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Thank you for your question. At this point, this is just to see how PCA works on this kind of data-set. Normally, PCA is used to reduce the dimension of the dat
by umutisik 9y ago
Thank you for your question. At this point, this is just to see how PCA works on this kind of data-set. Normally, PCA is used to reduce the dimension of the data in a way that loses less information. It can be useful for saving computation on classification tasks for example.
- hammock 9y agoIts a cool exploration for curiosity's sake. Seems weird to use it vs Fft though because while some have said PCA is supposed to be "more optimal" , we have prior knowledge that the music is actually constructed along the FFT dimensions. So I wouldnt expect PCA to be any better
- highd 9y agoFFT and PCA are totally different. FFT is a fixed linear transform - PCA finds the linear projection to k-space that retains the most variance from the original dataset. If you projected to the top k frequencies you'd just get a couple of tones, while PCA finds linear combinations of the original signal that are most "descriptive" in a sense. Also human hearing is much closer to a wavelet transform or STFT than FFT.