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Melodic shape stylization for robust and efficient motif detection in Hindustani vocal music

In Hindustani classical music, melodic phrases are identified not only by the stable notes at precise pitch intervals but also by the shapes of the continuous transient pitch segments connecting these. Time-series matching via subsequence dynamic time warping (DTW) facilitates the equal contribution of stable notes and transients to the computation of similarity between pitch contour segments corresponding to melodic phrases. In the interest of reducing computational complexity it is advantageous to replace time-series DTW with low-dimensional string matching provided a principled approach to the time-series to symbolic string conversion is available. While the stable notes easily lend themselves to quantization, we address the compact representation of the transient pitch segments in this work. We analyze the design considerations at each stage: pitch curve fitting, normalization (with respect to pitch interval and duration), shape dictionary generation, inter-symbol proximity measure and string matching cost functions. A combination of domain knowledge- and data-driven optimization on a database of raga music is exploited to design the melodic representation of a raga phrase that enables a performance comparable to the time series based matching in an audio search by query task at significantly lower computational cost.