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EmoMV: Affective music-video correspondence learning datasets for classification and retrieval

  • Published:2023-03 Issue: Volume:91 Page:64-79
  • ISSN:1566-2535
  • Container-title:Information Fusion
  • language:en
  • Short-container-title:Information Fusion

Author:

Thao Ha Thi Phuong,Roig Gemma,Herremans DorienORCID

Funder

Government of Singapore Ministry of Education

Publisher

Elsevier BV

Subject

Hardware and Architecture,Information Systems,Signal Processing,Software

Reference102 articles.

1. Deep audio-visual learning: A survey;Zhu;Int. J. Autom. Comput.,2021

2. Look, listen and learn;Arandjelovic,2017

3. Cross-modal embeddings for video and audio retrieval;Surís,2018

4. Emotion-based end-to-end matching between image and music in valence-arousal space;Zhao,2020

5. Query by video: Cross-modal music retrieval;Li,2019

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1. Video2Music: Suitable music generation from videos using an Affective Multimodal Transformer model;Expert Systems with Applications;2024-09

2. Multimodal music datasets? Challenges and future goals in music processing;International Journal of Multimedia Information Retrieval;2024-08-28

3. Prompting Audios Using Acoustic Properties for Emotion Representation;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

4. Acoustic-articulatory emotion recognition using multiple features and parameter-optimized cascaded deep learning network;Knowledge-Based Systems;2024-01

5. Deformer: Denoising Transformer for Improved Audio Music Genre Classification;Applied Sciences;2023-11-25
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