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

Abstract

This article 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 tāl. 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 tāl cycle.

An Interdisciplinary Review of Music Performance Analysis

“…Studies analyzing musical timing are not only limited to Western music. Srinivasamurthy et al (2017) performed a large-scale computational analysis of rhythm in Hindustani classical percussion, confirming and quantifying tendencies pertaining to timing such as deviations of tempo within a metric cycle (also referred to as tal). The study demonstrated the value of using MIR techniques for rhythm analysis of large corpora of music.…”

“…A large body of work focuses on an exploratory approach to analyzing performance recordings and describing performance characteristics. Such studies typically extract characteristics such as the tempo curve or histogram (Repp, 1990;Palmer, 1989;Povel, 1977;Srinivasamurthy et al, 2017) or loudness curve (Repp, 1998a;Seashore, 1938) from the audio and aim at either gaining general knowledge on performances or comparing attributes between different performances/performers based on trends observed in the extracted data. Additionally, there are also studies focusing on discovery of general patterns in performance parameters, which can be useful in identifying trends such as changes over eras (Ornoy and Cohen, 2018).…”

Saraga: Open Datasets for Research on Indian Art Music

“…High-quality manual annotations serve as ground truth for supervised automatic analysis tasks and enable us to apply machine learning techniques. One example is our previous work (Srinivasamurthy et al, 2017), focusing on meter analysis of IAM. For such studies, large sets of annotated data are a basic requirement.…”

“…The sama annotations are particularly useful for studies focusing on cycle level rhythm and meter analysis, as done by Srinivasamurthy et al (2016Srinivasamurthy et al ( , 2017. They can be used as a pre-processing step for further structural and melodic analysis of the music piece.…”

Learning, Probability and Logic: Toward a Unified Approach for Content-Based Music Information Retrieval

“…• Expand musical horizons by conducting research on various types of music. Progress toward this goal have already been made by exploring non-Western music traditions such as Indian (Srinivasamurthy et al, 2017) or Chinese (Repetto and Serrá, 2014) music, but other facets of music, such as contemporary classical music remain unexplored, and, as stated above, existing computational models cannot be applied to them. Taking advantage of relational structure learning algorithms, that would allow finding explainable representation from the data, would be of particular interest.…”