AI-generated Computed, not drawn Published figures need checking

This spectrum analyzer was generated by Claude (Anthropic) for the DSP 101 course materials. Every curve and every number on it is computed in your browser: the window coefficients; the coherent gain (Σw)/N and the equivalent noise bandwidth N·Σw²/(Σw)² taken straight from the coefficients; the scalloping loss from the window’s own transform at half a bin; the peak sidelobe level and main-lobe width measured off a heavily oversampled FFT of the window; a real radix-2 FFT of the windowed signal; and the detected peak — its bin, its frequency, and its amplitude corrected for the window’s coherent gain, 2|X_peak|/Σw. Nothing here is a plausible-looking curve.

The published figures are the exception, and they carry a flag. The standard window numbers are Harris (1978) Table 1. This page was written with no network access and no copy of the source in the repository, so they were transcribed from the author’s knowledge rather than read off the document. They are shown beside the measured value, which is the real cross-check, and raise a visible “verify against the source” flag. Do not clear that flag by asserting the numbers are right — clear it by checking them, and record who checked.

Window figures: F. J. Harris, “On the use of windows for harmonic analysis with the discrete Fourier transform,” Proc. IEEE, vol. 66, no. 1, pp. 51–83, Jan. 1978, Table 1. Algorithm: J. W. Cooley and J. W. Tukey, “An algorithm for the machine calculation of complex Fourier series,” Math. Comput., vol. 19, pp. 297–301, 1965.

Chosen rather than computed, and marked where it matters: the default tone frequencies and amplitudes, the default noise level and the PRNG seed. The noise is a real seeded Gaussian draw, so its statistics are computed — only the choice of level is illustrative. There is no microphone and no audio; the signal is synthesised in the page.

Spectrum analyzer — window figures of merit, and reading a peak off the FFT

Course demo — linked from the Module 6 lesson deck; the page itself is English‑only for now. Built for DSP-101 Module 6 (the FFT and spectral analysis). Put a tone (plus noise) through a windowed FFT, detect the peak, and read back its frequency and its amplitude — the amplitude corrected for the window’s coherent gain. Watch the four Harris figures of merit — ENBW, coherent gain, scalloping loss, peak sidelobe — computed from the coefficients and shown beside the published values. The analyzer’s central trade is on screen: a narrow window resolves close tones but its high sidelobes hide weak ones; a wide window sees the weak tone but merges the close pair. Resolution against dynamic range.

 

 

 

 

 
 

 

 
 

 

 
 

 

 
 

  

10.00

 

 

1.00

 

 

13.00

 

 

-40

 

off

 

 

7

 

  

 

 

64

 

 

  

 

 

120

 

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4 

 

 

1 

 

 

2 

 

 

5 

 

 

 

5 

 

 

The arithmetic, in full