Graduate School and Research Center in Digital Sciences

Combining music specific embeddings for computing artist similarity

Lisena, Pasquale; Troncy, Raphaël

ISMIR 2017, 18th International Society for Music Information Retrieval Conference, Late-breaking & demo track, 23-27 October 2017, Suzhou, China

In this paper, we present an original approach and preliminary results for computing similarities between musicrelated entities (artists, works, performances) using embeddings that take into account not only the semantic description of those entities but also their usage in a rich music dataset.

Document Bibtex

Title:Combining music specific embeddings for computing artist similarity
Type:Poster / Demo
Language:English
City:Suzhou
Country:CHINA
Date:
Department:Data Science
Eurecom ref:5361
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Bibtex: @poster / demo{EURECOM+5361, year = {2017}, title = {{C}ombining music specific embeddings for computing artist similarity}, author = {{L}isena, {P}asquale and {T}roncy, {R}apha{\"e}l}, number = {EURECOM+5361}, month = {10}, institution = {Eurecom} address = {{S}uzhou, {CHINA}}, url = {http://www.eurecom.fr/publication/5361} }
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