Monterey Mirror: Combining Markov Models, Genetic Algorithms, and Power Laws An Experiment in Interactive Evolutionary Music Performance
نویسندگان
چکیده
Monterey Mirror is an interactive stochastic music generator based on Markov models, genetic algorithms, and power-law metrics for music information retrieval. It combines the predictive power of Markov models with the innovative power of genetic algorithms, using power-law metrics for fitness evaluation. These metrics have been developed and refined in a decade-long project, which explores music information retrieval based on Zipf’s law and related power laws. We describe the architecture of Monterey Mirror, which can generate musical responses based on aesthetic variations of user input. We also explore how such a system may be used as a musical metainstrument / environment in avant-garde music composition and performance projects.
منابع مشابه
Optimization of sediment rating curve coefficients using evolutionary algorithms and unsupervised artificial neural network
Sediment rating curve (SRC) is a conventional and a common regression model in estimating suspended sediment load (SSL) of flow discharge. However, in most cases the data log-transformation in SRC models causing a bias which underestimates SSL prediction. In this study, using the daily stream flow and suspended sediment load data from Shalman hydrometric station on Shalmanroud River, Guilan Pro...
متن کاملOptimal Reconfiguration of Distribution Network for Power Loss Reduction and Reliability Improvement Using Bat Algorithm
In power systems, reconfiguration is one of the simplest and most low-cost methods to reach many goals such as self-healing, reliability improvement, and power loss reduction, without including any additional components. Regarding the expansion of distribution networks, communications become more complicate and the number of parameters increases, which makes the reconfiguration problem infeasib...
متن کاملNovel Hybrid Fuzzy-Evolutionary Algorithms for Optimization of a Fuzzy Expert System Applied to Dust Phenomenon Forecasting Problem
Nowadays, dust phenomenon is one of the important challenges in warm and dry areas. Forecasting the phenomenon before its occurrence helps to take precautionary steps to prevent its consequences. Fuzzy expert systems capabilities have been taken into account to assist and cope with the uncertainty associated to complex environments such as dust forecasting problem. This paper presents novel hyb...
متن کاملAutomatic Composition of Music with Methods of Computational Intelligence
We describe our approach for the automatic composition of monophone melodies on a user given chord sequence. For this purpose we use Markov-chains and other methods to build some initial individuals. These are then optimised with evolutionary algorithms using automatically learned classifiers next to interactive evaluation as fitness functions. Key–Words: computational Intelligence, automatic c...
متن کاملNovel Hybrid Fuzzy-Evolutionary Algorithms for Optimization of a Fuzzy Expert System Applied to Dust Phenomenon Forecasting Problem
Nowadays, dust phenomenon is one of the important challenges in warm and dry areas. Forecasting the phenomenon before its occurrence helps to take precautionary steps to prevent its consequences. Fuzzy expert systems capabilities have been taken into account to assist and cope with the uncertainty associated to complex environments such as dust forecasting problem. This paper presents novel hyb...
متن کامل