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A novel study on perception–cognition scenario in music using deterministic and non-deterministic approach

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  • Banerjee, Archi
  • Sanyal, Shankha
  • Roy, Souparno
  • Nag, Sayan
  • Sengupta, Ranjan
  • Ghosh, Dipak

Abstract

In the last few decades, nonlinear science and chaos theory has provided several robust non-deterministic tools by means of which the complexity of a nonlinear audio waveform can be measured precisely. On the other hand, sound signal analysis in linear deterministic approach has reached a new dimension where a number of well equipped software have been developed which can minutely measure and control the basic parameters of sound like pitch, intensity, tempo etc. The main objective of the present work is to quantitatively study the changes in acoustic signal complexity (measured using chaos based fractal technique) with individual variation in pitch, loudness and timbre of a sound signal. EEG (Electroencephalography) was also performed on 10 participants to see how the neuro-cognitive attributes of a sound change, i.e. when these basic components — pitch, loudness and timbre of the sound vary, one at a time. Single strokes of a piano were recorded where pitch and loudness of the sound signals were varied one at a time keeping the other parameters fixed. Then the sounds of 14 different musical instruments playing the same pitch at same loudness were recorded, which effectively served the purpose of timbre variation. EEG experiment was conducted with these audio signals as stimuli for the participants. The multifractal spectral widths were calculated for all the music signals as well as the corresponding EEG signals using Multifractal Detrended Fluctuation Analysis (MFDFA) and compared with each other. The results point towards the direction of a correlation between the conventional linear parameters and the latest nonlinear features in the acoustic domain, while the changes in the multifractal values of the different EEG waves reveal new information about the cognition of the basic features of sound in human brain. This study is a novel attempt to provide new data in engulfing apparent objective (acoustics) - subjective (EEG) connection, which is highly needed for building any model for perception–cognition connectivity.

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  • Banerjee, Archi & Sanyal, Shankha & Roy, Souparno & Nag, Sayan & Sengupta, Ranjan & Ghosh, Dipak, 2021. "A novel study on perception–cognition scenario in music using deterministic and non-deterministic approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    • Handle: RePEc:eee:phsmap:v:567:y:2021:i:c:s0378437120309808
    DOI: 10.1016/j.physa.2020.125682
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    References listed on IDEAS

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    1. Ghosh, Dipak & Dutta, Srimonti & Chakraborty, Sayantan, 2014. "Multifractal detrended cross-correlation analysis for epileptic patient in seizure and seizure free status," Chaos, Solitons & Fractals, Elsevier, vol. 67(C), pages 1-10.
    2. Sanyal, Shankha & Banerjee, Archi & Patranabis, Anirban & Banerjee, Kaushik & Sengupta, Ranjan & Ghosh, Dipak, 2016. "A study on Improvisation in a Musical performance using Multifractal Detrended Cross Correlation Analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 67-83.
    3. Bhaduri, Susmita & Bhaduri, Anirban & Ghosh, Dipak, 2020. "Acoustical genesis of uniqueness of tanpura-drone signal—Probing with non-statistical fluctuation pattern," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    4. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
    5. Su, Zhi-Yuan & Wu, Tzuyin, 2007. "Music walk, fractal geometry in music," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 418-428.
    6. Dutta, Srimonti & Ghosh, Dipak & Samanta, Shukla & Dey, Santanu, 2014. "Multifractal parameters as an indication of different physiological and pathological states of the human brain," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 155-163.
    7. Kantelhardt, Jan W & Koscielny-Bunde, Eva & Rego, Henio H.A & Havlin, Shlomo & Bunde, Armin, 2001. "Detecting long-range correlations with detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 295(3), pages 441-454.
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    Cited by:

    1. Gündüz, Güngör, 2023. "Entropy, energy, and instability in music," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    2. Nag, Sayan & Basu, Medha & Sanyal, Shankha & Banerjee, Archi & Ghosh, Dipak, 2022. "On the application of deep learning and multifractal techniques to classify emotions and instruments using Indian Classical Music," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).

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