BEHM-GAN: Bandwidth Extension of Historical Music using Generative Adversarial Networks
Audio bandwidth extension aims to expand the spectrum of narrow-band audio signals. Although this topic has been broadly studied during recent years, the particular problem of extending the bandwidth of historical music recordings remains an open challenge. This paper proposes BEHM-GAN, a model base...
Gespeichert in:
Veröffentlicht in: | arXiv.org 2022-06 |
---|---|
Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | |
container_title | arXiv.org |
container_volume | |
creator | Moliner, Eloi Välimäki, Vesa |
description | Audio bandwidth extension aims to expand the spectrum of narrow-band audio signals. Although this topic has been broadly studied during recent years, the particular problem of extending the bandwidth of historical music recordings remains an open challenge. This paper proposes BEHM-GAN, a model based on generative adversarial networks, as a practical solution to this problem. The proposed method works with the complex spectrogram representation of audio and, thanks to a dedicated regularization strategy, can effectively extend the bandwidth of out-of-distribution real historical recordings. The BEHM-GAN is designed to be applied as a second step after denoising the recording to suppress any additive disturbances, such as clicks and background noise. We train and evaluate the method using solo piano classical music. The proposed method outperforms the compared baselines in both objective and subjective experiments. The results of a formal blind listening test show that BEHM-GAN significantly increases the perceptual sound quality in early-20th-century gramophone recordings. For several items, there is a substantial improvement in the mean opinion score after enhancing historical recordings with the proposed bandwidth-extension algorithm. This study represents a relevant step toward data-driven music restoration in real-world scenarios. |
format | Article |
fullrecord | <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2650101151</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2650101151</sourcerecordid><originalsourceid>FETCH-proquest_journals_26501011513</originalsourceid><addsrcrecordid>eNqNir0OgjAYABsTE4nyDk2cSfpj0biBQVhwMnEkDRQtklb7FfDxZfABXO6GuwUKGOc0OuwYW6EQoCOEsHjPhOABuqVZUUZ5cjniVJpm0o1_4OzjlQFtDbYtLjR463Qte1wOoGs8w9xxroxy0utR4aQZlQPp9LxclJ-se8IGLVvZgwp_XqPtObueiujl7HtQ4KvODs7MqWKxIJRQKij_7_oCtsxAtg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2650101151</pqid></control><display><type>article</type><title>BEHM-GAN: Bandwidth Extension of Historical Music using Generative Adversarial Networks</title><source>Free E- Journals</source><creator>Moliner, Eloi ; Välimäki, Vesa</creator><creatorcontrib>Moliner, Eloi ; Välimäki, Vesa</creatorcontrib><description>Audio bandwidth extension aims to expand the spectrum of narrow-band audio signals. Although this topic has been broadly studied during recent years, the particular problem of extending the bandwidth of historical music recordings remains an open challenge. This paper proposes BEHM-GAN, a model based on generative adversarial networks, as a practical solution to this problem. The proposed method works with the complex spectrogram representation of audio and, thanks to a dedicated regularization strategy, can effectively extend the bandwidth of out-of-distribution real historical recordings. The BEHM-GAN is designed to be applied as a second step after denoising the recording to suppress any additive disturbances, such as clicks and background noise. We train and evaluate the method using solo piano classical music. The proposed method outperforms the compared baselines in both objective and subjective experiments. The results of a formal blind listening test show that BEHM-GAN significantly increases the perceptual sound quality in early-20th-century gramophone recordings. For several items, there is a substantial improvement in the mean opinion score after enhancing historical recordings with the proposed bandwidth-extension algorithm. This study represents a relevant step toward data-driven music restoration in real-world scenarios.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Algorithms ; Audio signals ; Background noise ; Bandwidths ; Generative adversarial networks ; Music ; Regularization</subject><ispartof>arXiv.org, 2022-06</ispartof><rights>2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>780,784</link.rule.ids></links><search><creatorcontrib>Moliner, Eloi</creatorcontrib><creatorcontrib>Välimäki, Vesa</creatorcontrib><title>BEHM-GAN: Bandwidth Extension of Historical Music using Generative Adversarial Networks</title><title>arXiv.org</title><description>Audio bandwidth extension aims to expand the spectrum of narrow-band audio signals. Although this topic has been broadly studied during recent years, the particular problem of extending the bandwidth of historical music recordings remains an open challenge. This paper proposes BEHM-GAN, a model based on generative adversarial networks, as a practical solution to this problem. The proposed method works with the complex spectrogram representation of audio and, thanks to a dedicated regularization strategy, can effectively extend the bandwidth of out-of-distribution real historical recordings. The BEHM-GAN is designed to be applied as a second step after denoising the recording to suppress any additive disturbances, such as clicks and background noise. We train and evaluate the method using solo piano classical music. The proposed method outperforms the compared baselines in both objective and subjective experiments. The results of a formal blind listening test show that BEHM-GAN significantly increases the perceptual sound quality in early-20th-century gramophone recordings. For several items, there is a substantial improvement in the mean opinion score after enhancing historical recordings with the proposed bandwidth-extension algorithm. This study represents a relevant step toward data-driven music restoration in real-world scenarios.</description><subject>Algorithms</subject><subject>Audio signals</subject><subject>Background noise</subject><subject>Bandwidths</subject><subject>Generative adversarial networks</subject><subject>Music</subject><subject>Regularization</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNqNir0OgjAYABsTE4nyDk2cSfpj0biBQVhwMnEkDRQtklb7FfDxZfABXO6GuwUKGOc0OuwYW6EQoCOEsHjPhOABuqVZUUZ5cjniVJpm0o1_4OzjlQFtDbYtLjR463Qte1wOoGs8w9xxroxy0utR4aQZlQPp9LxclJ-se8IGLVvZgwp_XqPtObueiujl7HtQ4KvODs7MqWKxIJRQKij_7_oCtsxAtg</recordid><startdate>20220628</startdate><enddate>20220628</enddate><creator>Moliner, Eloi</creator><creator>Välimäki, Vesa</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20220628</creationdate><title>BEHM-GAN: Bandwidth Extension of Historical Music using Generative Adversarial Networks</title><author>Moliner, Eloi ; Välimäki, Vesa</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_26501011513</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Audio signals</topic><topic>Background noise</topic><topic>Bandwidths</topic><topic>Generative adversarial networks</topic><topic>Music</topic><topic>Regularization</topic><toplevel>online_resources</toplevel><creatorcontrib>Moliner, Eloi</creatorcontrib><creatorcontrib>Välimäki, Vesa</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Moliner, Eloi</au><au>Välimäki, Vesa</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>BEHM-GAN: Bandwidth Extension of Historical Music using Generative Adversarial Networks</atitle><jtitle>arXiv.org</jtitle><date>2022-06-28</date><risdate>2022</risdate><eissn>2331-8422</eissn><abstract>Audio bandwidth extension aims to expand the spectrum of narrow-band audio signals. Although this topic has been broadly studied during recent years, the particular problem of extending the bandwidth of historical music recordings remains an open challenge. This paper proposes BEHM-GAN, a model based on generative adversarial networks, as a practical solution to this problem. The proposed method works with the complex spectrogram representation of audio and, thanks to a dedicated regularization strategy, can effectively extend the bandwidth of out-of-distribution real historical recordings. The BEHM-GAN is designed to be applied as a second step after denoising the recording to suppress any additive disturbances, such as clicks and background noise. We train and evaluate the method using solo piano classical music. The proposed method outperforms the compared baselines in both objective and subjective experiments. The results of a formal blind listening test show that BEHM-GAN significantly increases the perceptual sound quality in early-20th-century gramophone recordings. For several items, there is a substantial improvement in the mean opinion score after enhancing historical recordings with the proposed bandwidth-extension algorithm. This study represents a relevant step toward data-driven music restoration in real-world scenarios.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | EISSN: 2331-8422 |
ispartof | arXiv.org, 2022-06 |
issn | 2331-8422 |
language | eng |
recordid | cdi_proquest_journals_2650101151 |
source | Free E- Journals |
subjects | Algorithms Audio signals Background noise Bandwidths Generative adversarial networks Music Regularization |
title | BEHM-GAN: Bandwidth Extension of Historical Music using Generative Adversarial Networks |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T14%3A50%3A47IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=BEHM-GAN:%20Bandwidth%20Extension%20of%20Historical%20Music%20using%20Generative%20Adversarial%20Networks&rft.jtitle=arXiv.org&rft.au=Moliner,%20Eloi&rft.date=2022-06-28&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E2650101151%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2650101151&rft_id=info:pmid/&rfr_iscdi=true |