Deep Learning based singer identification

Master Universitario en Deep Learning for Audio and Video Signal Processing

Bibliographic Details
Author: Bustos Manzanet, Laura
Format: master thesis
Publication Date:2021
Country:España
Institution:Universidad Autónoma de Madrid
Repository:Biblos-e Archivo. Repositorio Institucional de la UAM
Language:English
OAI Identifier:oai:repositorio.uam.es:10486/700206
Online Access:http://hdl.handle.net/10486/700206
Access Level:Open access
Keyword:SingerID
Deep Learning
wav2vec
Telecomunicaciones
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spelling Deep Learning based singer identificationBustos Manzanet, LauraSingerIDDeep Learningwav2vecTelecomunicacionesMaster Universitario en Deep Learning for Audio and Video Signal ProcessingIt is known that speaker identification is a field with a lot of related research carried out but,when it comes to looking for research developed from singingvoiceinstead of speech,only a few studiescan be found. This difference in the amount of work related to both fields is mainly due to the fact that the spoken voice is simpler and contains a much narrower frequency spectrum than the singingvoice. In this way, this Master's Final Project containsa study to identify singers from their recorded songs. For thispurpose, a more sophisticated system has been developed to facethe increased complexity in the data, being able to discriminateamongsingers.As a previous step to identify the singer, and due to the scarcity of databases of singing voice in the state of the art, the present work also includes the development of an automatic way for creating anovel databaseusing Spotify’s API. The database contains information related to the musical genre,the artist and differentmusical characteristics of the 30 seconds excerpt pre-view song provided by Spotify. The files of the songs have been source separated with the network of the Spleeter application to carry out a source separation and thus be able to work with the processedfile that only contains the singingvoice of the original songs.The developed system has used different feature extractors from the current state of the art using both speech analysis techniques and techniques that are used whenmusical instruments are wanted to be identified in recordings. With these obtained features, some current state of the art classifiers have been fed based on shallow neural networks and speaker identification networks.Martín Gutiérrez, David (Tutor)Ramos Castro, DanielDepartamento de Tecnología Electrónica y de las ComunicacionesEscuela Politécnica Superior20212021-09-01master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10486/700206reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/7002062026-06-23T12:46:27Z
dc.title.none.fl_str_mv Deep Learning based singer identification
title Deep Learning based singer identification
spellingShingle Deep Learning based singer identification
Bustos Manzanet, Laura
SingerID
Deep Learning
wav2vec
Telecomunicaciones
title_short Deep Learning based singer identification
title_full Deep Learning based singer identification
title_fullStr Deep Learning based singer identification
title_full_unstemmed Deep Learning based singer identification
title_sort Deep Learning based singer identification
dc.creator.none.fl_str_mv Bustos Manzanet, Laura
author Bustos Manzanet, Laura
author_facet Bustos Manzanet, Laura
author_role author
dc.contributor.none.fl_str_mv Martín Gutiérrez, David (Tutor)
Ramos Castro, Daniel
Departamento de Tecnología Electrónica y de las Comunicaciones
Escuela Politécnica Superior
dc.subject.none.fl_str_mv SingerID
Deep Learning
wav2vec
Telecomunicaciones
topic SingerID
Deep Learning
wav2vec
Telecomunicaciones
description Master Universitario en Deep Learning for Audio and Video Signal Processing
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-09-01
dc.type.none.fl_str_mv master thesis
http://purl.org/coar/resource_type/c_bdcc
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/700206
url http://hdl.handle.net/10486/700206
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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