A unitary model of auditory frequency change perception
Fig 5
(A) Schematic of cue computation and integration, illustrated with a pair of harmonic sounds shifted by 11 and 1 st along the SFS and SE dimension, respectively. For the AC cue, autocorrelations within each auditory band are computed and subsequently summed across bands. From the resulting summed autocorrelation function, peaks are extracted from a search range corresponding to 64–128 Hz (gray patch) and subtracted, yielding an estimate of the periodicity difference of the two sounds; positive differences indicate downward movement. CC cues are computed using the rms-energy of bands across time, followed by a logarithm (conversion to level-domain) and thresholding below -30 dB peak level. The CCres cue considers the presumably resolved part of the excitation pattern below 905 Hz (geometric mean of the frequency of the 10th partial component across Exps. 1 and 2); the CCunres cue considers the unresolved part above that frequency limit. For both types of cues, the remaining excitation patterns are cross-correlated and the centroid (first moment) is computed in a search range (gray patches). Positive centroids indicate downward movement. Cues are integrated in a weighted sum (positive sum coded as “down”). (B) Possible cue weightings of sum one. (C) Cue weights projected onto x-y plane; the farther a point’s distance to a specific edge, the less weight the respective cue receives. The corresponding model fit is displayed on z-axis for one human listener’s results in the inharmonic SFS-EN condition. The dashed line shows the optimal weights for this example.