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

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. •How individual changes in perceptual acoustic features govern the change in nonlinear acoustic complexity.•The individual cognitive attributes of these perceptual features analyzed with EEG response.•This study attempts to bridge the gap between linear and non linear approaches of music signal analysis.•Multifractal spectral width used as a robust parameter to classify audio as well as EEG signals.