[Here](https://dsp.stackexchange.com/questions/26358/how-to-detect-whether-a-signal-is-unimodal-or-bimodal), and in the stats stackexchange, seem to be answers that reference tests for bimodal distributions that involve iterative binning or iterative curve fitting methods. However "eyeballing" a plot of a data set often shows a clear bimodality (say a 10 dB dip or several standard deviations between two clear mode peaks, etc.), versus a single "hump", or something ambiguous (less than a 3 dB dip).

Are there any lightweight algorithms (computationally efficient, single pass, or deterministic low iteration count tests) which can separate out the easy cases (clearly unimodal, or clearly bimodal), versus data sets that may require more computationally intensive tests for likelihood of multi or bi-modality?

Or, in signal processing terms, how can one non-graphically and quickly test whether some area in a noisy spectrogram is more likely to contain 2-tone FSK (with an unknown separation), rather than noise or some other kind of modulated signal?