This paper compares multi-step algorithms for estimating banding arameters of a harmonic signature model. The algorithms are based on two different spectral measures, the power spectrum (PS) and the collapsed average (CA) of the generalized spectrum. The generalized spectrum has superior noise reduction properties and is applied for the first time to this application. Monte Carlo simulations compare estimation performances of profile (or coherent) averaging and non-coherent spatial averaging for estimating banding parameters in grain noise. Results demonstrate that profile averaging has superior noise reduction properties, but is less flexible in applications with irregular banding patterns. The PS-based methods result in lower fundamental frequency estimation error and greater peak height stability for low SNR values, with coherent averaging being significantly superior to non-coherent averaging. The CA has the potential of simplifying the detection of multiple simultaneous banding patterns because its peaks are related to intra-harmonic distances; however, good CA estimation performance requires sufficiently regular harmonic phase patterns for the banding harmonics so as not to undergo reduction along with the noise. In addition to the simulations, the algorithms are applied to samples from inkjet and laser printers to demonstrate the ability of the harmonic signature model in separating banding from grain and other image artifacts. Good results from experimental data are demonstrated based on visual inspection of examples where banding and grain have been separated.