estimate_frequency
Builtin index · Signal processing
result = dominant_frequency(x, [fs])
Input: x, fs · Output: result
What it is
x: a length-N real vector or Signal; fs (optional number, Hz) is read off a Signal input the same way welch/rfft do, or passed explicitly — a plain vector with neither returns a normalized frequency in cycles/sample instead of Hz.
Overloads
One call shape — this function is not overloaded.
| Code | Input | Output | Description |
result = dominant_frequency(x, [fs]) | x vector or Signalx: a length-N real vector or Signal.fs number, Signal, or vector, optionalfs (optional number, Hz) is read off a Signal input the same way welch/rfft do, or passed explicitly — a plain vector with neither returns a normalized frequency in cycles/sample instead of Hz. | result number | Returns a single number: the frequency of the largest non-DC bin in x's one-sided spectrum, accurate to the bin's own resolution fs/N. The cheapest of the estimators a full sine-fit or phase-regression method would improve on; both names are the same function. |
Examples
No documentation example calls this form yet. The Signal processing chapter is where one belongs.
How it works
dominant_frequency(x, [fs]) / estimate_frequency(x, [fs]) -- the Hz of the largest non-DC bin in x's one-sided spectrum, the cheapest of the estimators toolkit-signal.md §6 names (zero-crossing, FFT interpolation, phase regression, least squares, and super-resolution methods for closely-spaced tones are not implemented here; this is the FFT-bin estimator only, with the bin's own resolution fs/N as its error bound). fs is read off a Signal input the same way welch/rfft do, or passed explicitly; a bare vector with no rate returns a normalized frequency in cycles/sample.
From the note above this builtin in the interpreter, where the reasoning was written down as it was built.
See also
dominant_frequency — documented in the same entry.