kumoh national institute of technology
Networked Systems Lab.

C B Moon, J Y Yi, D-S Kim and B M Kim, "Non Keyword-Based Music Retrieval Using Social Tags", The 10th International Conference on Ubiquitous and Future Networks (ICUFN 2018), 3 - 6 July 2018, Prague, Czech Republic. (A)
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Date : 2018-04-23
Views : 140

Abstract— To retrieve music by mood tags in a social network, we introduce a mood vector, which allows moods of music pieces and mood tags to be represented internally by numeric values. A mood vector consists of 12 arousal-valence pairs each represents a mood of Thayers two-dimensional mood model. To determine the mood vector of a music piece, two Support Vector regressors are created for each arousal-valence pair using features of a music piece. Then, the regressors predict a mood vector. To map a mood tags to its mood vector, we investigate the relationship between them based on tagging data retrieved from Last.fm. To show the benefits of the proposed method, in this paper, we create a test set by using last.fm tags and their synonyms, and measure its retrieval performance over the keyword-based approach using the test set. The results illustrate that the proposed method can be useful in many respects including solving the problem caused by synonyms.