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L'article présente une nouvelle bibliothèque OpenMusic qui met en œuvre une méthode de programmation géo-nétique de régression symbolique sur des ensembles de points de données d'entrée et cherche une fonction Common Lisp (S-expression) qui peut être utilisée soit pour créer des modèles mathématiques qui pourraient potentiellement aider à comprendre le comportement mathématique des données d'entrée, soit pour générer des paramètres dans la composition assistée par ordinateur. Soulignant qu'un certain nombre de questions doivent encore être abordées pour prouver la bibliothèque proposée, l'article présente certaines des stratégies pour le faire et pour faire de la régression symbolique un outil pratique dans la composition et l'analyse musicales assistées par ordinateur.
El documento presenta una nueva biblioteca OpenMusic que implementa un método de Programación Genética de Regresión Simbólica en conjuntos de puntos de datos de entrada y busca una función Common Lisp (expresión S) que se puede usar para crear modelos matemáticos que podrían ayudar a comprender el comportamiento matemático de los datos de entrada o para generar parámetros en la composición asistida por computadora. Haciendo hincapié en que aún deben abordarse una serie de cuestiones para mejorar la biblioteca propuesta, el documento presenta algunas de las estrategias para hacerlo y para hacer de la regresión simbólica una herramienta práctica en la composición y el análisis de música asistida por ordenador.
The paper presents a new OpenMusic library that implements a Ge- netic Programming method of Symbolic Regression on sets of input data-points and seeks for a Common Lisp function (S-expression) that can be used either to create mathematical models that could potentially help to understand the mathe- matical behavior of the input data or to generate parameters in computer-aided composition. Stressing that a number of issues must still be addressed to im- prove the proposed library, the paper presents some of the strategies to do this and to make Symbolic Regression a practical tool in computer-assisted music composition and analysis.
تقدم الورقة مكتبة OpenMusic جديدة تنفذ طريقة البرمجة الجينية للانحدار الرمزي على مجموعات من نقاط بيانات الإدخال وتسعى إلى دالة LISP مشتركة (S - express) يمكن استخدامها إما لإنشاء نماذج رياضية يمكن أن تساعد في فهم السلوك الرياضي لبيانات الإدخال أو لتوليد المعلمات في التكوين بمساعدة الكمبيوتر. مع التأكيد على أنه لا يزال يتعين معالجة عدد من القضايا لإثبات المكتبة المقترحة، تقدم الورقة بعض الاستراتيجيات للقيام بذلك وجعل الانحدار الرمزي أداة عملية في تكوين الموسيقى وتحليلها بمساعدة الكمبيوتر.
Function Approximation, Computer Aided Musicology, Computational Modelling, Symbolic Regression, Genetic Programming, Computer-aided Composition, Visual arts, Machine Learning, Semantic Genetic Programming, Theoretical computer science, Artificial Intelligence, Machine learning, FOS: Mathematics, Musical composition, Composition (language), Genetic Algorithms, Statistics, Computer music, Neural Network Fundamentals and Applications, Music education, Computer science, Regression, Symbolic data analysis, Interactive Evolutionary Music Systems and Instruments, Literature, Application of Genetic Programming in Machine Learning, Computer Science, Physical Sciences, Computer Music, Regression Analysis, Musical, Computer Vision and Pattern Recognition, Regression analysis, Mathematics, Art
Function Approximation, Computer Aided Musicology, Computational Modelling, Symbolic Regression, Genetic Programming, Computer-aided Composition, Visual arts, Machine Learning, Semantic Genetic Programming, Theoretical computer science, Artificial Intelligence, Machine learning, FOS: Mathematics, Musical composition, Composition (language), Genetic Algorithms, Statistics, Computer music, Neural Network Fundamentals and Applications, Music education, Computer science, Regression, Symbolic data analysis, Interactive Evolutionary Music Systems and Instruments, Literature, Application of Genetic Programming in Machine Learning, Computer Science, Physical Sciences, Computer Music, Regression Analysis, Musical, Computer Vision and Pattern Recognition, Regression analysis, Mathematics, Art
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