Kaustuv Kanti Ganguli

Classification of Melodic Motifs in Raga Music with Time-series Matching

Abstract Ragas are characterized by their melodic motifs or catch phrases that constitute strong cues to the raga identity for both the performer and the listener, and therefore are of great interest in music retrieval and automatic transcription. While the characteristic phrases, or pakads, appear in written notation as a sequence of notes, musicological rules for interpretation of the phrase in performance in a manner that allows considerable creative expression, while not transgressing raga grammar, are not explicitly defined. In this work, machine learning methods are used on labelled databases of Hindustani and Carnatic vocal audio concerts to obtain phrase classification on manually segmented audio. Dynamic time warping and HMM based classification are applied on time series of detected pitch values used for the melodic representation of a phrase. Retrieval experiments on raga-characteristic phrases show promising results while providing interesting insights on the nature of variation in the surface realization of raga-characteristic motifs within and across concerts.

Discrimination of melodic patterns in indian classical music

The melodic phrases of a raga are an important cue to its identity. Artists, however, incorporate considerable creative variation within a raga phrase during performance while still preserving its identity in the ears of the listeners. It is of interest therefore to explore the boundaries of this categorization of phrase identity, given the space of musical variations in the tonal interval and duration dimensions. Such an endeavor can help better model musical similarity for music retrieval and pedagogy applications. In this work, we carry out melodic shape manipulations on a selected prominent phrase of raga Deshkar to study the subjective responses of musicians in comparison with non-musicians in terms of perceived discrimination of the controlled variations. A method is presented for deriving musically consistent synthetic stimuli for listening. Subjective responses on the discrimination and identification tasks are presented along with a discussion on possible perceptual mechanisms at play.

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.

Aspects of Tempo and Rhythmic Elaboration in Hindustani Music: A Corpus Study

This paper provides insights into aspects of tempo and rhythmic elaboration in Hindustani music, based on a study of a large corpus of recorded performances. Typical tempo developments and stress patterns within a metrical cycle are computed, which we refer to as tempo and rhythm patterns, respectively. Rhythm patterns are obtained by aggregating spectral features over metrical cycles. They reflect percussion patterns that are frequent in the corpus, and enable a discussion of the relation between such patterns and the underlying metrical framework, the taal. Tempo patterns, on the other hand, are computed using reference beat annotations. They document the dynamic development of tempo throughout a metrical cycle, and reveal insights into the flexibility of time in Hindustani music for the first time using quantitative methods on a large set of performances. Focusing on aspects of tempo and rhythm, we demonstrate the value of a computational methodology for the analysis of large music corpora by revealing the range of tempi used in performances, intra-cycle tempo dynamics and percussion accents at different positions of the taal cycle.