Time Capsule Gift with Affective Awareness of Event Memories via Near Field Communication

Chen, Oscal Tzyh-Chiang; Chang, Syu-Yang; Ma, Yu-Zhi; Zhang, Yu Cheng; Lee, Yi Lun

doi:10.1109/sii46433.2020.9026183

Cited by 1 publication

(2 citation statements)

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“…From these studies, there are two works that present proposals for systems for automatic emotion detection (González & McMullen, 2020;Vinayagasundaram et al, 2016). Three other studies use techniques for generating affective or personalized music (Savery et al, 2019;Y.-C. Wu & Chen, 2016;Chen et al, 2020).…”

Section: Musical Composition Recommendation and Customizationmentioning

confidence: 99%

“…The vast majority of works in this group deal with approaches for recommending music or audiovisual content in a personalized way (Deshmukh et al, 2017;Subramaniam et al, 2018;Chang et al, 2017;Chen et al, 2020;Tiple et al, 2016;Seanglidet et al, 2016;Gu et al, 2018;Amali et al, 2018;Gilda et al, 2017;Su et al, 2020). In these works, several aspects are used to identify users' opinions.…”

Section: Musical Composition Recommendation and Customizationmentioning

confidence: 99%

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Affective computing in the context of music therapy: a systematic review

Santana

Lima

Torcate

et al. 2021

RSD

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Music therapy is an effective tool to slow down the progress of dementia since interaction with music may evoke emotions that stimulates brain areas responsible for memory. This therapy is most successful when therapists provide adequate and personalized stimuli for each patient. This personalization is often hard. Thus, Artificial Intelligence (AI) methods may help in this task. This paper brings a systematic review of the literature in the field of affective computing in the context of music therapy. We particularly aim to assess AI methods to perform automatic emotion recognition applied to Human-Machine Musical Interfaces (HMMI). To perform the review, we conducted an automatic search in five of the main scientific databases on the fields of intelligent computing, engineering, and medicine. We search all papers released from 2016 and 2020, whose metadata, title or abstract contains the terms defined in the search string. The systematic review protocol resulted in the inclusion of 144 works from the 290 publications returned from the search. Through this review of the state-of-the-art, it was possible to list the current challenges in the automatic recognition of emotions. It was also possible to realize the potential of automatic emotion recognition to build non-invasive assistive solutions based on human-machine musical interfaces, as well as the artificial intelligence techniques in use in emotion recognition from multimodality data. Thus, machine learning for recognition of emotions from different data sources can be an important approach to optimize the clinical goals to be achieved through music therapy.

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