Parag Chordia

754 citations
22 papers · 480 indexed · h-index 11
Topics
Music and Audio Processing (19 papers)Music Technology and Sound Studies (15 papers)Neuroscience and Music Perception (10 papers)

In The Last Decade

Parag Chordia

22 papers receiving 428 citations

Peers

Parag Chordia
Comparison fields: 5 of 64
  • Signal Processing 302
  • Cognitive Neuroscience 256
  • Computer Vision and Pattern Recognition 241
  • Social Psychology 47
  • Music 42
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Anja Volk Netherlands
Larry Polansky United States
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Citations per field
00.5×1.7×
Anja Volk · 1×
Citations per year

Countries citing papers authored by Parag Chordia

Since Specialization
Citations

This map shows the geographic impact of Parag Chordia's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Parag Chordia with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Parag Chordia more than expected).

Fields of papers citing papers by Parag Chordia

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Parag Chordia. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Parag Chordia. The network helps show where Parag Chordia may publish in the future.

Co-authorship network of co-authors of Parag Chordia

This figure shows the co-authorship network connecting the top 25 collaborators of Parag Chordia. A scholar is included among the top collaborators of Parag Chordia based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Parag Chordia. Parag Chordia is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 104
2 26
3
Multiple Viewpoint Modeling of North Indian Classical Vocal Compositions
2
4 21
5
MUSIC TAGGING WITH REGULARIZED LOGISTIC REGRESSION
6
6 9
7 21
8
Tabla Gyan: An Artificial Tabla Improviser.
8
9 14
10 9
11 5
12 11
13
TABLA GYAN: A SYSTEM FOR REALTIME TABLA RECOGNITION AND RESYNTHESIS
6
14 14
15 6
16 76
17
Automatic Raag Classification of Pitch-tracked Performances Using Pitch-class and Pitch-class Dyad Distributions
18
18 26
19
Automatic transcription of tabla music
2
20
Automatic rag classification using spectrally derived tone profiles
8

About Parag Chordia

Parag Chordia is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Cognitive Neuroscience, having authored 22 papers that have together received 480 indexed citations. Recurring topics across this work include Music and Audio Processing (19 papers), Music Technology and Sound Studies (15 papers) and Neuroscience and Music Perception (10 papers). The work is most often cited by research in Signal Processing (302 citations), Music (42 citations) and Cognitive Neuroscience (256 citations). Parag Chordia has collaborated with scholars based in United States, Canada and Australia. Frequent co-authors include Daniel J. Levitin, Vinod Menon, Daniel A. Abrams, Amirah Khouzam, Tianwen Chen, Srikanth Ryali, Ajay Srinivasamurthy, Elliot Moore, Wei Bian and Dacheng Tao. Their work appears in journals such as Proceedings of the National Academy of Sciences, European Journal of Neuroscience and Computer Music Journal.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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