Mixing consoleA mixing console or mixing desk is an electronic device for mixing audio signals, used in sound recording and reproduction and sound reinforcement systems. Inputs to the console include microphones, signals from electric or electronic instruments, or recorded sounds. Mixers may control analog or digital signals. The modified signals are summed to produce the combined output signals, which can then be broadcast, amplified through a sound reinforcement system or recorded.
Audio mixing (recorded music)In sound recording and reproduction, audio mixing is the process of optimizing and combining multitrack recordings into a final mono, stereo or surround sound product. In the process of combining the separate tracks, their relative levels are adjusted and balanced and various processes such as equalization and compression are commonly applied to individual tracks, groups of tracks, and the overall mix. In stereo and surround sound mixing, the placement of the tracks within the stereo (or surround) field are adjusted and balanced.
Machine learningMachine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.
Supervised learningSupervised learning (SL) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value (also known as human-labeled supervisory signal) train a model. The training data is processed, building a function that maps new data on expected output values. An optimal scenario will allow for the algorithm to correctly determine output values for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way (see inductive bias).
Byzantine mosaicsByzantine mosaics are mosaics produced from the 4th to 15th centuries in and under the influence of the Byzantine Empire. Mosaics were some of the most popular and historically significant art forms produced in the empire, and they are still studied extensively by art historians. Although Byzantine mosaics evolved out of earlier Hellenistic and Roman practices and styles, craftspeople within the Byzantine Empire made important technical advances and developed mosaic art into a unique and powerful form of personal and religious expression that exerted significant influence on Islamic art produced in Umayyad and Abbasid Caliphates and the Ottoman Empire.
Coordinate descentCoordinate descent is an optimization algorithm that successively minimizes along coordinate directions to find the minimum of a function. At each iteration, the algorithm determines a coordinate or coordinate block via a coordinate selection rule, then exactly or inexactly minimizes over the corresponding coordinate hyperplane while fixing all other coordinates or coordinate blocks. A line search along the coordinate direction can be performed at the current iterate to determine the appropriate step size.
Optimal controlOptimal control theory is a branch of mathematical optimization that deals with finding a control for a dynamical system over a period of time such that an objective function is optimized. It has numerous applications in science, engineering and operations research. For example, the dynamical system might be a spacecraft with controls corresponding to rocket thrusters, and the objective might be to reach the moon with minimum fuel expenditure.
Reinforcement learningReinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected.
Ensemble learningIn statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives.
Feature learningIn machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.
