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I don’t really understand your descriptions. Maybe you can ground it for people better by comparing visuals produced by normal FFT and your novel method. I mi
by undershirt 6y ago
I don’t really understand your descriptions. Maybe you can ground it for people better by comparing visuals produced by normal FFT and your novel method.
I might go further by letting people hear the difference between them (by transposing the visuals back into audio, to ground our sense of quality loss in the original medium, sound).
- avaku 6y agoGreat ideas, will do! Thanks for suggestions!
- undershirt 6y agolooking forward to it! great work so far, congrats on publishing
- avaku 6y agoThank you! Subscribe to the email list on the front page, if you want to get updated. I'll probably release a desktop app soon, also free.
- ssfrr 6y agoThe difficulty he's describing also applies to wavelet transforms: higher frequency bands have wider bandwidth, so they need to be sampled more often. This means the resolution is different in different frequency bands. With the short-time fourier transform (STFT), which is widely used in Music Information Retrieval, each frequency band has the same bandwidth and is sampled at the same rate, so the output of the transform is a rectangular matrix. With a wavelet transform the output is sort of a trapezoid, because you have more samples in higher frequencies. In both cases though it's possible to losslessly round-trip the audio within numerical precision (neither the STFT nor the Wavelet transform lose information).
- avaku 6y agoExactly!