Acoustic Intensity Causes Perceived Changes in Arousal Levels in Music: An Experimental Investigation

Dean, Roger T.; Bailes, Freya; Schubert, Emery

doi:10.1371/journal.pone.0018591

Cited by 59 publications

(76 citation statements)

References 27 publications

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“…For example, we have recently presented evidence that the perception of loudness in a well-studied Dvorak Slavonic Dance is driven mainly 'bottom-up', bearing a very high correlation with intensity almost throughout the studied extract (Ferguson, Schubert, & Dean, 2011). Correspondingly we have been able to demonstrate by experimental manipulations of intensity profiles that in this particular piece, and in three other stylistic diverse pieces, intensity can be a major driver of perception of change and expressed arousal (Dean, Bailes, & Schubert, 2011). In the same paper we also showed that intensity might have actions in addition to those mediated via perceived loudness, a matter for future investigation.…”

Section: Computational Cognitive Modelling and Its Potential Applicatsupporting

confidence: 64%

Modelling Perception of Structure and Affect in Music: Spectral Centroid and Wishart's Red Bird

Dean¹,

Bailes²

2011

Empirical Musicology Review

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Pearce (2011) provides a positive and interesting response to our article on time series analysis of the influences of acoustic properties on real-time perception of structure and affect in a section of Trevor Wishart's Red Bird (Dean & Bailes, 2010). We address the following topics raised in the response and our paper. First, we analyse in depth the possible influence of spectral centroid, a timbral feature of the acoustic stream distinct from the high level general parameter we used initially, spectral flatness. We find that spectral centroid, like spectral flatness, is not a powerful predictor of real-time responses, though it does show some features that encourage its continued consideration. Second, we discuss further the issue of studying both individual responses, and as in our paper, group averaged responses. We show that a multivariate Vector Autoregression model handles the grand average series quite similarly to those of individual members of our participant groups, and we analyse this in greater detail with a wide range of approaches in work which is in press and continuing. Lastly, we discuss the nature and intent of computational modelling of cognition using acoustic and music-or information theoretic data streams as predictors, and how the music-or information theoretic approaches may be applied to electroacoustic music, which is 'sound-based' rather than note-centred like Western classical music.Submitted 2011 September 30; accepted 2011 October 10.KEYWORDS: time series analysis; musical structure; musical affect; information theoretic analysis; computational modelling of cognition; electroacoustic music. PEARCE (2011) supports our focus on studying real-time responses to music, and appreciates our introducing the methods of time series analysis (which have been rarely used in music studies) and using computer-mediated electroacoustic music as part of this analysis. Indeed, in our ongoing work, we make a point of contrasting and comparing responses to the 'sound-centered' electroacoustic musics, as for example Landy (2009) characterizes them, with those to 'note-centered' music, such as piano music from the Western classical music tradition. Our work allows demonstration not only of the predictive capacity of acoustic information streams for perception of musical structure and affect, but also of interactions between individual perceptual/cognitive responses, and their autoregressive properties.In this article, we respond to three main topics raised by Pearce's comments and by our work to date: the possible influence of the acoustic parameter spectral centroid on perceptions of musical structure and affect; the comparison between group average perceptual responses and individual responses; and finally the nature of computational cognitive modelling and possibilities for developing information theoretic aspects of it in relation to electroacoustic music.

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Section: Computational Cognitive Modelling and Its Potential Applicatsupporting

confidence: 64%

Modelling Perception of Structure and Affect in Music: Spectral Centroid and Wishart's Red Bird

Dean¹,

Bailes²

2011

Empirical Musicology Review

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“…To further assess mechanistic causalities, intervention experiments are called for, such as those that we earlier undertook successfully in relation to the influence of acoustic intensity change profiles upon the continuous perception of affect (Dean, Bailes, & Schubert, 2011). The time series analysis approach is one way to reach a level of understanding of the complex temporal relationships that can guide such intervention studies.…”

Section: Discussionmentioning

confidence: 99%

Dangers and uses of cross-correlation in analyzing time series in perception, performance, movement, and neuroscience: The importance of constructing transfer function autoregressive models

2015

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Many articles on perception, performance, psychophysiology, and neuroscience seek to relate pairs of time series through assessments of their cross-correlations. Most such series are individually autocorrelated: they do not comprise independent values. Given this situation, an unfounded reliance is often placed on cross-correlation as an indicator of relationships (e.g., referent vs. response, leading vs. following). Such cross-correlations can indicate spurious relationships, because of autocorrelation. Given these dangers, we here simulated how and why such spurious conclusions can arise, to provide an approach to resolving them. We show that when multiple pairs of series are aggregated in several different ways for a cross-correlation analysis, problems remain. Finally, even a genuine cross-correlation function does not answer key motivating questions, such as whether there are likely causal relationships between the series. Thus, we illustrate how to obtain a transfer function describing such relationships, informed by any genuine cross-correlations. We illustrate the confounds and the meaningful transfer functions by two concrete examples, one each in perception and performance, together with key elements of the R software code needed. The approach involves autocorrelation functions, the establishment of stationarity, prewhitening, the determination of cross-correlation functions, the assessment of Granger causality, and autoregressive model development. Autocorrelation also limits the interpretability of other measures of possible relationships between pairs of time series, such as mutual information. We emphasize that further complexity may be required as the appropriate analysis is pursued fully, and that causal intervention experiments will likely also be needed.Keywords Autocorrelation . Autoregression . Cross-correlation . Continuous responses . Time series analysis . Granger causality . Prewhitening . Transfer function modelingThe study of relationships between simultaneous time series, particularly those involving continuous human perceptions and performance, has been ongoing in many fields of psychology for several decades (e.g., Brunsdon & Skinner, 1987;Gregson, 1983;Pressing, 1999). To illustrate the ubiquity of time series data in these fields, consider the categorization provided by Pressing (1999) in his synthesis of "the referential dynamics of cognition and action". Pressing enunciates "referential behavior theory," which is a "general dynamical approach to psychological . . . systems that operate through a control or referencing process" (p. 714)-specifically, processes that operate continuously. The analytical approach to such continuous processes is most commonly discrete, in that the data are samples of the process spaced regularly in time, and this is what concerns us here. The resulting "discrete control equation" is normally an example of a vector (multivariate) autoregression time series analysis model of the paired (or several) continuous response/performance time series, w...

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“…Intensity changes also substantially drive perception of affect in most pieces, and we showed with an empirical intervention that changing the intensity profile of certain pieces could correspondingly change the perception of their expressed arousal (Dean, Bailes, & Schubert, 2011).…”

mentioning

confidence: 81%

“…For valence, even intensity was no longer required in the optimal model, but its degree of fit remained poor, and this observation is not powerful. The result suggests that indeed event density may well be influential particularly on the perception of arousal, and that this hypothesis will bear further investigation by means of experimental perturbation studies manipulating event density, such as we have previously conducted with acoustic intensity (Dean et al, 2011). In the case of event density, such experiments will require specially generated stimuli, or substantial segment by segment speeding and slowing of audio stimuli.…”

Section: Analyzing the Impact Of Agency: Dissecting Perceived Change mentioning

confidence: 93%

“…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. For example, using short simple (pitch-centered) tonal items that were algorithmically composed to permit parameter control, 77-89% of variance in the components of the circumplex model, though labeled differently, could be explained by six musical cues: mode, tempo, dynamics, articulation, timbre, and register (Eerola, Friberg, & Bresin, 2013).…”

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confidence: 99%

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Modeling Perceptions of Valence in Diverse Music

Dean

Bailes

2016

Music Perception

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WE INVESTIGATE THE ROLES OF THE ACOUSTIC parameters intensity and spectral flatness in the modeling of continuously measured perceptions of affect in nine diverse musical extracts. The extract sources range from Australian Aboriginal and Balinese music, to classical music from Mozart to minimalism and Xenakis; and include jazz, ambient, drum n' bass and performance text. We particularly assess whether modeling perceptions of the valence expressed by the music, generally modeled less well than the affective dimension of arousal, can be enhanced by inclusion of perceptions of change in the sound, human agency, musical segmentation, and random effects across participants, as model components. We confirm each of these expectations, and provide indications that perceived change in the music may eventually be subsumed adequately under its components such as acoustic features and agency. We find that participants vary substantially in the predictors useful for modeling their responses (judged by the random effects components of mixed effects crosssectional time series analyses). But we also find that pieces do too, while yet sharing sufficient features that a single common model of the responses to all nine pieces has competitive precision.

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Acoustic Intensity Causes Perceived Changes in Arousal Levels in Music: An Experimental Investigation

Cited by 59 publications

References 27 publications

Modelling Perception of Structure and Affect in Music: Spectral Centroid and Wishart's Red Bird

Modelling Perception of Structure and Affect in Music: Spectral Centroid and Wishart's Red Bird

Dangers and uses of cross-correlation in analyzing time series in perception, performance, movement, and neuroscience: The importance of constructing transfer function autoregressive models

Modeling Perceptions of Valence in Diverse Music

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