Had attended a talk the other day by Dr. Jayant Haritsa from the CSA department, on using textual representations of Carnatic music (Music written as Sa Ri Ga Ma etc.,) to determine what is the ‘Aarohana’ and ‘Avarohana’ (the equivalent of scale in Western music) of a given Raaga or identifying the raaga itself, given another piece of music, outside the ones used to train the identification system. Among other aims than the ones given above, was to provide a ‘scientific basis’ for the raagas, based on the statistics of usage of notes in various compositions, and maybe, provide a better Arohana/Avarohana for the raaga itself than the one received from tradition.
The talk was itself quite interesting and the system seems to do pretty well. In the Q&A session, a lot of concern was generated as to whether the ‘better’ Arohana/Avarohana proposed by the system would capture the ‘mood’ of the raaga, which seems to be an essential part of each raaga. Haritsa was of the opinion that as scientific researchers, we must not take things for granted and must try to question tradition using tools of science.
The essential issue, which one can generalize to things further than just music and its analysis, is the question of what is knowledge and/or Truth. More specifically in this context, one can ask the question as to what type of knowledge can we obtain using the scientific method, and whether this is the only kind which is ‘reliable’, the rest being ‘subjective’ is useless in a more general context, i.e, whether Truth in all its glory is best sought out using the scientific method.
Upfront, one must understand the fundamental premise of the scientific method, even leaving out its reductionist inclinations — Nature is not random: it follows some logic, some pattern which by large number of observations and/or experiments is discovered and this knowledge (from observation/experimentation) eventually can be called Truth. This is not hard to justify: we can see patterns everywhere in Nature and can build quite accurate models of the same. The reliability of scientific knowledge depends hugely on the concept of measurement – representing natural phenomena as cardinal numbers – numbers we can use to say something about the size of the measured phenomenon. No observation or experiment can be called a success/failure if it does not produce some kind of number. For example, Haritsa’s system produces a number per candidate scale for a raaga — higher the number, more likely it is the correct scale.
Immediately, one can see phenomena that the scientific method cannot be used to investigate : Emotions, ethics, likes, dislikes, etc., etc., Not only are these immeasurable (neuroscientists may disagree!) quantities, but they are also incommensurable: a statement like makes absolutely no sense. Also, science can give no answers to statements like ‘The world is Maaya’, or ‘What we perceive is not what Is’. These statements belong to the same class of knowledge that the fundamental ‘axiom’ of science belongs to — you cannot prove or disprove them within the logical system that is built upon that axiom.
Now, music is a strange beast. It is highly patterned (scientists like to talk about its ‘mathematical’ structure), but at the same time, its main (probably only) value is in the emotion that it evokes: it is not coincidence that music is an essential part of religious worship, especially of the Bhakti variety. Therefore, no musical education is complete without a good understanding of both the patterns and the emotions (Bhaava) associated with music. Now, scientists are uncomfortable (or dismissive) about things they cannot measure, and musicians are uncomfortable (or dismissive!) of statistical analyses of their art. Therefore, it is not surprising to for each to value one of the two more. Haritsa’s and the audience’s apprehensions merely betrays their respective inclinations.
With the advent of huge computing power, a scientist’s optimism in understanding the universe has understandably increased. It is a common notion that failure of mathematical models is simply due to the ‘exclusion of some variable’ from the model. With more information/data, one can do arbitrarily well. This attitude conveniently ignores the fact that some quantities are not measurable and even if some quantitative representation is possible, they might be incommensurable. This can be seen best in sciences dealing with human tastes and values, like economics, sociology or anthropology. Subjects like econometrics, social psychology seem to be treading a fine line that distinguishes scientific knowledge from gobbledygook. For example, if one surveys 100 students asking them to rate the facilities at the hostel on a scale of 1 to 10, and we conclude that the average score is 8 and so most are satisfied (assume a score greater than 7 implies satisified), we are making two assumptions : we can add the satisfaction of 100 people and divide that number by 100, and that one student’s rating of 7 is the same as another student’s rating of 7. Though there have been arguments justifying such an approach, it is upto the individual to decide how seriously to take such surveys.
The dominant paradigm of our times is that of scientific optimism, and most appeals to emotion or morals are considered ‘woolly’ and ‘unscientific’. But one must realise that unless there is a healthy engagement with both pattern finding and moralising, the Truth can never emerge.