From Speech to Recital - A case of phase transition? A non-linear study

A question has been whirling in the musical fraternity for generations. Which comes first? Melody or lyrics? The answer to which is still unknown. It is well established that the lyrics play a very important role in portraying the emotional content of a particular recitation or song. The audience intuitively listens to the lyrics of the song or recitation and tends to get an idea of the mood of the song or recitation. But what would happen if the lyric part is separated from the melody and conveyed to the audience as a separate entity altogether. Would the emotional content of the recitation remain the same or the meaning would change altogether? A recitation is a way of combining the words together so that they have a sense of rhythm and thus an emotional content is imbibed within. In this study we envisaged to answer these questions in a scientific manner taking into consideration five well known Bengali recitations of different poets conveying a variety of moods ranging from joy to sorrow. The clips were recited as well as read by the same person to avoid any perceptual difference arising out of timbre variation. Next, the emotional content from the 5 recitations were standardized with the help of listening test conducted on a pool of 50 participants. The recitations as well as the speech were analyzed with the help of a latest non linear technique called Detrended Fluctuation Analysis that gives a scaling exponent, which is essentially the measure of long range correlations present in the signal. Similar pieces were extracted from the complete signal and analyzed with the help of DFA technique. Our analysis shows that the scaling exponent for all parts of recitation were much higher in general as compared to their counterparts in speech.

[1]  H. Stanley,et al.  Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. , 1995, Chaos.

[2]  R. Voss,et al.  Evolution of long-range fractal correlations and 1/f noise in DNA base sequences. , 1992, Physical review letters.

[3]  R. Acharya U,et al.  Nonlinear analysis of EEG signals at different mental states , 2004, Biomedical engineering online.

[4]  Ranjan Sengupta,et al.  Study on Brain Dynamics by Non Linear Analysis of Music Induced EEG Signals , 2016 .

[5]  J. Panksepp,et al.  Emotional sounds and the brain: the neuro-affective foundations of musical appreciation , 2002, Behavioural Processes.

[6]  K. Hsü,et al.  Fractal geometry of music. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

[7]  S. Havlin,et al.  Detecting long-range correlations with detrended fluctuation analysis , 2001, cond-mat/0102214.

[8]  A. Eke,et al.  Fractal characterization of complexity in temporal physiological signals , 2002, Physiological measurement.

[9]  H. Stanley,et al.  Effect of trends on detrended fluctuation analysis. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  S. K. Mullick,et al.  NONLINEAR DYNAMICAL ANALYSIS OF SPEECH , 1996 .

[11]  Rodrigo Quian Quiroga,et al.  Nonlinear multivariate analysis of neurophysiological signals , 2005, Progress in Neurobiology.

[12]  Ranjan Sengupta,et al.  Gestalt Phenomenon in Music: Which Frequencies Do We Really Hear? , 2018 .

[13]  Ahmed H. Tewfik,et al.  Rhythm and periodicity detection in polyphonic music , 1999, 1999 IEEE Third Workshop on Multimedia Signal Processing (Cat. No.99TH8451).