![]() ![]() My experience of advanced digital techniques is that they sound somehow lower quality. I'll leave this here, just in case the different framing is useful.įrom a musical perspective the analog mental model produces very clean and high quality results. And heaven help you if the different loops' bass drums are tuned to marginally different notes.Įdit: Only after writing this did it sink in that the top overall comment's remarks (by "highd") about earlier dimensional reduction were getting at the same issue. But I suspect that right now your analysis is spending a huge fraction of its effort effectively trying to get the first drum beat to happen at precisely the right fraction of a second, and separately to get the second drum beat to happen at precisely the right fraction of a second, and separately the third, and the fourth, and so on. Maybe that's not the right answer: like I said, I'm not an audio processing expert. My first instinct is to say "take the FFT of each loop first, and then run your PCA on that". The near-perfect cancellation of the average track illustrates this: two loops of the exact same bass drum beat offset by a fraction of a second will be treated as orthogonal or even opposite by this algorithm, but they're essentially identical as perceived by the listener.Ĭonceptually, I imagine what you'd want is some way of encoding the various loops whose average came out sounding like a real average, rather than as nearly silent due to phase cancellation. I feel like this approach will inevitably put a whole lot of (unhelpful!) emphasis on the detailed phase information of the various sounds. Here's my worry, speaking as someone who knows physics but not all that much audio processing. And goes to show what can be built with traditional data analysis techniques and a clever idea!ĮDIT: Also if you uploaded your preprocessed data I think that would be really amazing.įirst and foremost: This is really cool, and thank you for sharing! (It's also the first explanation of the terms in the matrix equation for SVD that I've happened across that has really clocked for me: much appreciated.) I would love to play with some sort of live music generation system based on this - really, really interesting ideas. those will each require two eigenvectors to represent the sine or cosine phases of the signal - but if you could "project" every kick drum sound so they were close to linearly related then PCA could isolate them with fewer eigenvectors. For example, if you have kick drums with fundamentals at 21Hz, 22Hz, 23Hz, 24Hz etc. ![]() That make make it so not all of the first 20 or so eigenvectors all have slightly different kick drums. you could do a short-time fourier transform and then threshold and bin frequencies - then invert the transform to reduce the sounds to their basic characteristics. That might make more of the melodies come through the eigenvectors.Ģ) Visualize the loop point cloud - maybe with like 10-50 dimensions of PCA followed by 2 or 3 dimensions T-SNE.ģ) Maybe some form of earlier dimensionality reduction? I.e. Some other ideas that would be really cool:ġ) Do key detection and pitch-shift all the loops to a common key before processing.
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