How to Retrieve Music using Mood Tags in a Folksonomy

Authors

  • Chang Bae Moon ICT-Convergence Research Center, Kumoh National Institute of Technology, Korea https://orcid.org/0000-0003-2919-0373
  • Jong Yeol Lee Computer and Software Engineering, Kumoh National Institute of Technology, Korea
  • Byeong Man Kim Computer and Software Engineering, Kumoh National Institute of Technology, Korea https://orcid.org/0000-0003-4456-9314

DOI:

https://doi.org/10.13052/jwe1540-9589.2086

Keywords:

music mood, folksonomy, mood tag, Last.fm, mood vector, relationship between mood and tag

Abstract

A folksonomy is a classification system in which volunteers collaboratively create and manage tags to annotate and categorize content. The folksonomy has several problems in retrieving music using tags, including problems related to synonyms, different tagging levels, and neologisms. To solve the problem posed by synonyms, we introduced a mood vector with 12 possible moods, each represented by a numeric value, as an internal tag. This allows moods in music pieces and mood tags to be represented internally by numeric values, which can be used to retrieve music pieces. To determine the mood vector of a music piece, 12 regressors predicting the possibility of each mood based on acoustic features were built using Support Vector Regression. To map a tag to its mood vector, the relationship between moods in a piece of music and mood tags was investigated based on tagging data retrieved from Last.fm, a website that allows users to search for and stream music. To evaluate retrieval performance, music pieces on Last.fm annotated with at least one mood tag were used as a test set. When calculating precision and recall, music pieces annotated with synonyms of a given query tag were treated as relevant. These experiments on a real-world data set illustrate the utility of the internal tagging of music. Our approach offers a practical solution to the problem caused by synonyms.

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Author Biographies

Chang Bae Moon, ICT-Convergence Research Center, Kumoh National Institute of Technology, Korea

Chang Bae Moon received a BSc, an MSc, and a PhD from the Dept. of Software Eng. at Kumoh National Institute of Technology, Korea, in 2007, 2010, and 2013, respectively. He has been with the Kumoh National Institute of Technology since 2014 as a Research Professor in the ICT Convergence Research Center. From 2013 to 2014, he was a Senior Researcher in Young Poong Elec. Co. His current research areas include artificial intelligence, Web intelligence, information filtering, and image processing.

Jong Yeol Lee, Computer and Software Engineering, Kumoh National Institute of Technology, Korea

Jong Yeol Lee received the BS and MS degree in Dept. of computer Eng. from Kumoh National Institute of Technology, Korea, in 1992 and 1994, respectively and the PhD candidate in software Eng. from Kumoh National Institute of Technology, Korea, in 2018. He has been with Kumoh National Institute of Technology since 2005 as a time lecturer of Computer Software Engineering Department. His current research areas include Artificial Intelligence, Machine Learning and Information Security.

Byeong Man Kim, Computer and Software Engineering, Kumoh National Institute of Technology, Korea

Byeong Man Kim received the BS degree in Dept. of computer Eng. from Seoul National University (SNU), Korea, in 1987, and the MS and the PhD degree in computer science from Korea Advanced Institute of Science and Technology (KAIST), Korea, in 1989 and 1992, respectively. He has been with Kumoh National Institute of Technology since 1992 as a faculty member of Computer Software Engineering Department. From 1998–1999, he was a post-doctoral fellow in UC, Irvine. From 2005–2006, he was a visiting scholar at Dept. of Computer Science of Colorado State University, working on design of a collaborative Web agent based on friend network. His current research areas include artificial intelligence, Web intelligence, information filtering and brain computer interface.

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Published

2021-11-21

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