The aim of this study is to manipulate musical cues systematically to determine the aspects of music that contribute to emotional expression, and whether these cues operate in additive or interactive fashion, and whether the cue levels can be characterized as linear or non-linear. An optimized factorial design was used with six primary musical cues (mode, tempo, dynamics, articulation, timbre, and register) across four different music examples. Listeners rated 200 musical examples according to four perceived emotional characters (happy, sad, peaceful, and scary). The results exhibited robust effects for all cues and the ranked importance of these was established by multiple regression. The most important cue was mode followed by tempo, register, dynamics, articulation, and timbre, although the ranking varied across the emotions. The second main result suggested that most cue levels contributed to the emotions in a linear fashion, explaining 77–89% of variance in ratings. Quadratic encoding of cues did lead to minor but significant increases of the models (0–8%). Finally, the interactions between the cues were non-existent suggesting that the cues operate mostly in an additive fashion, corroborating recent findings on emotional expression in music (Juslin and Lindström, 2010).

There have been few empirical investigations of how individual differences influence the perception of the sonic environment. The present study included the Big Five traits and noise sensitivity as personality factors in two listening experiments (n = 43, n = 45). Recordings of urban and restaurant soundscapes that had been selected based on their type were rated for Pleasantness and Eventfulness using the Swedish Soundscape Quality Protocol. Multivariate multiple regression analysis showed that ratings depended on the type and loudness of both kinds of sonic environments and that the personality factors made a small yet significant contribution. Univariate models explained 48% (cross-validated adjusted R²) of the variation in Pleasantness ratings of urban soundscapes, and 35% of Eventfulness. For restaurant soundscapes the percentages explained were 22% and 21%, respectively. Emotional stability and noise sensitivity were notable predictors whose contribution to explaining the variation in quality ratings was between one-tenth and nearly half of the soundscape indicators, as measured by squared semipartial correlation. Further analysis revealed that 36% of noise sensitivity could be predicted by broad personality dimensions, replicating previous research. Our study lends empirical support to the hypothesis that personality traits have a significant though comparatively small influence on the perceived quality of sonic environments.

The notion of perceptual features is introduced for describing general music properties based on human perception. This is an attempt at rethinking the concept of features, aiming to approach the underlying human perception mechanisms. Instead of using concepts from music theory such as tones, pitches, and chords, a set of nine features describing overall properties of the music was selected. They were chosen from qualitative measures used in psychology studies and motivated from an ecological approach. The perceptual features were rated in two listening experiments using two different data sets. They were modeled both from symbolic and audio data using different sets of computational features. Ratings of emotional expression were predicted using the perceptual features. The results indicate that (1) at least some of the perceptual features are reliable estimates; (2) emotion ratings could be predicted by a small combination of perceptual features with an explained variance from 75% to 93% for the emotional dimensions activity and valence; (3) the perceptual features could only to a limited extent be modeled using existing audio features. Results clearly indicated that a small number of dedicated features were superior to a "brute force" model using a large number of general audio features. (C) 2014 Acoustical Society of America.

Vocal sound imitations provide a new challenge for understanding the coupling between articulatory mechanisms and the resulting audio. In this study, the classification of three articulatory categories, phonation, supraglottal myoelastic vibrations, and turbulence, have been modeled from audio recordings. Two data sets were assembled, consisting of different vocal imitations by four professional imitators and four non-professional speakers in two different experiments. The audio data were manually annotated by two experienced phoneticians using a detailed articulatory description scheme. A separate set of audio features was developed specifically for each category using both time-domain and spectral methods. For all time-frequency transformations, and for some secondary processing, the recently developed Auditory Receptive Fields Toolbox was used. Three different machine learning methods were applied for predicting the final articulatory categories. The result with the best generalization was found using an ensemble of multilayer perceptrons. The cross-validated classification accuracy was 96.8% for phonation, 90.8% for supraglottal myoelastic vibrations, and 89.0% for turbulence using all the 84 developed features. A final feature reduction to 22 features yielded similar results.

Sensory valuation is a fundamental aspect of cognition. It involves assigning hedonic value to a stimulus based on its sensory information considering personal and contextual factors. Hedonic values (e.g., liking) can be deemed affective states that motivate behavior, but the relations between hedonic and affective judgments have yet to be established. To fill this gap, we investigated the relations between stimulus features, perceived affect, and liking across domains and with potentially relevant individual traits. Fifty-eight participants untrained in music and visual art rated their liking and perceived valence and arousal for visual designs and short melodies varying in balance, contour, symmetry, or complexity and filled out several questionnaires. First, we examined group-level relations between perceived affect and liking across domains. Second, we inspected the relations between the individual use of musical and visual properties in judgments of liking and perceived affect-that is, between aesthetic and perceived-affect sensitivities. Third, we inquired into the influence of information-related (need for cognition, or NFC) and affect-related (need for emotion) traits on individual sensitivities. We found domain-specific effects of the stimulus features on liking, a linear association between valence and liking, the inverted-U model of arousal and liking, a binary profile of musical aesthetic sensitivities, and a modulatory effect of NFC on how people use stimulus properties in their hedonic and affective judgments. In summary, the results suggest that hedonic value is primarily computed from domain-specific sensory information partially moderated by NFC. (PsycInfo Database Record (c) 2022 APA, all rights reserved).