Multimedia Analysis and Fusion via Wasserstein Barycenter
- https://doi.org/10.2991/ijndc.k.200217.001How to use a DOI?
- Multimedia analysis, Wasserstein Barycenter, fusion
Optimal transport distance, otherwise known as Wasserstein distance, recently has attracted attention in music signal processing and machine learning as powerful discrepancy measures for probability distributions. In this paper, we propose an ensemble approach with Wasserstein distance to integrate various music transcription methods and combine different music classification models so as to achieve a more robust solution. The main idea is to model the ensemble as a problem of Wasserstein Barycenter, where our two experimental results show that our ensemble approach outperforms existing methods to a significant extent. Our proposal offers a new visual angle on the application of Wasserstein distance through music transcription and music classification in multimedia analysis and fusion tasks.
- © 2020 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Cong Jin AU - Junhao Wang AU - Jin Wei AU - Lifeng Tan AU - Shouxun Liu AU - Wei Zhao AU - Shan Liu AU - Xin Lv PY - 2020 DA - 2020/02 TI - Multimedia Analysis and Fusion via Wasserstein Barycenter JO - International Journal of Networked and Distributed Computing SP - 58 EP - 66 VL - 8 IS - 2 SN - 2211-7946 UR - https://doi.org/10.2991/ijndc.k.200217.001 DO - https://doi.org/10.2991/ijndc.k.200217.001 ID - Jin2020 ER -