The dependency of timbre on fundamental frequency

Abstract

The dependency of the timbre of musical sounds on their fundamental frequency (F 0 ) was examined in three experiments. In experiment I subjects compared the timbres of stimuli produced by a set of 12 musical instruments with equal F 0 , duration, and loudness. There were three sessions, each at a different F 0 . In experiment II the same stimuli were rearranged in pairs, each with the same difference in F 0 , and subjects had to ignore the constant difference in pitch. In experiment III, instruments were paired both with and without an F 0 difference within the same session, and subjects had to ignore the variable differences in pitch. Experiment I yielded dissimilarity matrices that were similar at different F 0 's, suggesting that instruments kept their relative positions within timbre space. Experiment II found that subjects were able to ignore the salient pitch difference while rating timbre dissimilarity. Dissimilarity matrices were symmetrical, suggesting further that the absolute displacement of the set of instruments within timbre space was small. Experiment III extended this result to the case where the pitch difference varied from trial to trial. Multidimensional scaling ͑MDS͒ of dissimilarity scores produced solutions ͑timbre spaces͒ that varied little across conditions and experiments. MDS solutions were used to test the validity of signal-based predictors of timbre, and in particular their stability as a function of F 0 . Taken together, the results suggest that timbre differences are perceived independently from differences of pitch, at least for F 0 differences smaller than an octave. Timbre differences can be measured between stimuli with different F 0 's.

Separate Neural Processing of Timbre Dimensions in Auditory Sensory Memory

“…These acoustic parameters fall into three broad categories: temporal, spectral, and spectrotemporal descriptors. Consistently across studies, attack time (more precisely its logarithm) and spectral centroid (weighted mean of spectrum energy) have been reported as correlates of timbre space dimensions (see, e.g., Marozeau, de Cheveigné, McAdams, & Winsberg, 2003;Samson, Zatorre, & Ramsay, 1997;McAdams et al, 1995;Krumhansl, 1989;Grey, 1977). Attack time separates impulsive from sustained instruments, and spectral centroid reflects the global spectral distribution.…”

“…Attack time separates impulsive from sustained instruments, and spectral centroid reflects the global spectral distribution. Other parameters have been less systematically reported: spectrum fine structure (Krimphoff, McAdams, & Winsberg, 1994), spectral spread (Marozeau et al, 2003), or spectral flux (McAdams et al, 1995), the latter being a measure of the variation of the spectrum over time. Altogether, these studies confirm that timbre indeed comprises several perceptual dimensions, with each of these dimensions corresponding to a single acoustic parameter.…”

Preparation of stimuli for timbre perception studies

“…Most multidimensional timbre studies consistently find dimensions correlating to brightness (T b ) (Grey, 1977;Marozeau et al, 2003;McAdams et al, 1995;Krimphoff et al, 1994) and often spectral flux is also found as a dimension (Grey, 1977;McAdams et al, 1995). In McAdams et al (1995) and Krimphoff et al (1994), one dimension also corresponds to the attack (characterized by the LRT).…”

Perceptually-Driven Signal Analysis for Acoustic Event Classification

“…As timbre is most likely a multidimensional attribute, most studies in timbre rely on a numerical technique known as multidimensional scaling (MDS) [1,[10][11][12][13][14][15]. MDS is a nonlinear data analysis technique which takes known (perceptual) distances between data points and identifies a low dimensional Euclidian space that maintains those distances [16,17].…”