1/31/2024 0 Comments Musicality singers( C) shows mean tonality of improvisation as measured by proportion of tonal notes (z-score) according to group and type of stem. Both groups were above chance and the control group had higher scores than the group with amusia. The lower dashed line represents chance (0.149), as estimated from over one million random permutations generated over the course of the analyses. The upper dashed line represents the score of a professional singer. Each value was calculated as an average from 28 improvisations, except in a handful of cases with truncated recordings. ( B) shows the proportion of trials in which the participant ended their improvisation within 50 cents of the tonic determined by the key-finding algorithm. The lower dashed line represents chance, i.e., z-score of 0. His data provide a benchmark of proficient singing since the measures are standardized with a clear baseline but no clear ceiling. The upper dashed line represents the score of a professional baritone singer with 11 years of formal training and 29 years of total singing experience. Individual data points were calculated as the average of 28 improvisations, which were z-transformed against 1000 randomly generated melodies. ( A) shows the z-transformed proportion of tonal notes. The algorithm is visualized dynamically in Supplementary Materials (Movie S1) and available as a python module (see Code Availability). In short, the algorithm ‘lines up’ the sung notes with the scale as much as possible. The minimum value indicates the transposition or index with the best fit (vertical red line), which can then be used to locate the tonic to the nearest cent. ( G) visualizes the mean negative log likelihood across notes, weighted by note duration, at each transposition. By comparing the PDF distribution in ( E) with the distribution of pitch classes in ( F), a score of how well the distributions overlap (negative log likelihood) can be calculated for each transposition. ( F) visualizes a histogram of notes from the performance as pitch class in cents (i.e., 0–1200), both in their original value (blue bars) and after being transposed together in steps of one cent, covering the entire octave, until the best alignment with the PDF is found (red bars). ( E) visualizes the normalized probability density function (PDF), which used only the in-scale intervals from ( D). ( D) visualizes the probe tone ratings (here, minor mode), representing how well each interval fits within the scale, which were used to scale the height and spread of the Gaussians in the normalized probability density function. ( C) visualizes the semi-automated annotation of note boundaries (blue boxes) and pitch (black line) in the program Tony. ( B) visualizes the waveform of an actual improvisation (Audio S1, amusic). ( A) visualizes the general progression of the study. Overview of the task and key-finding algorithm. The findings are a proof of concept that improvisation can serve as a novel, even enjoyable method for systematically measuring hidden aspects of musicality across the spectrum of musical ability. The results show signatures of tonality in both nonmusicians and individuals with congenital amusia, who have notorious difficulty performing musical tasks that require explicit responses and memory. To assess the extent to which each improvisation reflects tonality, which has been proposed to be a core organizational principle of musicality and which is present within most music traditions, we developed a new algorithm that compares a sung excerpt to a probability density function representing the tonal hierarchy of Western music. Each sang 28 long improvisations as a response to a verbal prompt or a continuation of a melodic stem. Here, we exploit this natural inclination to probe implicit musical knowledge in 33 untrained and poor singers (amusia). Humans spontaneously invent songs from an early age.
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