Bi-LSTM deep reinforcement learning network-based drum music generation method

The invention discloses a folk drumbeat generation method based on a Bi-LSTM deep reinforcement learning network. The method comprises the following steps: S100, representing folk ancient music as a note sequence; s200, converting the generated note sequence into a note vector set through a coding t...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: LI PENG, WANG XIAOMING, LIANG TIANMIAN, WU XIAOJUN, CAO YUMEI
Format: Patent
Sprache:chi ; eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator LI PENG
WANG XIAOMING
LIANG TIANMIAN
WU XIAOJUN
CAO YUMEI
description The invention discloses a folk drumbeat generation method based on a Bi-LSTM deep reinforcement learning network. The method comprises the following steps: S100, representing folk ancient music as a note sequence; s200, converting the generated note sequence into a note vector set through a coding technology; s300, inputting the note vector set into a Bi-LSTM neural network for training, and generating a Bi-LSTM drumbeat generation network based on a character level; s400, the Bi-LSTM drumbeat generation network based on the character level is trained based on a reinforcement learning Actor-Critic algorithm, and a deep reinforcement learning network based on Bi-LSTM is obtained; and S500, inputting the input note sequence into the Bi-LSTM-based deep reinforcement learning network, and autonomously generating drum music works. According to the method, the generation of high-quality and artistic Xian drum music can be basically realized, and the problem of lack of Xian drum music tracks is relieved. 一种基于Bi-LSTM
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN116229922A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN116229922A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN116229922A3</originalsourceid><addsrcrecordid>eNqNy7EOwiAURmEWB6O-w_UBGIqJSUdtNA7axe4Nwt9KLBcCNL6-Dj6A01m-sxTt0cnrvbuRBSIlOB5CMvDgQhN0YscjMco7pJd86AxLNs2e_JydoRGMpIsLTB7lGexaLAY9ZWx-XYnt-dQ1F4kYeuSozfcofdNW1V6pulbqsPvHfADrXjZs</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Bi-LSTM deep reinforcement learning network-based drum music generation method</title><source>esp@cenet</source><creator>LI PENG ; WANG XIAOMING ; LIANG TIANMIAN ; WU XIAOJUN ; CAO YUMEI</creator><creatorcontrib>LI PENG ; WANG XIAOMING ; LIANG TIANMIAN ; WU XIAOJUN ; CAO YUMEI</creatorcontrib><description>The invention discloses a folk drumbeat generation method based on a Bi-LSTM deep reinforcement learning network. The method comprises the following steps: S100, representing folk ancient music as a note sequence; s200, converting the generated note sequence into a note vector set through a coding technology; s300, inputting the note vector set into a Bi-LSTM neural network for training, and generating a Bi-LSTM drumbeat generation network based on a character level; s400, the Bi-LSTM drumbeat generation network based on the character level is trained based on a reinforcement learning Actor-Critic algorithm, and a deep reinforcement learning network based on Bi-LSTM is obtained; and S500, inputting the input note sequence into the Bi-LSTM-based deep reinforcement learning network, and autonomously generating drum music works. According to the method, the generation of high-quality and artistic Xian drum music can be basically realized, and the problem of lack of Xian drum music tracks is relieved. 一种基于Bi-LSTM</description><language>chi ; eng</language><subject>ACOUSTICS ; CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTROPHONIC MUSICAL INSTRUMENTS ; MUSICAL INSTRUMENTS ; PHYSICS</subject><creationdate>2023</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20230606&amp;DB=EPODOC&amp;CC=CN&amp;NR=116229922A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,309,781,886,25569,76552</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20230606&amp;DB=EPODOC&amp;CC=CN&amp;NR=116229922A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>LI PENG</creatorcontrib><creatorcontrib>WANG XIAOMING</creatorcontrib><creatorcontrib>LIANG TIANMIAN</creatorcontrib><creatorcontrib>WU XIAOJUN</creatorcontrib><creatorcontrib>CAO YUMEI</creatorcontrib><title>Bi-LSTM deep reinforcement learning network-based drum music generation method</title><description>The invention discloses a folk drumbeat generation method based on a Bi-LSTM deep reinforcement learning network. The method comprises the following steps: S100, representing folk ancient music as a note sequence; s200, converting the generated note sequence into a note vector set through a coding technology; s300, inputting the note vector set into a Bi-LSTM neural network for training, and generating a Bi-LSTM drumbeat generation network based on a character level; s400, the Bi-LSTM drumbeat generation network based on the character level is trained based on a reinforcement learning Actor-Critic algorithm, and a deep reinforcement learning network based on Bi-LSTM is obtained; and S500, inputting the input note sequence into the Bi-LSTM-based deep reinforcement learning network, and autonomously generating drum music works. According to the method, the generation of high-quality and artistic Xian drum music can be basically realized, and the problem of lack of Xian drum music tracks is relieved. 一种基于Bi-LSTM</description><subject>ACOUSTICS</subject><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTROPHONIC MUSICAL INSTRUMENTS</subject><subject>MUSICAL INSTRUMENTS</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNy7EOwiAURmEWB6O-w_UBGIqJSUdtNA7axe4Nwt9KLBcCNL6-Dj6A01m-sxTt0cnrvbuRBSIlOB5CMvDgQhN0YscjMco7pJd86AxLNs2e_JydoRGMpIsLTB7lGexaLAY9ZWx-XYnt-dQ1F4kYeuSozfcofdNW1V6pulbqsPvHfADrXjZs</recordid><startdate>20230606</startdate><enddate>20230606</enddate><creator>LI PENG</creator><creator>WANG XIAOMING</creator><creator>LIANG TIANMIAN</creator><creator>WU XIAOJUN</creator><creator>CAO YUMEI</creator><scope>EVB</scope></search><sort><creationdate>20230606</creationdate><title>Bi-LSTM deep reinforcement learning network-based drum music generation method</title><author>LI PENG ; WANG XIAOMING ; LIANG TIANMIAN ; WU XIAOJUN ; CAO YUMEI</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN116229922A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2023</creationdate><topic>ACOUSTICS</topic><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTROPHONIC MUSICAL INSTRUMENTS</topic><topic>MUSICAL INSTRUMENTS</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>LI PENG</creatorcontrib><creatorcontrib>WANG XIAOMING</creatorcontrib><creatorcontrib>LIANG TIANMIAN</creatorcontrib><creatorcontrib>WU XIAOJUN</creatorcontrib><creatorcontrib>CAO YUMEI</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>LI PENG</au><au>WANG XIAOMING</au><au>LIANG TIANMIAN</au><au>WU XIAOJUN</au><au>CAO YUMEI</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Bi-LSTM deep reinforcement learning network-based drum music generation method</title><date>2023-06-06</date><risdate>2023</risdate><abstract>The invention discloses a folk drumbeat generation method based on a Bi-LSTM deep reinforcement learning network. The method comprises the following steps: S100, representing folk ancient music as a note sequence; s200, converting the generated note sequence into a note vector set through a coding technology; s300, inputting the note vector set into a Bi-LSTM neural network for training, and generating a Bi-LSTM drumbeat generation network based on a character level; s400, the Bi-LSTM drumbeat generation network based on the character level is trained based on a reinforcement learning Actor-Critic algorithm, and a deep reinforcement learning network based on Bi-LSTM is obtained; and S500, inputting the input note sequence into the Bi-LSTM-based deep reinforcement learning network, and autonomously generating drum music works. According to the method, the generation of high-quality and artistic Xian drum music can be basically realized, and the problem of lack of Xian drum music tracks is relieved. 一种基于Bi-LSTM</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language chi ; eng
recordid cdi_epo_espacenet_CN116229922A
source esp@cenet
subjects ACOUSTICS
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTROPHONIC MUSICAL INSTRUMENTS
MUSICAL INSTRUMENTS
PHYSICS
title Bi-LSTM deep reinforcement learning network-based drum music generation method
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-12T02%3A24%3A53IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=LI%20PENG&rft.date=2023-06-06&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN116229922A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true