EmoSynth Real Time Emotion-Driven Sound Texture Synthesis via Brain-Computer Interface-Reference-Cited by-同舟云学术

EmoSynth Real Time Emotion-Driven Sound Texture Synthesis via Brain-Computer Interface

Published:2024-06-27 Issue: Volume: Page:616-621
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Container-title:Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization
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Author:

Colafiglio Tommaso¹^ORCID,Lofù Domenico²^ORCID,Sorino Paolo³^ORCID,Lombardi Angela⁴^ORCID,Narducci Fedelucio²^ORCID,Festa Fabrizio⁵^ORCID,Di Noia Tommaso⁶^ORCID

Affiliation:

1. DEI, Politecnico di Bari, Italy

2. Politecnico di Bari, Italy

3. Electrical and Information Engineering, Politecnico di Bari, Italy

4. Dipartimento di Ingegneria Elettrica e dell'Informazione, Politecnico di Bari, Italy

5. Conservatorio E.R. Duni, Italy

6. Department of Electrical and Information Engineering, Politecnico di Bari, Italy

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3631700.3665196

Reference22 articles.

1. Brain Computer Interface: Deep Learning Approach to Predict Human Emotion Recognition

2. N Barascud. [n. d.]. meegkit: EEG and Meg denoising in Python.

3. Water quality prediction on a Sigfox-compliant IoT device: The road ahead of WaterS

4. Tommaso Colafiglio, Domenico Lofù, Paolo Sorino, Fabrizio Festa, Tommaso Di Noia, and Eugenio Di Sciascio. 2023. Exploring the Mental State Intersection by Brain-Computer Interfaces, Cellular Automata and Biofeedback. In IEEE EUROCON 2023-20th International Conference on Smart Technologies. IEEE, 461–466.

5. Tommaso Colafiglio, Paolo Sorino, Domenico Lofu, Angela Lombardi, Fedelucio Narducci, and Tommaso Di Noia. 2023. Combining Mental States Recognition and Machine Learning for Neurorehabilitation. In 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 3848–3853.