Comparative Time Series Analysis of Perceptual Responses to Electroacoustic Music

Abstract

this study investigates the relationship between acoustic patterns in contemporary electroacoustic compositions, and listeners' real-time perceptions of their structure and affective content. Thirty-two participants varying in musical expertise (nonmusicians, classical musicians, expert computer musicians) continuously rated the affect (arousal and valence) and structure (change in sound) they perceived in four compositions of approximately three minutes duration. Time series analyses tested the hypotheses that sound intensity influences listener perceptions of structure and arousal, and spectral flatness influences perceptions of structure and valence. Results suggest that intensity strongly influences perceived change in sound, and to a lesser extent listener perceptions of arousal. Spectral flatness measures were only weakly related to listener perceptions, and valence was not strongly shaped by either acoustic measure. Differences in response by composition and musical expertise suggest that, particularly with respect to the perception of valence, individual experience (familiarity and liking), and meaningful sound associations mediate perception.

Modeling Perceptions of Valence in Diverse Music

“…We also previously found indications that perceptions of agency, particularly human agency (Bailes & Dean, 2012;Dean & Bailes, 2010a), could provide predictors that are useful for valence modeling. By agency we here refer primarily to the broad idea that some acoustic events have physical origins that a listener can detect or at least imagine (for example, waves, wind, or breaking glass), and among these, some have human or animal origins (vocal sounds, clapping).…”

“…Our methods for determining continuous perceptions of musical change, expressed arousal, and valence have been detailed previously (Bailes & Dean 2012). Briefly, at the outset, participants learned to use the computerized continuous response system, by means of a practice trial in each of the ''structure'' and ''affect'' tasks.…”

“…Autoregressive time series analysis with external predictors (ARX). We previously presented extensive tutorial and descriptive introductions to this univariate technique and to multivariate vector autoregression (VARX) describing their application in our field (Bailes & Dean, 2012;Dean & Bailes, 2010a). We have focused on modeling continuous perceptual responses to music.…”

“…The resulting time series of moment-by-moment perceptual data can then be modeled in a bid to understand the dynamic factors that shape perceived affect. In the case of analyses of continuous responses (Schubert, 1996(Schubert, , 1999(Schubert, , 2004, the resultant statistical models of the data have been moderately good in predicting the arousal response, but notably worse with the valence response (Bailes & Dean, 2012;Coutinho & Cangelosi, 2009Dean & Bailes, 2010a). Even in the much larger set of studies that measure discrete retrospective perceptions (which are necessarily much simpler, and contain far fewer data elements to be modeled), the same disparity exists, though there are occasional dramatic exceptions, where the use of a large enough panoply of musical components as predictors or simplified musical stimuli has evinced success.…”

“…We suggest this may partly reflect individuals' overall liking for a piece and for music at large, as suggested by data correlations in the case of both musicians and nonmusicians listening to the music we studied previously. This music comprised electroacoustic pieces by Wishart, Xenakis, and Dean, and piano music by Webern (Bailes & Dean, 2012). It follows that techniques that can model differences between individuals (so called ''random effects'': see Results section for further description of the ''mixed effects'' aspect of our analyses) might provide better models of valence.…”