@techreport{derbinsky_exploring_nodate,
	title = {Exploring {Reinforcement} {Learning} for {Mobile} {Percussive} {Collaboration}},
	abstract = {This paper presents a system for mobile percussive collaboration. We show that reinforcement learning can incremen-tally learn percussive beat patterns played by humans and supports real-time collaborative performance in the absence of one or more performers. This work leverages an existing integration between urMus and Soar and addresses multiple challenges involved in the deployment of machine-learning algorithms for mobile music expression, including tradeoffs between learning speed \& quality; interface design for human collaborators; and real-time performance and improvisation .},
	urldate = {2019-09-04},
	author = {Derbinsky, Nate and Essl, Georg},
	keywords = {Mobile music, cognitive architecture, machine learning},
}

{"_id":"4pNAHWQBq4amxvQpt","bibbaseid":"derbinsky-essl-exploringreinforcementlearningformobilepercussivecollaboration","author_short":["Derbinsky, N.","Essl, G."],"bibdata":{"bibtype":"techreport","type":"techreport","title":"Exploring Reinforcement Learning for Mobile Percussive Collaboration","abstract":"This paper presents a system for mobile percussive collaboration. We show that reinforcement learning can incremen-tally learn percussive beat patterns played by humans and supports real-time collaborative performance in the absence of one or more performers. This work leverages an existing integration between urMus and Soar and addresses multiple challenges involved in the deployment of machine-learning algorithms for mobile music expression, including tradeoffs between learning speed & quality; interface design for human collaborators; and real-time performance and improvisation .","urldate":"2019-09-04","author":[{"propositions":[],"lastnames":["Derbinsky"],"firstnames":["Nate"],"suffixes":[]},{"propositions":[],"lastnames":["Essl"],"firstnames":["Georg"],"suffixes":[]}],"keywords":"Mobile music, cognitive architecture, machine learning","bibtex":"@techreport{derbinsky_exploring_nodate,\n\ttitle = {Exploring {Reinforcement} {Learning} for {Mobile} {Percussive} {Collaboration}},\n\tabstract = {This paper presents a system for mobile percussive collaboration. We show that reinforcement learning can incremen-tally learn percussive beat patterns played by humans and supports real-time collaborative performance in the absence of one or more performers. This work leverages an existing integration between urMus and Soar and addresses multiple challenges involved in the deployment of machine-learning algorithms for mobile music expression, including tradeoffs between learning speed \\& quality; interface design for human collaborators; and real-time performance and improvisation .},\n\turldate = {2019-09-04},\n\tauthor = {Derbinsky, Nate and Essl, Georg},\n\tkeywords = {Mobile music, cognitive architecture, machine learning},\n}\n\n","author_short":["Derbinsky, N.","Essl, G."],"key":"derbinsky_exploring_nodate","id":"derbinsky_exploring_nodate","bibbaseid":"derbinsky-essl-exploringreinforcementlearningformobilepercussivecollaboration","role":"author","urls":{},"keyword":["Mobile music","cognitive architecture","machine learning"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"techreport","biburl":"https://api.zotero.org/users/1999854/collections/DCM2A6Q7/items?key=bNsZItJrr69T2OxakZEi8c4h&format=bibtex&limit=100","dataSources":["zZu8sFBHHHNgxZx6k"],"keywords":["mobile music","cognitive architecture","machine learning"],"search_terms":["exploring","reinforcement","learning","mobile","percussive","collaboration","derbinsky","essl"],"title":"Exploring Reinforcement Learning for Mobile Percussive Collaboration","year":null}