Interactive Sonification Strategies for the Motion and Emotion of Dance Performances

Landry, Steven J.

doi:10.37099/mtu.dc.etdr/896

Cited by 3 publications

(3 citation statements)

References 32 publications

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“…However, the first mock exam is usually not satisfactory. e traditional single-mode research based on can only express some characteristics of the object, just as people observe the world through only one sense, which has considerable limitations [14]. In contrast, multimodal information has richer semantic information, and the information of each mode can complement each other.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Music Emotion Research Based on Reinforcement Learning and Multimodal Information

2022

Journal of Mathematics

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Music is an important carrier of emotion and an indispensable factor in people’s daily life. With the rapid growth of digital music, people’s demand for music emotion analysis and retrieval is also increasing. With the rapid development of Internet technology, digital music has been derived continuously, and automatic recognition of music emotion has become the main research focus. For music, emotion is the most essential feature and the deepest inner feeling. Under the ubiquitous information environment, revealing the deep semantic information of multimodal information resources and providing users with integrated information services has important research and application value. In this paper, a multimodal fusion algorithm for music emotion analysis is proposed, and a dynamic model based on reinforcement learning is constructed to improve the analysis accuracy. The model dynamically adjusts the emotional analysis results by learning the user’s behavior, so as to realize the personalized customization of the user’s emotional preference.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Music Emotion Research Based on Reinforcement Learning and Multimodal Information

2022

Journal of Mathematics

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“…In the future it can support multimedia as the output. Dancers can, for instance, map movements to particular sound efects and create models that can generate music based on dancing movement [11]. Another direction is using Echofuid as a procedural choreographic generation tool.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Echofluid: An Interface for Remote Choreography Learning and Co-creation Using Machine Learning Techniques

Wang

Duer

Hardwig

et al. 2022

Adjunct Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology

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Born from physical activities, dance carries beyond mere body movement. Choreographers interact with audiences' perceptions through the kinaesthetics, creativity, and expressivity of wholebody performance, inviting them to construct experience, emotion, culture, and meaning together. Computational choreography support can bring endless possibilities into this one of the most experiential and creative artistic forms. While various interactive and motion technologies have been developed and adopted to support creative choreographic processes, little work has been done in exploring incorporating machine learning in a choreographic system, and few remote dance teaching systems in particular have been suggested. In this exploratory work, we proposed Echofuid-a novel AI-based choreographic learning and support system that allows student dancers to compose their own AI models for learning, evaluation, exploration, and creation. In this poster, we present the design, development and ongoing validation process of Echofuid, and discuss the possibilities of applying machine learning in collaborative art and dance as well as the opportunities of augmenting interactive experiences between the performers and audiences with emerging technologies.

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“…Different kind of data, thus also non-audio data, can be converted into an audio representation and radiated in form of audible sound to convey information. For example, the sonification of human movement pattern is a common practice in contemporary performing arts [Landry and Jeon, 2020], but can be also utilised as a feedback technique in physiotherapeutic treatment [Guerra et al, 2020]. Besides, data sonification can be a useful technique for applications in favour of nature and environment as well.…”

Section: Sonificationmentioning

confidence: 99%

Climate Change & Computer Audition: A Call to Action and Overview on Audio Intelligence to Help Save the Planet

Schuller¹,

Akman²,

Chen³

et al. 2022

Preprint

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Among the seventeen Sustainable Development Goals (SDGs) proposed within the 2030 Agenda and adopted by all the United Nations member states, the 13 th SDG is a call for action to combat climate change for a better world. In this work, we provide an overview of areas in which audio intelligence -a powerful but in this context so far hardly considered technology -can contribute to overcome climate-related challenges. We categorise potential computer audition applications according to the five elements of earth, water, air, fire, and aether, proposed by the ancient Greeks in their five element theory; this categorisation serves as a framework to discuss computer audition in relation to different ecological aspects. Earth and water are concerned with the early detection of environmental changes and, thus, with the protection of humans and animals, as well as the monitoring of land and aquatic organisms. Aerial audio is used to monitor and obtain information about bird and insect populations. Furthermore, acoustic measures can deliver relevant information for the monitoring and forecasting of weather and other meteorological phenomena. The fourth considered element is fire. Due to the burning of fossil fuels, the resulting increase in CO 2 emissions and the associated rise in temperature, fire is used as a symbol for man-made climate change and in this context includes the monitoring of noise pollution, machines, as well as the early detection of wildfires. In all these areas, computer audition can help counteract climate change. Aether then corresponds to the technology itself that makes this possible. This work explores these areas and discusses potential applications, while positioning computer audition in relation to methodological alternatives.

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Interactive Sonification Strategies for the Motion and Emotion of Dance Performances

Cited by 3 publications

References 32 publications

[Retracted] Music Emotion Research Based on Reinforcement Learning and Multimodal Information

[Retracted] Music Emotion Research Based on Reinforcement Learning and Multimodal Information

Echofluid: An Interface for Remote Choreography Learning and Co-creation Using Machine Learning Techniques

Climate Change & Computer Audition: A Call to Action and Overview on Audio Intelligence to Help Save the Planet

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