Ajay Srinivasamurthy

In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music

Abstract The aim of this paper is to identify and discuss various methods in computational rhythm description of Carnatic and Hindustani music of India, and Makam music of Turkey. We define and describe three relevant rhythm annotation tasks for these cultures—beat tracking, meter estimation, and downbeat detection. We then evaluate several methodologies from the state of the art in Music Information Retrieval (MIR) for these tasks, using manually annotated datasets of Turkish and Indian music. This evaluation provides insights into the nature of rhythm in these cultures and the challenges to automatic rhythm analysis. Our results indicate that the performance of evaluated approaches is not adequate for the presented tasks, and that methods that are suitable to tackle the culture specific challenges in computational analysis of rhythm need to be developed. The results from the different analysis methods enable us to identify promising directions for an appropriate exploration of rhythm analysis in Turkish, Carnatic and Hindustani music.

A Study of Instrument-wise Onset Detection in Beijing Opera Percussion Ensembles

Note onset detection and instrument recognition are two of the most investigated tasks in Music Information Retrieval (MIR). Various detection methods have been proposed in previous research for western music, with less focus on other music cultures of the world. In this paper, we focus on onset detection for percussion instruments in Beijing Opera, a major genre of Chinese traditional music. A dataset of individual audio samples of four primary percussion instruments is used to obtain the spectral bases for each instrument. With these bases, we separate the input percussion ensemble recordings into its spectral sources and their activations using a Non-negative Matrix Factorization (NMF) based algorithm. A simple onset detection conducted on each NMF activation presents satisfactory overall detection rates, and provides us valuable implications and suggestions for future development of drum transcription and percussion pattern analysis in Beijing Opera.

A supervised approach to hierarchical metrical cycle tracking from audio music recordings

A supervised approach to metrical cycle tracking from audio is presented, with a main focus on tracking the tala, the hierarchical cyclic metrical structure in Carnatic music. Given the tala of a piece, we aim to estimate the aksara (lowest metrical pulse), the aksara period, and the sama (first pulse of the tala cycle). Starting with percussion enhanced audio, we estimate the aksara pulse period from a tempogram computed using an onset detection function. A novelty function is computed using a self similarity matrix constructed using frame level audio features. These are then used to estimate possible aksara and sama candidates, followed by a candidate selection based on periodicity constraints, which leads to the final estimates. The algorithm is tested on an annotated collection of 176 pieces spanning four different talas. Though applied to Carnatic music, the framework presented is general and can be extended to other music cultures with cyclical metrical structures.

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.

A generalized Bayesian model for tracking long metrical cycles in acoustic music signals

Most musical phenomena involve repetitive structures that enable listeners to track meter, i.e. the tactus or beat, the longer over-arching measure or bar, and possibly other related layers. Meters with long measure duration, sometimes lasting more than a minute, occur in many music cultures, e.g. from India, Turkey, and Korea. However, current meter tracking algorithms, which were devised for cycles of a few seconds length, cannot process such structures accurately. We present a novel generalization to an existing Bayesian model for meter tracking that overcomes this limitation. The proposed model is evaluated on a set of Indian Hindustani music recordings, and we document significant performance increase over the previous models. The presented model opens the way for computational analysis of performances with long metrical cycles, and has important applications in music studies as well as in commercial applications that involve such musics.

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.