[HN Gopher] Eigentechno - Applying Principal Component Analysis ...
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       Eigentechno - Applying Principal Component Analysis to electronic
       music loops
        
       Author : zorked
       Score  : 18 points
       Date   : 2020-10-18 20:22 UTC (2 hours ago)
        
 (HTM) web link (www.math.uci.edu)
 (TXT) w3m dump (www.math.uci.edu)
        
       | lgrebe wrote:
       | Neat. Waiting for "the drop" or changes in the music that "tickle
       | my senses" makes me wonder if an emergent Property (the
       | pleasurable sound from the composition of base, kick, Snares,
       | "woop" in this case) can be covered with PCA.
       | 
       | More than the sum of its parts (emergent) is to be reduced to its
       | principal components.
        
       | Der_Einzige wrote:
       | Have you considered trying more sophisticated dimensionality
       | reduction algorithms? I'm especially interested in seeing the
       | results of UMAP on this dataset.
       | 
       | Also, what about doing inverse transforms to try to hallucinate
       | music?
        
       | JorgeGT wrote:
       | One of the issues when using PCA (or POD, Karhunen-Loeve
       | expansion... whatever your field calls it) on a dataset is that
       | it optimizes the spectral content of each mode to obtain the best
       | possible orthonormal basis for the reconstruction, so you can get
       | very different frequencies in a single mode. Should you want to
       | restrict this, you could use DMD (Dynamic Mode Decomposition, one
       | frequency only per each mode) or mPOD (Multi-Scale Proper
       | Orthogonal Decomposition, a restricted set of frequencies per
       | mode).
        
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       (page generated 2020-10-18 23:00 UTC)