Leveraging Melodic Context for Improved Svara Representation

For the South Indian musical tradition known as Carnatic music, embeddings of svara (note) pitch time series have proven useful for tasks such as svara classification and performance analysis. In this paper, we extend an existing embedding method by incorporating findings from musicological research on the relationship between the performance of a svara and its immediate melodic context, in order to improve the learning of these embedding models. We present a context-aware GRU-based model, adapting the existing DeepGRU architecture to encode both svara and its surrounding melodic context, before combining them via a co-attention mechanism prior to classification. For a ground truth dataset of 2,077 expert svara annotations across two performances in raga Bhairavi, we observe that the inclusion of melodic context leads to a 6.6% absolute increase in F1 score for svara label classification (from 78.3% to 84.9%), and an 7.8% absolute increase (from 59.9% to 67.7%) for classification of svara-form: sub-svara clusters that capture gamaka (ornamentation) variations in the performed svara.