PERCEPTUAL ANCHOR OR ATTRACTOR: HOW DO MUSICIANS PERCEIVE RAGA PHRASES? Kaustuv Kanti Ganguli and Preeti Rao Department of Electrical Engineering Indian Institute of Technology Bombay, Mumbai. {kaustuvkanti,prao}@ee.iitb.ac.in Abstract- A raga performance in Hindustani vocal music builds upon a melodic framework wherein raga-characteristic phrases are presented with creative variations while strongly retaining their identity. It is therefore of interest, for both music information retrieval and pedagogy, to understand better the space of “allowed” variations of the melodic motifs. Our recent study of melodic shapes corresponding to a selected raga phrase showed that variations in the temporal extent of a passing note within a characteristic phrase was perceived categorically by trained musicians. The work is extended here to non-prototypical melodic phrases. Several synthetic but musically valid versions of the phrase are generated from the canonical form and presented to musicians in a pairwise discrimination rating task. Results demonstrated better discrimination performance in the non-prototypical context than in the prototypical context. We interpret this finding to indicate that a category prototype may function as a “perceptual magnet”, effectively decreasing perceptual distance, and thus discriminability, between stimuli. This paper provides a few insights into the nature of musical phrase categories in terms of their raga-belongingness. Keywords- Raga-characteristic phrase, behavioral experiment, perceptual magnet effect. 1. Introduction Musicians are trained to produce and recognize raga phrases. An interesting analogy would be to imagine a phrase as a spoken word in a language that musicians understand. We want to present a musician with many acoustic versions (each slightly modified to a different extent from the “canonical” form, e.g., what might be stored in their long-term memory). We would like to know whether they are sensitive to the differences and measure how the physically measured acoustic signal differences relate to perceived differences. To answer the question how this would be useful for us, we expect music learners to make mistakes akin to the deviations in certain melodic aspects. If we can predict how a good musician responds to such stimuli, we can give proper feedback to the learner (correct/slightly incorrect/very wrong etc.). The question we ask is whether trained Hindustani musicians perform a memory abstraction for the raga characteristic phrases. Our recent work [1] investigated, through acoustic measurements followed by behavioral experiments through listening, the possibility of a canonical form or “prototype” of a raga characteristic phrase. In our context, a prototype may be considered as the phrase that serves to establish the raga around the initial phase of the performance. The case study was conducted for a characteristic phrase DPGRS in raga Deshkar. We first determine all the distinct independent dimensions of actual physical variability by observing actual instances from concerts. We would like to verify whether the existence of a ‘prototype’ only applies to raga- characteristic phrases or it extends to any melodic pattern. The chief objective of the current work is to investigate via perception experiments whether a non-characteristic melodic shape behaves like a prototypical melodic motif. Researchers in the past [2] have used the term melodic ‘predictors’, in the context of music similarity, to refer to high-level quasi-independent musical features (pitch distance, pitch direction, rhythmic salience, melodic contour, and tonal stability). The authors proposed the algorithmic (dis)similarity measure to be a function (multiple linear regression) of these melodic predictors. For our case, stimuli should be generated with the appropriate modifications of the given melodic shape. Thus to obtain a canonical form of a phrase, we need to observe several instances of the phrase to infer the dimensions in which the variations take place and to what extent. This is because we should be able to create artificial stimuli by extrapolating on the obtained trend in the given dimensions. We aim to