.png){kind=icon}
.png){kind=icon}
Return
Musical key extraction using diffusion maps
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2015.05.006.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We propose a method for automatic musical key extraction using a two-stage spectral dimensionality reduction (two consecutive mappings). First we build a data set representing the 24 Western musical keys, and then we use a nonlinear dimensionality reduction method, in order to understand the true manifold on which the musical keys lie. The order of the keys along the manifold is perfectly correlated with a cognitive model for the key space. We exploit this manifold in order to extract the musical key from a musical piece. Furthermore we propose three classifiers using the extracted manifold. The Classifiers Work in two stages, by first estimating the mode and then by estimating the key within the estimated mode. Finally we examine our method on The Beatles data set and demonstrate its improved performance compared to various existing methods. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Key extraction
Dimensionality reduction
Diffusion maps
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.6
Papers:
9.9K
Citations:
1.7W