User Tools
glad
====== Differences ====== This shows you the differences between two versions of the page.
|
glad [2013/06/30 22:36] dawenl [2.1 Solo v.s. Instrument] |
glad [2013/07/01 18:31] (current) dawenl [3 Future work] |
||
|---|---|---|---|
| Line 5: | Line 5: | ||
| | Code | [[https://github.com/dawenl/glad_cal500|Github Link]] | | | Code | [[https://github.com/dawenl/glad_cal500|Github Link]] | | ||
| - | [[http://cosmal.ucsd.edu/cal/projects/AnnRet/|Cal500]] is a widely used dataset for music tagging. The tags it contains include instrumentation ("Electric Guitar"), genre ("Jazz"), emotion ("Happy"), usage ("For a Party"), etc. They were collected from human annotators and integrated by "majority voting". However, considering the expertise from different annotators and the difficulty of different pieces, we can come up with a better statistical model for optimal label integration, which would ideally infer the label, as well as the expertise of the annotators and the difficulty of the songs. This work is primarily based on [[http://mplab.ucsd.edu/~jake/OptimalLabeling.pdf|this paper]] in NIPS 2009. | + | [[http://cosmal.ucsd.edu/cal/projects/AnnRet/|Cal500]] is a widely used dataset for music tagging. The tags it contains include instrumentation ("Electric Guitar"), genre ("Jazz"), emotion ("Happy"), usage ("For a Party"), etc. They were collected from human annotators and integrated by "majority voting" (The tags that most people annotated are kept). However, considering the expertise from different annotators and the difficulty of different pieces, we can come up with a better statistical model for optimal label integration, which would ideally infer the label, as well as the expertise of the annotators and the difficulty of the songs. This work is primarily based on [[http://mplab.ucsd.edu/~jake/OptimalLabeling.pdf|this paper]] in NIPS 2009. |
| ===== - Model ===== | ===== - Model ===== | ||
| Line 79: | Line 79: | ||
| Not surprising, Jazz is hard. | Not surprising, Jazz is hard. | ||
| + | |||
| + | ===== - Future work ===== | ||
| + | - At the moment, only binary labels are supported. But in fact, the model is easily extended to handle multinomial labels. | ||
| + | |||
| + | - Now each individual label is treated completely independent. However, in the real world, it's easy to consider the correlation between different tags (e.g. "Rock" is definitely more positively-correlated to "Electric Guitar (Distortion)" than "Sampler"). This can be done by the similar idea from Correlated Topic Model ([[http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2005_774.pdf|CTM]]). | ||
| + | |||
| + | - An interesting yet challenging problem would be to integrate the noisy beat annotations to create better ground truth data for beat tracking tasks. The main difference is that in beat annotation, the labels are no longer discretized categories, instead they are temporally-dependent series, which makes the problem much more difficult. | ||
glad.1372646203.txt.gz ยท Last modified: 2013/06/30 22:36 by dawenl
Page Tools
Except where otherwise noted, content on this wiki is licensed under the following license: CC Attribution-Share Alike 3.0 Unported
