Interfaces and Presentation]: Sound and Music Computing Modeling, Systems General Terms: Algorithms, Performance, Human Factors Additional Key Words
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) – Association for Computing Machinery
Published: Oct 1, 2011
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