Happy, sad, scary and peaceful musical excerpts for research on emotions

Vieillard, Sandrine; Peretz, Isabelle; Gosselin, Nathalie; Khalfa, S.; Gagnon, Lise; Bouchard, Bernard

doi:10.1080/02699930701503567

Cited by 287 publications

(418 citation statements)

References 47 publications

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“…The stimuli were 56 short musical excerpts validated by Vieillard et al (2008) for research on emotions. These excerpts were composed to express happiness, sadness, fear/threat and peacefulness, 14 stimuli per category, whilst also varying on valence (pleasant vs. unpleasant) and arousal (relaxing vs. stimulating).…”

Section: Methodsmentioning

confidence: 99%

“…Last but not least, since most studies only include happiness as a positive emotion (e.g., Calder et al, 2003;Laukka & Juslin, 2007;Williams et al, 2006), it is impossible to ascertain whether the consistently reported stability for happiness is specific to this emotional quality or rather extends to other positive emotions. Here we examine the recognition of emotional expressions in music over the adult life span (17 -84 years) with a set of stimuli that use structural features as a means to portray two positive emotions, happiness and peacefulness, and two negative ones, sadness and fear/threat (Vieillard, Peretz, Gosselin, Khalfa, Gagnon, & Bouchard, 2008). Therefore, we have an equal Emotion recognition in music and aging !…”

Section: Emotion Recognition In Music Changes Across the Adult Life Spanmentioning

confidence: 99%

“…An unexplored question is whether and how emotion recognition in music changes as a function of aging. In the present study, healthy adults aged between 17 and 84 years (N = 114) judged the magnitude to which a set of musical excerpts (Vieillard et al, 2008) expressed happiness, peacefulness, sadness and fear/threat. The results revealed emotion specific age-related changes: advancing age was associated with a gradual decrease in responsiveness to sad and scary music from middle age onwards, whereas the recognition of happiness and peacefulness, both positive emotional qualities, remained stable from young adulthood to older age.…”

Section: Introductionmentioning

confidence: 99%

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Emotion recognition in music changes across the adult life span

Lima

Castro

2011

Cognition & Emotion

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In comparison with other modalities, the recognition of emotion in music has received little attention. An unexplored question is whether and how emotion recognition in music changes as a function of aging. In the present study, healthy adults aged between 17 and 84 years (N = 114) judged the magnitude to which a set of musical excerpts (Vieillard et al., 2008) expressed happiness, peacefulness, sadness and fear/threat. The results revealed emotion specific age-related changes: advancing age was associated with a gradual decrease in responsiveness to sad and scary music from middle age onwards, whereas the recognition of happiness and peacefulness, both positive emotional qualities, remained stable from young adulthood to older age. Additionally, the number of years of music training was associated with more accurate categorization of the musical emotions examined here. We argue that these findings are consistent with two accounts on how aging might influence the recognition of emotions: motivational changes towards positivity and, to a lesser extent, selective neuropsychological decline.

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Section: Methodsmentioning

confidence: 99%

Section: Emotion Recognition In Music Changes Across the Adult Life Spanmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Emotion recognition in music changes across the adult life span

Lima

Castro

2011

Cognition & Emotion

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“…Moreover, Vieillard et al [43] demonstrated that within the same culture, the emotional responses to music could be highly consistent. All these results indicate that modeling emotion or mood in music is feasible.…”

Section: Introductionmentioning

confidence: 99%

Indexing music by mood: design and integration of an automatic content-based annotator

Laurier

Meyers

Serrà

et al. 2009

Multimed Tools Appl

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In the context of content analysis for indexing and retrieval, a method for creating automatic music mood annotation is presented. The method is based on results from psychological studies and framed into a supervised learning approach using musical features automatically extracted from the raw audio signal. We present here some of the most relevant audio features to solve this problem. A ground truth, used for training, is created using both social network information systems (wisdom of crowds) and individual experts (wisdom of the few). At the experimental level, we evaluate our approach on a database of 1000 songs. Tests of different classification methods, configurations and optimizations have been conducted, showing that Support Vector Machines perform best for the task at hand. Moreover, we evaluate the algorithm robustness against different audio compression schemes. This fact, often neglected, is fundamental to build a system that is usable in real conditions. In addition, the integration of a fast and scalable version of this technique with the European Project PHAROS is discussed. This real world application demonstrates the usability of this tool to annotate large-scale databases. We also report on a user evaluation in the context of the PHAROS search engine, asking people about the utility, interest and innovation of this technology in real world use cases.

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“…Moreover, Vieillard et al [25] demonstrated that within the same culture, the emotional responses to music can be highly consistent. These results indicate that modeling emotion or mood in music is feasible.…”

Section: Introductionmentioning

confidence: 99%

Music Mood Annotator Design and Integration

Laurier

Meyers

Serrà

et al. 2009

2009 Seventh International Workshop on Content-Based Multimedia Indexing

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A robust and efficient technique for automatic music mood annotation is presented. A song's mood is expressed by a supervised machine learning approach based on musical features extracted from the raw audio signal. A ground truth, used for training, is created using both social network information systems and individual experts. Tests of 7 different classification configurations have been performed, showing that Support Vector Machines perform best for the task at hand. Moreover, we evaluate the algorithm robustness to different audio compression schemes. This fact, often neglected, is fundamental to build a system that is usable in real conditions. In addition, the integration of a fast and scalable version of this technique with the European Project PHAROS is discussed.

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Happy, sad, scary and peaceful musical excerpts for research on emotions

Cited by 287 publications

References 47 publications

Emotion recognition in music changes across the adult life span

Emotion recognition in music changes across the adult life span

Indexing music by mood: design and integration of an automatic content-based annotator

Music Mood Annotator Design and Integration

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