The Six Emotion-Face Clock as a Tool for Continuously Rating Discrete Emotional Responses to Music

Schubert, Emery; Ferguson, Sam; Farrar, Natasha; Taylor, David; McPherson, Gary E.

doi:10.1007/978-3-642-41248-6_1

Cited by 10 publications

(19 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Scales were constructed to provide icon analogues of a range of non-visual sensory ratings, as well as some visual sensory scales (colors, shapes and emotion faces). Sensory scales from several sensory modes (Murari et al, 2014; for a detailed discussion, see Murari et al, 2015a, Da Pos, Dal Mas, & Stelluto, 2012) were included, as well as nonverbal ratings of emotions, using simple emoticon-like faces (as used by Schubert, Ferguson, Farrar, Taylor, & McPherson, 2013) and images of the poles used in the Self-Assessment Manikin (SAM; Bradley & Lang, 1994) which represent the affective dimensions of pleasure, arousal and dominance. Selection of colors for emotion faces was based on previous work (Da Pos & Green-Armytage, 2007; Schubert et al, 2013).…”

Section: Methodsmentioning

confidence: 99%

“…Sensory scales from several sensory modes (Murari et al, 2014; for a detailed discussion, see Murari et al, 2015a, Da Pos, Dal Mas, & Stelluto, 2012) were included, as well as nonverbal ratings of emotions, using simple emoticon-like faces (as used by Schubert, Ferguson, Farrar, Taylor, & McPherson, 2013) and images of the poles used in the Self-Assessment Manikin (SAM; Bradley & Lang, 1994) which represent the affective dimensions of pleasure, arousal and dominance. Selection of colors for emotion faces was based on previous work (Da Pos & Green-Armytage, 2007; Schubert et al, 2013). Specifically, the modalities represented by the icons were emotion-related colors (red, blue, orange, and cyan), temperature (hot, cold), shapes (angular, curved), speed (fast, slow), texture (rough, smooth, soft, hard) and weight (heavy, light).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

How >:( is Bizet? Icon ratings of music

Murari

Schubert

Rodà

et al. 2017

Psychology of Music

Self Cite

View full text Add to dashboard Cite

Can music be rated consistently using icon descriptors without verbal mediation? Sixty-eight participants rated six experimenter- and two self-selected pieces of music along 15 bipolar icon scales intended to portray emotions, and sensory experiences involving color, temperature, shape, speed, texture, and weight. Several replications were reported, including Mozart being blue, Brahms being soft and Bizet being takete (a jagged shape). Crossmodal associations with individual pieces were similar to those reported in previous studies, but self-selected liked and disliked musics did not evoke as many such associations, leading to the conclusion that crossmodal perception may be indicative of music character more so than it is of hedonic tone. The similarity of results in the present study and previous research suggests that icon scales may provide a convenient alternative to sensory scales in various modalities that are difficult to reproduce via current conventional computer interface technology

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

How >:( is Bizet? Icon ratings of music

Murari

Schubert

Rodà

et al. 2017

Psychology of Music

Self Cite

View full text Add to dashboard Cite

show abstract

“…The evaluation of such a rich experience triggers multiple methodological difficulties. Subjective assessment is therefore a common method that can be conducted through post-performance questionnaires or emotion rating during the concerts [22]. Post-performance questionnaires are a good source of information but may be less precise than live subjective reports.…”

Section: Modulesmentioning

confidence: 99%

Toward Augmented Familiarity of the Audience with Digital Musical Instruments

Capra

Berthaut

Grisoni

2018

Music Technology With Swing

View full text Add to dashboard Cite

The diversity and complexity of Digital Musical Instruments often lead to a reduced appreciation of live performances by the audience. This can be linked to the lack of familiarity they have with the instruments. We propose to increase this familiarity thanks to a transdisciplinary approach in which signals from both the musician and the audience are extracted, familiarity analyzed, and augmentations dynamically added to the instruments. We introduce a new decomposition of familiarity and the concept of correspondences between musical gestures and results. This paper is both a review of research that paves the way for the realization of a pipeline for augmented familiarity, and a call for future research on the identified challenges that remain before it can be implemented.

show abstract

“…This allowed the collection of momentto-moment changes in perception and experience at regular and relatively short intervals as the music unfolded (Egermann, Nagel, Altenmüller, & Kopiez, 2009;Schubert, 2004;Zentner & Eerola, 2010). Participant watched or listened to the stimulus and reported its nature by clicking a mouse or pressing a joystick (Mckeown & Sneddon, 2013;Schubert, Ferguson, Farrar, Taylor, & Mcpherson, 2012). First, it allowed the researcher to identify the remembered affect of a stimulus; the listener's affective responses to a stimulus were dependent more on the peak moments rather than the previous moments.…”

mentioning

confidence: 99%

“…It is represented with two orthogonal axes, popularly known as arousal (excited or sleepy) and valence (positive and negative emotion) (Barrett, 2010). The distinction between the two approaches becomes blurred when the intensity of a particular emotion is rated in a discrete scale EMOTIONS AND THEIR INTENSITY IN MUSIC 5 (Schubert et al, 2012). The other major approach, discrete or categorical approach, measures discrete emotions, are a small set of independent emotions on orthogonal monopolar axes, separate from each other and governed by a marked biological and physiological phenomenon (Kragel & LaBar, 2013).…”

mentioning

confidence: 99%

Emotions and Their Intensity in Hindustani Classical Music Using Two Rating Interfaces

Borgohain¹,

Mullick²,

Karambelkar³

et al. 2021

Preprint

View full text Add to dashboard Cite

One of the very popular techniques of assessing music is using the dimensional model. Although it is used in numerous studies, the discrete model is of great importance in the Indian tradition. This study assesses two discrete interfaces for continuous rating of Hindustani classical music. The first interface, the Discrete emotion wheel (DEW), captures the range of eight aesthetic emotions relevant to Hindustani classical music and cited in Natyashastra. The second interface, the Intensity-rating emotion wheel (IEW), assesses the emotional arousal and identifies whether the additional cognitive load interferes with accurate rating. Forty-eight participants rated emotions expressed by five Western and six Hindustani classical clips. Results suggest that both the interfaces work effectively for both the music genres. The intensity-rating emotion wheel was able to capture arousal in the clips where they show higher intensities in the dominant emotions. Implications of the tool for assessing the relation between musical structures, emotions, and time are also discussed.

show abstract

The Six Emotion-Face Clock as a Tool for Continuously Rating Discrete Emotional Responses to Music

Cited by 10 publications

References 42 publications

How >:( is Bizet? Icon ratings of music

How >:( is Bizet? Icon ratings of music

Toward Augmented Familiarity of the Audience with Digital Musical Instruments

Emotions and Their Intensity in Hindustani Classical Music Using Two Rating Interfaces

Contact Info

Product

Resources

About