Expression, Perception, and Induction of Musical Emotions: A Review and a Questionnaire Study of Everyday Listening

Juslin, Peter; Laukka, Petri

doi:10.1080/0929821042000317813

Cited by 806 publications

(824 citation statements)

References 62 publications

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“…It shows that "happy" songs have higher values for the onset rate, which confirms the psychological results that "happy" music is fast [15]. In Figure 13, we look at the chords change rate, which is higher for "angry" than "not angry".…”

Section: Onset Rate Chords Change Ratesupporting

confidence: 60%

“…From psychological results, one important musical feature when expressing different mood types is rhythm (generally, faster means more arousal) [15]. The basic measure/element of rhythm is the onset, which is defined as an event in the music (any note, drum, etc.).…”

Section: Onset Rate Chords Change Ratementioning

confidence: 99%

“…We focus on the following categories: happy, sad, angry, and relaxed. We decided to use these categories because these moods are related to basic emotions from psychological theories (reviewed in [15]) and they cover the four quadrants of the 2D representation from Russell [34] with valence and arousal dimensions (see Figure 2). …”

Section: Ground Truth From Wisdom Of Crowds and Wisdom Of The Fewmentioning

confidence: 99%

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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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Section: Onset Rate Chords Change Ratesupporting

confidence: 60%

Section: Onset Rate Chords Change Ratementioning

confidence: 99%

Section: Ground Truth From Wisdom Of Crowds and Wisdom Of The Fewmentioning

confidence: 99%

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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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“…Laukka (2007; see also Juslin & Laukka, 2004) obtained ratings of felt emotions to music in everyday-life contexts from a sample of older Swedish listeners (N ϭ 500; age range ϭ 65-75 years). For two reasons, meaningful comparisons could be drawn across both studies.…”

Section: Validity Of Musical and Extramusical Emotion Ratingsmentioning

confidence: 99%

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

Zentner

Grandjean

Scherer

2008

Emotion

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1,013

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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.

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“…A música é um estímulo acústico indutor de grande variedade de respostas emocionais nos ouvintes (Juslin & Laukka, 2004), que pode, portanto, ser utilizada como um estímulo adequado para que estudos sobre a influência das emoções sobre a percepção temporal sejam realizados.…”

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Emoções de uma escuta musical afetam a percepção subjetiva de tempo

Ramos¹,

Bueno²

2012

Psicol. Reflex. Crit.

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ResumoEste estudo verificou se emoções percebidas durante uma escuta musical influenciam a percepção temporal. Músicos e não músicos foram submetidos a tarefas de escuta de trechos musicais do repertório erudito ocidental com 20 segundos de duração cada um e tarefas de associação temporal de cada trecho ouvido a durações padrões, que variavam de 16 a 24 segundos. Os trechos musicais empregados eram representativos de uma dentre as categorias emocionais Alegria, Tristeza, Serenidade ou Medo / Raiva. Uma análise de variância mostrou que, enquanto os não músicos apresentaram subestimações temporais associadas a pelo menos um trecho musical de cada uma das categorias emocionais, os músicos subestimaram todos os trechos musicais tristes, relacionados às características de baixo arousal e valência afetiva negativa. Palavras-chave: Tempo subjetivo, emoções, expertise musical. AbstractThis study examined whether perceived emotions during music listening tasks influence time perception. Musicians and non-musicians were submitted to tasks of listening to musical excerpts from Western classical repertoire of 20 seconds and tasks of temporal association of each piece of music to standard durations, ranging from 16 to 24 seconds. Musical excerpts were representative from one of the following emotional categories: Happiness, Sadness, Threat and Peacefulness. An analysis of variance showed that, while non-musicians showed temporal underestimations associated with, at least, one piece of music from each emotional category, musicians underestimated all sad musical excerpts, related to low arousal and negative valence features.

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Expression, Perception, and Induction of Musical Emotions: A Review and a Questionnaire Study of Everyday Listening

Cited by 806 publications

References 62 publications

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

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

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

Emoções de uma escuta musical afetam a percepção subjetiva de tempo

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