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Decoding soundscape stimuli and their impact on ASMR studie

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EN
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EN
This paper focuses on extracting and understanding the acoustical features embedded in the soundscape used in ASMR (Autonomous Sensory Meridian Response) studies. To this aim, a dataset of the most common sound effects employed in ASMR studies is gathered, containing whispering stimuli but also sound effects such as tapping and scratching. Further, a comparative analytical survey is performed based on various acoustical features and two-dimensional representations in the form of mel spectrogram. A special interest is in whispering sounds uttered in different languages. That is why whispering sounds are compared in the language context, and the characteristics of speaking and whispering are investigated within languages. The results of the 2D analyses are shown in the form of similarity measures, such as Normalized Root Mean Squared Error (NRMSE), PSNR (peak signal-to-noise ratio), and SSIM (structural similarity index measure). The summary is produced, showing that the analytical aspect of the inherently experiential nature of ASMR is highly affected by the subjective, personal experience, so the evidence behind triggering certain brain waves cannot be unambiguous.
Twórcy
  • Gdańsk University of Technology
  • Gdańsk University of Technology
  • Gdańsk University of Technology
Bibliografia
  • [1] E. L. Barratt, C. Spence, and N. J. Davis, “Sensory determinants of the autonomous sensory meridian response (ASMR): Understanding the triggers,” PeerJ, vol. 5, e3846, 2017. https://doi.org/10.7717/peerj.3846.
  • [2] E. L. Barratt, and N. J. Davis, “Autonomous Sensory Meridian Response (ASMR): A flow-like mental state,” PeerJ, vol. 3, e851, 2015. https://doi.org/10.7717/peerj.851.
  • [3] G. L. Poerio, E. Blakey, T. J. Hostler, and T. Veltri, “More than a feeling: Autonomous sensory meridian response (ASMR) is characterized by reliable changes in affect and physiology,” PLOSONE, vol. 13(6), e0196645, 2018. https://doi.org/10.1371/journal.pone.0196645.
  • [4] T. Koumura, M. Nakatani, H.-I. Liao, and H. M. Kondo, “Deep, soft, and dark sounds induce autonomous sensory meridian response,” 2019. https://doi.org/10.1101/2019.12.28.889907.
  • [5] P. P. Zarazaga, G. Eje Henter, and Z. Malisz, “A Processing Framework to Access Large Quantities of Whispered Speech Found in ASMR,” ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1-5, 2023. https://doi.org/10.1109/ICASSP49357.2023.10095965.
  • [6] P. Fallgren, Z. Malisz, and J. Edlund, “How to Annotate 100 Hours in 45 Minutes,” Interspeech 2019, pp. 341-345, 2019. https://doi.org/10.21437/Interspeech.2019-1648.
  • [7] K. Yang, B. Russell, and J. Salamon, “Telling left from right: Learning spatial correspondence of sight and sound,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9932-9941, 2020.
  • [8] Bin Han, “A-SIREN: GAN-synthesized ASMR audio clips,” IEEE Dataport, November 1, 2022. https://doi.org/10.21227/d5zd-6n82.
  • [9] M. Song, Z. Yang, E. Parada-Cabaleiro, X. Jing, Y. Yamamoto, and B. Schuller, “Identifying languages in a novel dataset: ASMR-whispered speech,” Frontiers in Neuroscience, vol. 17, 1120311, 2023. https://doi.org/10.3389/fnins.2023.1120311.
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  • [12] J. Fernandes, F. Teixeira, V. Guedes, A. Junior, J.P. Teixeira, “Harmonic to Noise Ratio Measurement - Selection of Window and Length,” Procedia Computer Science, Volume 138, Pages 280-285, ISSN 1877-0509, 2018. https://doi.org/10.1016/j.procs.2018.10.040
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  • [19] B. McFee, C. Raffel, D. Liang, D.P.W. Ellis, M. McVicar, E. Battenberg, and O. Nieto, “librosa: Audio and music signal analysis in python,” in Proceedings of the 14th Python in Science Conference, pp. 18-25, 2015. https://librosa.org/doc/latest/index.html
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Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-bfdc689b-a221-4b2c-bda5-9cc258580f07
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