S
Sankalp Gulati
Researcher at Pompeu Fabra University
Publications - 28
Citations - 962
Sankalp Gulati is an academic researcher from Pompeu Fabra University. The author has contributed to research in topics: Music information retrieval & Melody. The author has an hindex of 15, co-authored 28 publications receiving 840 citations. Previous affiliations of Sankalp Gulati include Indian Institute of Technology Bombay.
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Proceedings ArticleDOI
Essentia: An Audio Analysis Library for Music Information Retrieval.
Dmitry Bogdanov,Nicolas Wack,Emilia Gómez,Sankalp Gulati,Perfecto Herrera,Oscar Mayor,Gerard Roma,Justin Salamon,Jose R. Zapata,Xavier Serra +9 more
TL;DR: Comunicacio presentada a la 14th International Society for Music Information Retrieval Conference, celebrada a Curitiba (Brasil) els dies 4 a 8 de novembre de 2013.
Proceedings ArticleDOI
ESSENTIA: an open-source library for sound and music analysis
Dmitry Bogdanov,Nicolas Wack,Emilia Gómez,Sankalp Gulati,Perfecto Herrera,Oscar Mayor,Gerard Roma,Justin Salamon,Jose R. Zapata,Xavier Serra +9 more
TL;DR: Essentia 2.0, an open-source C++ library for audio analysis and audio-based music information retrieval, is presented, which contains an extensive collection of reusable algorithms which implement audio input/output functionality, standard digital signal processing blocks, statistical characterization of data, and a large set of spectral, temporal, tonal and high-level music descriptors.
Journal ArticleDOI
Rāga Recognition based on Pitch Distribution Methods
TL;DR: This work brings together and discusses the previous computational approaches to rāga recognition correlating them with human techniques, in both Karṇāṭaka and Hindustānī music traditions.
Proceedings ArticleDOI
Phrase-based rĀga recognition using vector space modeling
TL;DR: This work proposes a raga recognition approach based on melodic phrases that can be used as a dictionary of semantically-meaningful melodic units for several computational tasks in Indian art music.
Journal ArticleDOI
Automatic Tonic Identification in Indian Art Music: Approaches and Evaluation
Sankalp Gulati,Ashwin Bellur,Justin Salamon,H. G. Ranjani,Vignesh Ishwar,Hema A. Murthy,Xavier Serra +6 more
TL;DR: It is shown that the approaches that combine multi-pitch analysis with machine learning provide the best performance in most cases (90% identification accuracy on average), and are robust across the aforementioned contexts compared to the approaches based on expert knowledge.