GrooveMeter: Enabling Music Engagement-aware Apps by Detecting Reactions to Daily Music Listening via Earable Sensing

Author:

Lee Euihyeok1ORCID,Min Chulhong2ORCID,Lee Jaeseung1ORCID,Yu Jin1ORCID,Kang Seungwoo1ORCID

Affiliation:

1. Korea University of Technology and Education, Cheonan, Republic of Korea

2. Nokia Bell Labs, Cambridge, United Kingdom

Publisher

ACM

Reference45 articles.

1. [n.d.]. Music Listening 2022. https://www.ifpi.org/wp-content/uploads/2022/11/ Engaging-with-Music-2022_full-report-1.pdf . Accessed : Apr. 22, 2023 . [n.d.]. Music Listening 2022. https://www.ifpi.org/wp-content/uploads/2022/11/ Engaging-with-Music-2022_full-report-1.pdf. Accessed: Apr. 22, 2023.

2. [n.d.]. TensorFlow Lite . https://www.tensorflow.org/lite. Accessed : Apr. 22, 2023 . [n.d.]. TensorFlow Lite. https://www.tensorflow.org/lite. Accessed: Apr. 22, 2023.

3. Yudhik Agrawal , Samyak Jain , Emily Carlson , Petri Toiviainen , and Vinoo Alluri . 2020. Towards Multimodal MIR: Predicting individual differences from music-induced movement. arXiv preprint arXiv:2007.10695 ( 2020 ). Yudhik Agrawal, Samyak Jain, Emily Carlson, Petri Toiviainen, and Vinoo Alluri. 2020. Towards Multimodal MIR: Predicting individual differences from music-induced movement. arXiv preprint arXiv:2007.10695 (2020).

4. Your reactions suggest you liked the movie

5. EarBit

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