Forthcoming and Online First Articles

International Journal of Arts and Technology

International Journal of Arts and Technology (IJART)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Arts and Technology (2 papers in press)

Regular Issues

  • Present and past of competition Versus Sharing economy in the music live industry   Order a copy of this article
    by Crismary Ospina Gallego, Maria Nuria Lloret Romero, Valeria Herrero Ruiz 
    Abstract: 73% of independent musicians have experienced
    Keywords: sharing economy; music industry; collaborative actions; competition spirit; stress anxiety; depression; authoritarianism; root cause analysis.
    DOI: 10.1504/IJART.2023.10055144
  • Multimodal music emotion recognition method based on multi data fusion   Order a copy of this article
    by Fanguang Zeng 
    Abstract: In order to overcome the problems of low recognition accuracy and long recognition time in traditional multimodal music emotion recognition methods, a multimodal music emotion recognition method based on multiple data fusion is proposed. The multi-modal music emotion is decomposed by the non-negative matrix decomposition method to obtain the multi-modal data of audio and lyrics, and extract the audio modal emotional features and text modal emotional features respectively. After the multi-modal data of the two modal emotional features are weighted and fused through the linear prediction residual, the normalised multi-modal data is used as the training sample and input into the classification model based on support vector machine, So as to identify multimodal music emotion. The experimental results show that the proposed method takes the shortest time for multimodal music emotion recognition and improves the recognition accuracy.
    Keywords: multi data fusion; multimodal music; emotional recognition; non-negative matrix decomposition method; support vector machines; SVMs.
    DOI: 10.1504/IJART.2023.10055835