Discovering rāga motifs by characterizing communities in networks of melodic patterns

Abstract

Rāga motifs are the main building blocks of the melodic structures in Indian art music. Therefore, the discovery and characterization of such motifs is fundamental for the computational analysis of this music. We propose an approach for discovering rāga motifs from audio music collections. First, we extract melodic patterns from a collection of 44 hours of audio comprising 160 recordings belonging to 10 rāgas. Next, we characterize these patterns by performing a network analysis, detecting non-overlapping communities, and exploiting the topological properties of the network to determine a similarity threshold. With that, we select a number of motif candidates that are representative of a rāga, the rāga motifs. For a formal evaluation we perform listening tests with 10 professional musicians. The results indicate that, on an average, the selected melodic phrases correspond to rāga motifs with 85% positive ratings. This opens up the possibilities for many musically-meaningful computational tasks in Indian art music, including human-interpretable rāga recognition, semantic-based music discovery, or pedagogical tools.

Repertoire-Specific Vocal Pitch Data Generation for Improved Melodic Analysis of Carnatic Music

“…Because the pitch extraction is run in an ideal scenario in which the singing voice is predominant and the leakage has been reduced, we can expect to obtain decent quality pitch tracks after post-processing. As a matter of fact, several computational studies of Carnatic Music build on top of pitch tracks obtained using a similar pipeline (excluding the leakage removal in Section 3.1.1) (Gulati et al 2014(Gulati et al , 2016Ganguli et al, 2016), and the pitch tracks in Saraga are also computed as such (Srinivasamurthy et al, 2020). However, given the shortage of ground-truth vocal pitch annotations for Carnatic Music, we are currently unable to evaluate the quality of these objectively.…”

“…Because isolated vocal audio signals for Carnatic Music are scarce, the predominant melody is usually extracted from audio mixtures (Rao et al, 2014;Gulati et al, 2014Gulati et al, , 2016Ganguli et al, 2016;Nuttall et al, 2021). However, predominant pitch extraction is a difficult and not completely solved problem (Bittner et al, 2017).…”

“…Typically, the task of melodic pattern discovery in Carnatic Music has been based on time-series of predominant or vocal pitch extracted from audio recordings (Rao et al, 2014;Gulati et al, 2016;Nuttall et al, 2021). Approaches that depend on symbolic notation are another option, but manual transcription is extremely time consuming, and automated transcription is not a trivial problem in Carnatic music, since svaras are performed with gamakas that can radically shift the sound of the resulting unit from the theoretical pitch position referred to by the svara name.…”

Unveiling the Hierarchical Structure of Music by Multi-Resolution Community Detection

“…More precisely, Serrà et al (2012) investigated the use of community detection algorithms to improve the performance and the interpretability of cover identification methods. Gulati et al (2016) applied community detection on structural segmentations of Indian music to characterise the discovered patterns into rāga, composition-specific and gamaka motifs.…”

Data mining Mandarin tone contour shapes

“…If we denote C to be set of types {c 1 , c 2 , ..., c k }, our goal in this section is to describe the algorithm A that learns a function g : S → C. We adapt an algorithm first proposed by (Gulati et al, 2016), which has been shown to be effective in identifying clusters in time-series data such as pitch contours. It also has several advantages over baseline algorithms such as k-means clustering, including outlier pruning and no need to determine the number of clusters before hand.…”