Despite the impressive progress in music emotion recognition, it remains unclear what aspect of a song, i.e., singing voice and accompanied music, carries more emotional information. As an initial attempt to answer th...
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ISBN:
(纸本)1595930361
Despite the impressive progress in music emotion recognition, it remains unclear what aspect of a song, i.e., singing voice and accompanied music, carries more emotional information. As an initial attempt to answer the question, we introduce source separation into a standard music emotion recognition system. This allows us to compare systems with and without source separation, and consequently reveal the influence of singing voice and accompanied music on emotion recognition. Classification experiments on a set of 267 songs with *** annotations verify the new finding that source separation improves song music emotion recognition. Copyright is held by the owner/author(s).
With the dramatic increase of data volume, automatic data distribution has been one of the key techniques and intractable problem for distributed systems. This work summarizes the problem of data distribution and abst...
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Wildfire spread is a complex process affected by many elements. FARSITE (Fire Area Simulator) is a forest fire spread simulation system widely applied and recognized in the world. This paper briefly describes the prin...
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In recent years MapReduce has risen to be the de-facto tool for big data processing. MapReduce is a disruptive innovation. It has changed the landscape of database market, the landscape of technologies, as well as the...
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In this paper we describe our image annotation system par ticipated in the ImageCLEF 2013 scalable concept image annotation task. The system leverages multiple base classifiers, including single feature and multi-feat...
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In this paper we describe our image annotation system par ticipated in the ImageCLEF 2013 scalable concept image annotation task. The system leverages multiple base classifiers, including single feature and multi-feature kNN classifiers and histogram intersection ker nel SVMs, all of which are learned from the provided 250K web images and provided features with no extra manual verification. These base clas sifiers are combined into a stacked model, with the combination weights optimized to maximize the geometric mean of F-samples, F-concepts, and AP-samples metrics on the provided development set. By varying the configuration of the system, we submitted five runs. Evaluation re sults show that for all of our runs, model stacking with optimized weights performs best. Our system can annotate diverse Internet images purely based on the visual content, at the following accuracy level: F-samples of 0.290, F-concepts of 0.304, and AP-samples of 0.380. What is more, a system-to-system comparison reveals that our system and the best sub mission this year are complementary with respect to the best annotated concepts, suggesting the potential for future improvement.
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Current studies on association rule mining focus on finding Boolean/quantitative association rules from certain databases or Boolean association rules from probabilistic databases. However, little work on mining assoc...
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