Academic Background

Soravitt Sangnark graduated with bachelor's and master's degrees in information engineering from Thailand. His master's thesis on music emotion recognition launched his academic career, leading to an opportunity as a research assistant/researcher at Vidyasirimedhi Institute of Science and Technology (VISTEC) in Thailand. At VISTEC, he conducted independent research, building on his master's thesis, to investigate human responses to music with and without lyrics using behavioral measures, 64-channel EEG, and machine learning. The data of this study are publicly available (MUSEC) . Leveraging his background in audio and emotion research, he was also invited to join other projects, such as creating the largest Thai speech emotion corpus, which involved data collection from 200 actors (THAI-SER), conducting other machine learning-related projects, and supervising students.

These research experiences have shaped Soravitt, crystallizing his research curiosity and leading him to become obsessed with the question: what are the sounds that humans define as 'music', from the cognitive perspectives of non-human species? Soravitt then decided to leave his computer science background behind, even though it is now the era of artificial intelligence, and to embark on a new journey in biomusicology to address the question. He developed his PhD proposal by incorporating his everyday experiences, including his roles as a bass player and music organizer, as well as his observations of dogs' and cats' responses to music.

In 2025, Soravitt's proposal was granted a full three-year PhD funding to study music cognition in dogs. He joined the Acoustic Research Institute in the same year to work on his proposal under the supervision of Marisa Hoeschele and collaborators.

Current Research

Soravitt is conducting his own independent PhD research, prioritizing ecological validity and generalizability, to address the following research question: What are the sounds that humans define as 'music', from the cognitive perspectives of dogs?

Personal Website: https://ssoravitt.github.io/

Project

Music in humankind's best friend: Are dogs attuned to our songs?

What are the sounds that humans define as 'music', from the cognitive perspectives of dogs?

PI: Soravitt Sangnark
Duration: Oct 2025 - Sep 2028
Funding: OeAD
Advisor: Marisa Hoeschele

Publications

J. Wongpithayadisai, C. Chaksangchaichot, S. Sangnark, P. Prakrankamanant, K. Gangwanpongpun, S. Boonpunmongkol, P. Milindasuta, D. Na-Pombejra, S. Nutanong, and E. Chuangsuwanich, “THAI Speech Emotion Recognition (THAI-SER) corpus,” arxiv, 2025. [Paper][Dataset]

P. Lakhan, N. Banluesombatkul, N. Sricom, P. Sawangjai, S. Sangnark, T. Yagi, T. Wilaiprasitporn, W. Saengmolee, and T. Limpiti, “EEG-BBnet: A hybrid framework for brain biometric using graph connectivity,” IEEE Sensors Letters, vol. 9, no. 2, pp. 1–4, 2025. [Paper]

P. Autthasan, P. Sukontaman, T. Wilaiprasitporn and S. Sangnark, “HeartRhythm: ECG-Based Music Preference Classification in Popular Music,” 2023 IEEE SENSORS, Vienna, Austria, 2023, pp. 1-4. [Paper][Poster]

B. Leelakittisin, M. Trakulruangroj, S. Sangnark, T. Wilaiprasitporn, and T. Sudhawiyangkul, “Enhanced lightweight CNN using joint classification with averaging probability for sEMG-based subject-independent hand gesture recognition,” IEEE Sensors Journal, pp. 1–1, 2023. [Paper]

R. Assabumrungrat, S. Sangnark, T. Charoenpattarawut, W. Polpakdee, T. Sudhawiyangkul, E. Boonchieng, and T. Wilaiprasitporn, “Ubiquitous Affective Computing: A Review,” in IEEE Sensors Journal, vol. 22, no. 3, pp. 1867-1881, 1 Feb.1, 2022. [Paper]

S. Sangnark, P. Autthasan, P. Ponglertnapakorn, P. Chalekarn, T. Sudhawiyangkul, M. Trakulruangroj, S. Songsermsawad, R. Assabumrungrat, S. Amplod, K. Ounjai, and T. Wilaiprasitporn, “Revealing Preference in Popular Music Through Familiarity and Brain Response,” in IEEE Sensors Journal, vol. 21, no. 13, pp. 14931-14940, 2021. [Paper][Video][Dataset]

S. Sangnark, M. Lertwatechakul, and C. Benjangkaprasert, “Thai music emotion recognition based on Western music,” Journal of Physics: Conference Series, vol. 1195(1), pp. 1-5, 2019. [Paper]

S. Sangnark, M. Lertwatechakul, and C. Benjangkaprasert, “Thai music emotion recognition based on linear regression,” in Proc. of the Int. Conf. on Automation, Control and Robot, Bangkok, Thailand, pp. 62-66, 2018. [Paper]