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I don't quite understand what you mean. Do you mean that in general you don't recommend DFT for signal processing? This seems counter to most approaches in the
by cnity 6y ago
I don't quite understand what you mean. Do you mean that in general you don't recommend DFT for signal processing? This seems counter to most approaches in the field. Issues (aliasing, spectral leakage, etc) arising from DFT more or less iron out with reasonable use of zero padding, sample rate, and windowing.
Edit: I didn't mean for this to sound so closed minded. I don't know that much about more recent audio processing practices and I think you probably know things I don't. If you have any reading about wavelets vs DFT I'd be very thankful and interested.
- waffletower 6y agoThe uncertainty principle is is the key concept that I think is relevant here: https://en.wikipedia.org/wiki/Uncertainty_principle#Harmonic_analysis https://en.wikipedia.org/wiki/Uncertainty_principle#Harmonic... A Discrete Fourier Transform (DFT/FFT) balances frequency vs time resolution based upon its parameterization. In this application, I think that frequency differentiation is less important than time differentiation for events. To get an advantage from utilizing spectral analysis, I think that the STFT can be used with very small overlapping analysis windows. (https://en.wikipedia.org/wiki/Short-time_Fourier_transform https://en.wikipedia.org/wiki/Short-time_Fourier_transform) However, due to the importance of transients I would consider use of wavelet transforms instead (https://en.wikipedia.org/wiki/Wavelet_transform https://en.wikipedia.org/wiki/Wavelet_transform) since they can resolve temporal characteristics better than a DFT.