Directing Physiology and Mood through Music: Validation of an Affective Music Player

Zwaag, Marjolein D. van der; Janssen, J.H.J.; Westerink, Joyce H. D. M.

doi:10.1109/t-affc.2012.28

Cited by 46 publications

(25 citation statements)

References 46 publications

(101 reference statements)

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“…17,37,43]. In particular, this approach can be valuable in such systems because it is shown to not just influence the feelings that we associate with emotions, but also other adaptive change that associates with emotion, see [7].…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…However, technologies designed to intentionally cause emotion are relatively rare. Recent work includes priming using digital media [17], adaptive music selection [43], and affective mirrors [37]. However, most research has focused on invoking emotion by mimicking social and affective interactions between a user and an interactive system, such as an avatar or robot [36].…”

Section: Causing Emotionmentioning

confidence: 99%

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Emotion and Creativity

Rooij

Corr²,

Jones

2015

Proceedings of the 2015 ACM SIGCHI Conference on Creativity and Cognition

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Creativity thrives when people experience positive emotions. How to design an interactive system that can effectively make use of this potential is, however, still an unanswered question. In this paper, we propose one approach to this problem that relies on hacking into the cognitive appraisal processes that form part of positive emotions. To demonstrate our approach we have conceived, made, and evaluated a novel interactive system that influences an individual's appraisals of their own idea generation processes by providing real-time and believable feedback about the originality of their ideas. The system can be used to manipulate this feedback to make the user's ideas appear more or less original. This has enabled us to test experimentally the hypothesis that providing more positive feedback, rather than neutral, or more negative feedback than the user is expecting, causes more positive emotion, which in turn causes more creativity during idea generation. The findings demonstrate that an interactive system can be designed to use the function of cognitive appraisal processes in positive emotion to help people to get more out of their own creative capabilities.

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Section: Discussion and Future Workmentioning

confidence: 99%

Section: Causing Emotionmentioning

confidence: 99%

Emotion and Creativity

Rooij

Corr²,

Jones

2015

Proceedings of the 2015 ACM SIGCHI Conference on Creativity and Cognition

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“…UNC-ITSPOKE is a speech-enabled intelligent tutoring system that automatically senses and responds to a learner's uncertainty by modeling acoustic-prosodic and lexical features of students' spoken responses [Forbes-Riley and Litman 2011]. Another example is the Affective Music Player, which strategically selects music to induce specific moods (positive, negative, neutral) on a personalized basis via a predictive psychophysiological model [van der Zwaag et al 2013]. In general, systems that both sense and respond to affect are continually emerging as documented in a recent edited volume on affective computing .…”

mentioning

confidence: 99%

A Review and Meta-Analysis of Multimodal Affect Detection Systems

2015

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Affect detection is an important pattern recognition problem that has inspired researchers from several areas. The field is in need of a systematic review due to the recent influx of Multimodal (MM) affect detection systems that differ in several respects and sometimes yield incompatible results. This article provides such a survey via a quantitative review and meta-analysis of 90 peer-reviewed MM systems. The review indicated that the state of the art mainly consists of person-dependent models (62.2% of systems) that fuse audio and visual (55.6%) information to detect acted (52.2%) expressions of basic emotions and simple dimensions of arousal and valence (64.5%) with feature-(38.9%) and decision-level (35.6%) fusion techniques. However, there were also person-independent systems that considered additional modalities to detect nonbasic emotions and complex dimensions using model-level fusion techniques. The meta-analysis revealed that MM systems were consistently (85% of systems) more accurate than their best unimodal counterparts, with an average improvement of 9.83% (median of 6.60%). However, improvements were three times lower when systems were trained on natural (4.59%) versus acted data (12.7%). Importantly, MM accuracy could be accurately predicted (cross-validated R 2 of 0.803) from unimodal accuracies and two system-level factors. Theoretical and applied implications and recommendations are discussed. ACM Reference Format:Sidney K. D'Mello and Jacqueline Kory. 2015. A review and meta-analysis of multimodal affect detection systems.

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“…There is a broad application potential for music players that support this ability to direct mood. Such devices need to include unobtrusive affect measurements and go for music selection accordingly to be able to direct mood to a target state [93]. The user study illustrated in this paper validated the concept of the affective music player and the affective music player takes in a desired mood as input and then selects songs that direct toward that required mood.…”

Section:  Entertainmentmentioning

confidence: 72%

A Survey on Human Emotion Recognition Approaches, Databases and Applications

Vinola¹,

Vimaladevi

2015

ELCVIA

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This paper presents the various emotion classification and recognition systems which implement methods aiming at improving Human Machine Interaction. The modalities and approaches used for affect detection vary and contribute to accuracy and efficacy in detecting emotions of human beings. This paper discovers them in a comparison and descriptive manner. Various applications that use the methodologies in different contexts to address the challenges in real time are discussed. This survey also describes the databases that can be used as standard data sets in the process of emotion identification. Thus an integrated discussion of methods, databases used and applications pertaining to the emerging field of Affective Computing (AC) is done and surveyed. With the advances in the field of emotion recognition, increasing number of investigations have been done and they have found a significant place in different applications involving human computer communication. The primary objective of an emotion recognition system is to interpret the input signals from different modalities and use them to convey information about the interpreted emotion. For example, the doctor may act based on patient's emotions recognized.The nonverbal communication is an important consideration by all these systems. The significant facet of such systems is to study the mechanism behind the non-verbal communication by human beings to be taught in the computer so that the applications can interpret and connect with user's emotions.Previously, publications on surveys have been published in this area where Zeng et al. surveyed the multimodal recognition methods including audio and visual input signals with a focus on spontaneous

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Directing Physiology and Mood through Music: Validation of an Affective Music Player

Cited by 46 publications

References 46 publications

Emotion and Creativity

Emotion and Creativity

A Review and Meta-Analysis of Multimodal Affect Detection Systems

A Survey on Human Emotion Recognition Approaches, Databases and Applications

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