• Title/Summary/Keyword: Mood Categorization

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Optimization of Domain-Independent Classification Framework for Mood Classification

  • Choi, Sung-Pil;Jung, Yu-Chul;Myaeng, Sung-Hyon
    • Journal of Information Processing Systems
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    • v.3 no.2
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    • pp.73-81
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    • 2007
  • In this paper, we introduce a domain-independent classification framework based on both k-nearest neighbor and Naive Bayesian classification algorithms. The architecture of our system is simple and modularized in that each sub-module of the system could be changed or improved efficiently. Moreover, it provides various feature selection mechanisms to be applied to optimize the general-purpose classifiers for a specific domain. As for the enhanced classification performance, our system provides conditional probability boosting (CPB) mechanism which could be used in various domains. In the mood classification domain, our optimized framework using the CPB algorithm showed 1% of improvement in precision and 2% in recall compared with the baseline.

Effective Mood Classification Method based on Music Segments (부분 정보에 기반한 효과적인 음악 무드 분류 방법)

  • Park, Gun-Han;Park, Sang-Yong;Kang, Seok-Joong
    • Journal of Korea Multimedia Society
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    • v.10 no.3
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    • pp.391-400
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    • 2007
  • According to the recent advances in multimedia computing, storage and searching technology have made large volume of music contents become prevalent. Also there has been increasing needs for the study on efficient categorization and searching technique for music contents management. In this paper, a new classifying method using the local information of music content and music tone feature is proposed. While the conventional classifying algorithms are based on entire information of music content, the algorithm proposed in this paper focuses on only the specific local information, which can drastically reduce the computing time without losing classifying accuracy. In order to improve the classifying accuracy, it uses a new classification feature based on music tone. The proposed method has been implemented as a part of MuSE (Music Search/Classification Engine) which was installed on various systems including commercial PDAs and PCs.

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