arXiv subjects

Chandan Misra

Publications and source records attributed to Chandan Misra.

5 recordsLinked to original sources

arXiv2026

Graph Representation of RaagBase: A Unique Dataset for Hindustani Music

Raag classification is a fundamental MIR task for Hindustani Music, with applications in recommendation, education, archiving, and intelligent search. However, raag clustering remains underexplored, as most existing approaches rely on annotated audio or labeled datasets. While annotated melodic phrases capture characteristic patterns, complete note sequences preserve temporal structure and contextual dependencies, making them more suitable for data-driven modeling. In this work, we introduce RaagBase, a notation-based text dataset consisting of note sequences from compositions by Pt. Bhatkhande. Furthermore we propose a novel graph-based representation of raag structures by modeling the dominance and absence of notes in compositions. Each composition is represented as a node, and the edges between two compositions corresponds the similarities between them based on the note frequency distribution. Further, we apply established graph clustering techniques to identify groups of similar raag compositions. Experimental results demonstrate highly coherent clusters with strong agreement to ground-truth raag labels, thereby validating both the dataset and the proposed representation. The dataset is publicly available at https://anonymous.4open.science/r/RaagBase-5427.

cs.SD↗

arXiv2026

Rag Classification of Tagore Songs using Symbolic Music Notation and Novel Weighted Distance Measures

Rabindra Sangeet, the body of songs written and composed by Rabindranath Tagore, occupies a distinctive position in Indian music by combining poetic expression with melodic ideas drawn from Hindustani rags, Bengali folk traditions, tappa, k{\i}rtan, Baul music, and Western tunes. Although many Tagore songs are associated with rag labels provided by Tagore himself or preserved in authoritative notational traditions, rag identification remains challenging because the songs often reflect creative freedom rather than strict adherence to classical rag grammar. This paper formulates rag identification in Rabindra Sangeet as a supervised classification problem using symbolic music-sheet notations from Swarabitan. Since large-scale annotated audio or music datasets for Rabindra Sangeet are not readily available, this study constructs a rag-labelled symbolic dataset from notated Tagore songs. The work investigates Euclidean distance and cosine similarity for rag classification and introduces a weighted Euclidean distance measure that assigns greater importance to notes belonging to characteristic rag sequences such as arohana and avarohana. Applied within a k-nearest-neighbour framework, the proposed measure improves rag classification by better capturing rag-specific melodic identity.

cs.SD↗

arXiv2023

SANGEET: A XML based Open Dataset for Research in Hindustani Sangeet

It is very important to access a rich music dataset that is useful in a wide variety of applications. Currently, available datasets are mostly focused on storing vocal or instrumental recording data and ignoring the requirement of its visual representation and retrieval. This paper attempts to build an XML-based public dataset, called SANGEET, that stores comprehensive information of Hindustani Sangeet (North Indian Classical Music) compositions written by famous musicologist Pt. Vishnu Narayan Bhatkhande. SANGEET preserves all the required information of any given composition including metadata, structural, notational, rhythmic, and melodic information in a standardized way for easy and efficient storage and extraction of musical information. The dataset is intended to provide the ground truth information for music information research tasks, thereby supporting several data-driven analysis from a machine learning perspective. We present the usefulness of the dataset by demonstrating its application on music information retrieval using XQuery, visualization through Omenad rendering system. Finally, we propose approaches to transform the dataset for performing statistical and machine learning tasks for a better understanding of Hindustani Sangeet. The dataset can be found at https://github.com/cmisra/Sangeet.

cs.SD↗

arXiv2018

Stark: Fast and Scalable Strassen's Matrix Multiplication using Apache Spark

This paper presents a new fast, highly scalable distributed matrix multiplication algorithm on Apache Spark, called Stark, based on Strassen's matrix multiplication algorithm. Stark preserves Strassen's 7 multiplications scheme in a distributed environment and thus achieves faster execution. It is based on two new ideas; it creates a recursion tree of computation where each level of such tree corresponds to division and combination of distributed matrix blocks in the form of Resilient Distributed Datasets(RDDs); It processes each divide and combine step in parallel and memorize the sub-matrices by intelligently tagging matrix blocks in it. To the best of our knowledge, Stark is the first Strassen's implementation in Spark platform. We show experimentally that Stark has a strong scalability with increasing matrix size enabling us to multiply two (16384 x 16384) matrices with 28% and 36% less wall clock time than Marlin and MLLib respectively, state-of-the-art matrix multiplication approaches based on Spark.

cs.DC↗

arXiv2018

SPIN: A Fast and Scalable Matrix Inversion Method in Apache Spark

The growth of big data in domains such as Earth Sciences, Social Networks, Physical Sciences, etc. has lead to an immense need for efficient and scalable linear algebra operations, e.g. Matrix inversion. Existing methods for efficient and distributed matrix inversion using big data platforms rely on LU decomposition based block-recursive algorithms. However, these algorithms are complex and require a lot of side calculations, e.g. matrix multiplication, at various levels of recursion. In this paper, we propose a different scheme based on Strassen's matrix inversion algorithm (mentioned in Strassen's original paper in 1969), which uses far fewer operations at each level of recursion. We implement the proposed algorithm, and through extensive experimentation, show that it is more efficient than the state of the art methods. Furthermore, we provide a detailed theoretical analysis of the proposed algorithm, and derive theoretical running times which match closely with the empirically observed wall clock running times, thus explaining the U-shaped behaviour w.r.t. block-sizes.

cs.DC↗