[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117642-en":3,"doc-seo-117642-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},117642,962085570644,"Evangeline","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Algorithmic composition of melodies with deep recurrent neural networks - Research summary and future work","This presentation studies how deep recurrent neural networks can be trained on large melody corpora to generate new music that remains coherent with the learned style. Gated-recurrent unit (GRU) models process rhythm and melody in parallel while capturing their relationship. The approach addresses long-range temporal dependencies that typical algorithmic composition systems may miss. Experiments include GRU-RNN prediction of upcoming notes from symbolic pitch and duration representations, enabling both complete autonomous melodies and coherent continuations of fragments.","View metadata, citation and similar [papers at ](papers at core.ac.uk)[core.ac.uk](papers at core.ac.uk)  \nAlgorithmic composition of melodies with deep recurrent neural networks  \nFlorian Colombo, Alexander Seeholzer, Samuel P. Muscinelli,  \nJohanni Brea & Wulfram Gerstner  \nSchool of Life Sciences, Brain Mind Institute & School of Computer and Communication Sciences  \nEcole Polytechnique Fédérale de Lausanne (EPFL), Switzerland  \nbrought to you by CORE  \nprovided by Infoscience- École polytechnique fédérale de Lausanne  \nLCN  \nINTRODUCTION  \nSONG CONTINUATION  \nA big challenge in algorithmic composition is to devise a model that is both easily trainable and able to reproduce the long-range temporal dependencies typical of music. Here we investigate how artificial neural networks can be trained on a large corpus of melodies and turned into automated music composers able to generate new melodies coherent with the style they have been trained on .  \nWe employ gated-recurrent unit (GRU) networks that have been shown tobe particularly efficient in learning complex sequential activations with arbitrary long time lags [1] . Our model processes rhythm and melody in parallel while modeling the relation between these two properties. Using such an approach, we were able to generate interesting complete melodies or suggest possible continuations of a melody fragment that is coherent with the characteristics of the fragment itself.  \nMUSIC REPRESENTATION  \na  a  b  b  c  c  d d  \nMusic representation  \nA2 0 0 0 0 | 0 0 0 0 | 0 0 0 | 0 0 0 | 0 1 0 0 0 0 | 0 1 0 0 0 0 | 0 0 0 | 0 0 0 | 0 G2 0 0 0 0 | 0 0 0 0 | 0 0 1 | 0 0 1 | 1 0 1 0 0 0 | 1 0 1 0 0 0 | 0 0 0 | 0 0 0 | 0 F2 0 0 0 0 | 0 0 0 0 | 0 1 0 | 0 1 0 | 0 0 0 1 0 0 | 0 0 0 1 0 0 | 0 0 0 | 0 0 0 | 0 E2 0 0 1 0 | 0 0 1 0 | 1 0 0 | 1 0 0 | 0 0 0 0 1 0 | 0 0 0 0 1 0 | 0 0 0 | 0 0 0 | 0 D2 0 1 0 0 | 0 1 0 0 | 0 0 0 | 0 0 0 | 0 0 0 0 0 0 | 0 0 0 0 0 0 | 0 0 0 | 0 0 0 | 0 C2 1 0 0 1 | 1 0 0 1 | 0 0 0 | 0 0 0 | 0 0 0 0 0 1 | 0 0 0 0 0 1 | 1 0 1 | 1 0 1 | 0 G1 0 0 0 0 | 0 0 0 0 | 0 0 0 | 0 0 0 | 0 0 0 0 0 0 | 0 0 0 0 0 0 | 0 1 0 | 0 1 0 | 0 || 0 0 0 0 | 0 0 0 0 | 0 0 0 | 0 0 0 | 0 0 0 0 0 0 | 0 0 0 0 0 0 | 0 0 0 | 0 0 0 | 1  \nduration   \n2 1  \n0 0 0 0 | 0 0 0 0 | 0 0 1 | 0 0 1 | 0 0 0 0 0 0 | 0 0 0 0 0 0 | 0 0 1 | 0 0 1 | 0 1 1 1 1 | 1 1 1 1 | 1 1 0 | 1 1 0 | 0 0 0 0 1 1 | 0 0 0 0 1 1 | 1 1 0 | 1 1 0 | 1  \n1/2 0 0 0 0 | 0 0 0 0 | 0 0 0 | 0 0 0 | 1 1 1 1 0 0 | 1 1 1 1 0 0 | 0 0 0 | 0 0 0 | 0  \nFour possible continuations suggested by the model  \nAUTONOMOUS SONG GENERATION  \nAn Irish tune autonomously generated by the model  \nrepresentation  \npitch  \nThe hidden layers are composed of 128 GRUs  \nhi [n] = zi [n] 􀀌 hi [n 􀀀 1] + (1 􀀀 zi [n]) 􀀌 i [n] ii [[ nn]]  ta􀀛 􀀀nhW􀀀yiWyizi yih[ i yn] [nW] +hiri[nzi hi][ 􀀌nh1]i hibiz[ n􀀁 􀀀 1] 􀀁 ri [n] = 􀀛 􀀀Wy iri yi [n] + Whir ihi [n 􀀀 1] + b ir􀀁 oj [n] = 􀀂 􀀀Wyoo yo [n] + bo 􀀁 j = Pr 􀀀xj [n + 1] = 1 􀀁  \n􀀁 Minimize the log likelihood with Adam optimizer [2]  \nS Ns 􀀀1  \nL (􀀒 j x1:S) =X X log 􀀐 Pr 􀀀xsj [n + 1] = 1 j x[n]; hi [n 􀀀 1]; 􀀒 􀀁􀀑  \ns=1 n=1  \n􀀁 Datasets  \n– 2158 tunes from the Irish music corpus of Henrik Norbecka  \n– 1012 tunes from the Nottingham Music Database b  \n~~ ~~a~~ ~~ttGp:~~ ~~enmeloorbeckeuabc/~~ ~~ xe[n + 1] 􀀘 Cat 􀀀o[n]􀀁  \n b [http://abc.sourceforge.net/NMD/](http://abc.sourceforge.net/NMD/)  \nTRAINING & MELODY GENERATION  \nACKNOWLEDGEMENT  \nSUMMARY  \n􀀁 Symbolic music is represented by its melody and rhythm components  \n􀀁 We trained GRU-RNNs to predict upcoming notes in large corpora of music  \n􀀁 The intrinsic ability of GRU-RNNs to operate on multiple timescales enables them to learn complex temporal dependencies in music  \n􀀁 Computer-aided algorithmic composition is obtained by optimization of the model generalization ability but long-range temporal dependencies are missing  \n􀀁 Solutions that optimize the likelihood given the training data allow for autonomous composition of complete and new melodies exhibiting temporal dependencies on long ti","cbCaiuu7fkvBFo9A","https://ap.wps.com/l/cbCaiuu7fkvBFo9A","pdf",1461368,1,"English","en",105,"# Introduction\n## Song continuation\n# Music representation\n# Autonomous song generation\n## Autonomous song generation\n# Training & melody generation\n## Datasets\n# Summary\n# Future work\n# References","[{\"question\":\"What problem does the model address in algorithmic composition?\",\"answer\":\"It targets the challenge of producing models that are easy to train while reproducing long-range temporal dependencies found in music.\"},{\"question\":\"How are rhythm and melody handled in the proposed network?\",\"answer\":\"The GRU-RNN processes rhythm and melody in parallel and models the relationship between the two properties.\"},{\"question\":\"What capability does the system demonstrate after training?\",\"answer\":\"It can autonomously generate complete melodies or suggest coherent continuations for a melody fragment, showing temporal dependencies over long timescales.\"}]","Algorithmic composition of melodies with deep recurrent neural networks - Research summary and future work | PDF",1785677559,3,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"algorithmic-composition-of-melodies-with-deep-recurrent-neural-networks-research-summary-and-future-work","",{"@graph":35,"@context":84},[36,52,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":28},"https://docshare.wps.com/document/research-report/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/algorithmic-composition-of-melodies-with-deep-recurrent-neural-networks-research-summary-and-future-work/117642/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":22,"description":14,"dateModified":60,"datePublished":61,"encodingFormat":59,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-09-04","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does the model address in algorithmic composition?","Question",{"text":74,"@type":75},"It targets the challenge of producing models that are easy to train while reproducing long-range temporal dependencies found in music.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How are rhythm and melody handled in the proposed network?",{"text":79,"@type":75},"The GRU-RNN processes rhythm and melody in parallel and models the relationship between the two properties.",{"name":81,"@type":72,"acceptedAnswer":82},"What capability does the system demonstrate after training?",{"text":83,"@type":75},"It can autonomously generate complete melodies or suggest coherent continuations for a melody fragment, showing temporal dependencies over long timescales.","https://schema.org",{"og:url":50,"og:type":86,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":88,"canonical":50},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":51,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]