Beyond valence and activity in the emotional connotations of music

Collier, Geoffrey L.

doi:10.1177/0305735607068890

Cited by 42 publications

(33 citation statements)

References 29 publications

(49 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…3 and 6 can be interpreted using more than one affective dimension. This result is in line with previous works which found an inherent ambiguity in the relation between music and emotions [37]. It is worth to remember that the clusters and structures of Figs.…”

Section: Discussionsupporting

confidence: 92%

“…In other words, given that the most variance is explained by the V-A plane, it is difficult to explain the residual variance finding significative correlation with other affective dimensions. To overcome this limitation, Collier [37] followed an experimental paradigm that enforces more subtle emotional distinctions. Listeners were asked to evaluate 10 musical selections in five different conditions: the first uses a set of affective adjectives from the entire V-A plane; the others ones use a set of adjectives belonging to only one quadrant of the V-A plane each time.…”

Section: Pointing Out Secondary Dimensionsmentioning

confidence: 99%

See 1 more Smart Citation

Clustering Affective Qualities of Classical Music: Beyond the Valence-Arousal Plane

Rodà

Canazza

Poli

2014

IEEE Trans. Affective Comput.

View full text Add to dashboard Cite

The important role of the valence and arousal dimensions in representing and recognizing affective qualities in music is well established. There is less evidence for the contribution of secondary dimensions such as potency, tension and energy. In particular, previous studies failed to find significant relations between computable musical features and affective dimensions other than valence and arousal. Here we present two experiments aiming at assessing how musical features, directly computable from complex audio excerpts, are related to secondary emotion dimensions. To this aim, we imposed some constraints on the musical features, namely modality and tempo, of the stimuli.The results show that although arousal and valence dominate for many musical features, it is possible to identify features, in particular Roughness, Loudness, and SpectralFlux, that are significantly related to the potency dimension. As far as we know, this is the first study that gained more insight into the affective potency in the music domain by using real music recordings and a computational approach

show abstract

Section: Discussionsupporting

confidence: 92%

Section: Pointing Out Secondary Dimensionsmentioning

confidence: 99%

Clustering Affective Qualities of Classical Music: Beyond the Valence-Arousal Plane

Rodà

Canazza

Poli

2014

IEEE Trans. Affective Comput.

View full text Add to dashboard Cite

show abstract

“…A description of emotional effects of music limited to valence and arousal gradations as basic dimensions of emotion (e.g., Russell, 2003;Watson, Wiese, Vaidya, & Tellegen, 1999) precludes assessment of the kind of qualitative differentiation required by the study of the emotional effects of music (Collier, 2007). Although we do not question that emotional ratings of music can, to an extent, be accounted for by arousal and valence dimensions, this result adds little to an understanding of those emotional qualities that make music's emotional effects so uniquely rewarding.…”

Section: Current Emotion Models: a Procrustean Bed Formentioning

confidence: 99%

“…Rather, they were asked to describe the music in emotional terms. A more recent characterization of basic dimensions of musical affect beyond valence and arousal was also based on listeners' perceptions of emotional connotations of the music, not on anything the listeners felt themselves (Collier, 2007). As pointed out by Sloboda (1992), "The relevance of these [earlier] studies to emotional experience, however, is not proven.…”

Section: Previous Research Into Musical Emotionsmentioning

confidence: 99%

Emotions evoked by the sound of music: Characterization, classification, and measurement.

Zentner

Grandjean

Scherer

2008

Emotion

947

1,013

View full text Add to dashboard Cite

One reason for the universal appeal of music lies in the emotional rewards that music offers to its listeners. But what makes these rewards so special? The authors addressed this question by progressively characterizing music-induced emotions in 4 interrelated studies. Studies 1 and 2 (n ϭ 354) were conducted to compile a list of music-relevant emotion terms and to study the frequency of both felt and perceived emotions across 5 groups of listeners with distinct music preferences. Emotional responses varied greatly according to musical genre and type of response (felt vs. perceived). Study 3 (n ϭ 801)-a field study carried out during a music festival-examined the structure of music-induced emotions via confirmatory factor analysis of emotion ratings, resulting in a 9-factorial model of music-induced emotions. Study 4 (n ϭ 238) replicated this model and found that it accounted for music-elicited emotions better than the basic emotion and dimensional emotion models. A domain-specific device to measure musically induced emotions is introduced-the Geneva Emotional Music Scale.

show abstract

“…Valence accounts for pleasantness and unpleasantness. This two-dimensional arousal-valence emotion model cannot very well distinguish finitely generated emotions by music such as calm and bored [Collier 2007]. Fontaine et al [2007] indicated that emotion was not only two-dimensional.…”

Section: Emotion Models In Previous Workmentioning

confidence: 99%

Emotional States Associated with Music

Deng

Leung

Milani

et al. 2015

ACM Trans. Interact. Intell. Syst.

View full text Add to dashboard Cite

We present several interrelated technical and empirical contributions to the problem of emotion-based music recommendation and show how they can be applied in a possible usage scenario. The contributions are (1) a new three-dimensional resonance-arousal-valence model for the representation of emotion expressed in music, together with methods for automatically classifying a piece of music in terms of this model, using robust regression methods applied to musical/acoustic features; (2) methods for predicting a listener's emotional state on the assumption that the emotional state has been determined entirely by a sequence of pieces of music recently listened to, using conditional random fields and taking into account the decay of emotion intensity over time; and (3) a method for selecting a ranked list of pieces of music that match a particular emotional state, using a minimization iteration method. A series of experiments yield information about the validity of our operationalizations of these contributions. Throughout the article, we refer to an illustrative usage scenario in which all of these contributions can be exploited, where it is assumed that (1) a listener's emotional state is being determined entirely by the music that he or she has been listening to and (2) the listener wants to hear additional music that matches his or her current emotional state. The contributions are intended to be useful in a variety of other scenarios as well.

show abstract

Beyond valence and activity in the emotional connotations of music

Cited by 42 publications

References 29 publications

Clustering Affective Qualities of Classical Music: Beyond the Valence-Arousal Plane

Clustering Affective Qualities of Classical Music: Beyond the Valence-Arousal Plane

Emotions evoked by the sound of music: Characterization, classification, and measurement.

Emotional States Associated with Music

Contact Info

Product

Resources

About