2 resultados para sequential-move contest
em DRUM (Digital Repository at the University of Maryland)
Resumo:
MOVE is a composition for string quartet, piano, percussion and electronics of approximately 15-16 minutes duration in three movements. The work incorporates electronic samples either synthesized electronically by the composer or recorded from acoustic instruments. The work aims to use electronic sounds as an expansion of the tonal palette of the chamber group (rather like an extended percussion setup) as opposed to a dominating sonic feature of the music. This is done by limiting the use of electronics to specific sections of the work, and by prioritizing blend and sonic coherence in the synthesized samples. The work uses fixed electronics in such a way that allows for tempo variations in the music. Generally, a difficulty arises in that fixed “tape” parts don’t allow tempo variations; while truly “live” software algorithms sacrifice rhythmic accuracy. Sample pads, such as the Roland SPD-SX, provide an elegant solution. The latency of such a device is close enough to zero that individual samples can be triggered in real time at a range of tempi. The percussion setup in this work (vibraphone and sample pad) allows one player to cover both parts, eliminating the need for an external musician to trigger the electronics. Compositionally, momentum is used as a constructing principle. The first movement makes prominent use of ostinato and shifting meter. The second is a set of variations on a repeated harmonic pattern, with a polymetric middle section. The third is a type of passacaglia, wherein the bassline is not introduced right away, but becomes more significant later in the movement. Given the importance of visual presentation in the Internet age, the final goal of the project was to shoot HD video of a studio performance of the work for publication online. The composer recorded audio and video in two separate sessions and edited the production using Logic X and Adobe Premiere Pro. The final video presentation can be seen at geoffsheil.com/move.
Resumo:
Natural language processing has achieved great success in a wide range of ap- plications, producing both commercial language services and open-source language tools. However, most methods take a static or batch approach, assuming that the model has all information it needs and makes a one-time prediction. In this disser- tation, we study dynamic problems where the input comes in a sequence instead of all at once, and the output must be produced while the input is arriving. In these problems, predictions are often made based only on partial information. We see this dynamic setting in many real-time, interactive applications. These problems usually involve a trade-off between the amount of input received (cost) and the quality of the output prediction (accuracy). Therefore, the evaluation considers both objectives (e.g., plotting a Pareto curve). Our goal is to develop a formal understanding of sequential prediction and decision-making problems in natural language processing and to propose efficient solutions. Toward this end, we present meta-algorithms that take an existent batch model and produce a dynamic model to handle sequential inputs and outputs. Webuild our framework upon theories of Markov Decision Process (MDP), which allows learning to trade off competing objectives in a principled way. The main machine learning techniques we use are from imitation learning and reinforcement learning, and we advance current techniques to tackle problems arising in our settings. We evaluate our algorithm on a variety of applications, including dependency parsing, machine translation, and question answering. We show that our approach achieves a better cost-accuracy trade-off than the batch approach and heuristic-based decision- making approaches. We first propose a general framework for cost-sensitive prediction, where dif- ferent parts of the input come at different costs. We formulate a decision-making process that selects pieces of the input sequentially, and the selection is adaptive to each instance. Our approach is evaluated on both standard classification tasks and a structured prediction task (dependency parsing). We show that it achieves similar prediction quality to methods that use all input, while inducing a much smaller cost. Next, we extend the framework to problems where the input is revealed incremen- tally in a fixed order. We study two applications: simultaneous machine translation and quiz bowl (incremental text classification). We discuss challenges in this set- ting and show that adding domain knowledge eases the decision-making problem. A central theme throughout the chapters is an MDP formulation of a challenging problem with sequential input/output and trade-off decisions, accompanied by a learning algorithm that solves the MDP.