Emotion and Ambiguity: A Study

Abstract

This chapter explores the utility of non linear source modeling for categorization and classification of evoked emotion from instrumental clips of Hindustani raga and their possible impacts in human brain. Hindustani Music (HM) has been known to convey a variety of emotional responses to the listeners since time immemorial—but neural processing of these emotional attributes is largely unrevealed. The detection of emotional cues from Hindustani Classical music is a demanding task due to the inherent ambiguity present in the different ragas, which makes it difficult to identify any particular emotion from a certain raga. This necessitates the use of a very high resolution mathematical microscope to procure information about the inherent complexities and time series fluctuations that constitute an acoustic and EEG signal. We chose 3 min alaap (opening) portion of six conventional ragas of Hindustani classical music namely, “Darbari Kanada”, “Yaman”, “Mian ki Malhar”, “Durga”, “Jay Jayanti” and “Hamswadhani” played in three different musical instruments (sitar, sarod and flute) by three maestros of HM. The first three ragas correspond to the negative dimension of the Russel’s emotional sphere, while the last three belong to the positive dimension (conventionally). Most of the musical instruments have resonators that are only approximately harmonic in nature, and their operation and harmonic sound spectrum both rely upon the extreme nonlinearity of their driving mechanisms. Such instruments might be described as ‘essentially nonlinear’. Hence, MFDFA (Multifractal Detrended Fluctuation Analysis) technique was utilized to assess the inherent complexity of the musical clips which proves to be an important parameter for classification of emotional attributes in musical clips. Next, EEG experiment was conducted on a pool of participants who were made to listen to these sets of musical clips of 2 min duration each. The brain response corresponding to each emotional clip analyzed with MFDFA technique is expected to elicit emotion specific arousal activities in different lobes of the brain. The multifractal spectral width obtained from alpha/theta frequency ranges of EEG data can be developed as a parameter for the development of an automated emotion recognition system. The study may prove to have far reaching implications in the development of an automated emotion classifier algorithm in future.