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...
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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 |
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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&date=20230606&DB=EPODOC&CC=CN&NR=116229922A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,309,781,886,25569,76552</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230606&DB=EPODOC&CC=CN&NR=116229922A$$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> |
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language | chi ; eng |
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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 |
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