I was very excited by the new ShorftTimeFFT class, and am beginning to
introduce it into my workflow. My team is interested in audio simulation
and source waveform reconstruction from microphone arrays, and the inverse
STFT functionality is a big draw to the new class.
Our first use case is an int
Thank you for your response, Dietrich.
We are currently implementing the differentiation as you suggested: (1)
STFT the signal, (2) multiply each slice by j2πf, (3) ISTFT. We are not
using a rectangular window, though I can see how it performs. I default to
a nuttall for DSP, and its relatively po
> fg1, ax1 = plt.subplots()
> ax1.set(title=rf"STFT-based Differentiator for $f_c={f[k_c]}\,$Hz Signal",
> xlabel="Time $t$", ylabel="Amplitude")
> ax1.plot(t, omega_c * x, alpha=0.5, label=r"$x(t) \cdot 2\pi f_c$")
> ax1.plot(t, y,
