A comparative study on content-based music genre classification
By Tao Li, Mitsunori Ogihara, Qi Li. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2003
488 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Signal Processing
View Paper: https://www.semanticscholar.org/paper/985ec818b640fa697ecb77d20f9c3d529b303c06
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Yoshua Bengio
- Luc Van Gool
- Dacheng Tao
- Xiaogang Wang
- Douglas Eck
- Trevor Darrell
- Jitendra Malik
- Ming-Hsuan Yang
In Signal Processing
- Douglas Eck
- Balázs Kégl
- Guojun Lu
- Dumitru Erhan
- Dengsheng Zhang
- Norman Casagrande
- Zhouyu Fu
- James Bergstra
Citations in the corpus
- Aggregate features and ADABOOST for music classification (2006, methodology): “This is the same dataset used in Tzanetakis et al. (2002), Tzanetakis and Cook (2002), and Li and Tzanetakis (2003).”
- A Survey of Audio-Based Music Classification and Annotation (2011, background): “An important metric learning method useful for genre classification is linear discriminant analysis (LDA) [18], which finds the optimal transformation by maximizing between-class scatter while minimizing intra-class scatter.”