Efficient Music Genre Retrieval Based on Peer Interest Clustering in P2P Networks
Authors
Xianfu Meng1, Zhenyu Guo, Yue Gong
1School of Electronic and Information Engineering, Dalian University of Technology
Corresponding Author
Xianfu Meng
Available Online October 2007.
- DOI
- 10.2991/iske.2007.63How to use a DOI?
- Keywords
- Music genre classification; Peer interest clustering; Content-based music retrieval; Peer-to-Peer
- Abstract
Content-based music retrieval is desirable in Peer-to-Peer (P2P) networks, considering its popularity for users and its ability of semantic search, intensive computing cost raises a barrier to efficiency and scalability though. In this paper, we propose an approach of music genre retrieval based on peer interest clustering. Automatic music feature extraction and adaptive shared music file clustering are described. Peers with similar music genre are clustered together, based on which search mechanism and improvement alternatives are deployed. The results of experiments prove the algorithm increases search performance, including precision and recall while reducing network traffic and peer workload.
- Copyright
- © 2007, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Xianfu Meng AU - Zhenyu Guo AU - Yue Gong PY - 2007/10 DA - 2007/10 TI - Efficient Music Genre Retrieval Based on Peer Interest Clustering in P2P Networks BT - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007) PB - Atlantis Press SP - 373 EP - 380 SN - 1951-6851 UR - https://doi.org/10.2991/iske.2007.63 DO - 10.2991/iske.2007.63 ID - Meng2007/10 ER -