Title: Musical Performance Dynamics Analysis Objective: Musical Performance Dynamics Analysis (MPDA) entails the creation of an archetypal model for the dynamic variation in human-played acoustic instruments. Dynamics include subtle or pronounced changes in volume, timbre, pitch, or articulation, and are produced when the performer encounters musical stimuli. The project will entail the analysis of these musical stimuli and their effects, as well as the development of applications to allow users to create models of the dynamics of acoustic instruments and use them to process MIDI sequences. Justification: In electronically-rendered music, a long-standing limitation has been the expressiveness and believability of sequenced sampled acoustic instruments. Not only is it a time-consuming process to hand-enter or record the fine details of performance for every instrumental part - it requires an intimacy with the working of the instrument itself. By allowing players of instruments to create models of their instruments (for their own use and for the use of others), composers will be provided with a great resource to enhance the realism and expressiveness of their electronically-rendered creations. Description: The project will have several stages. First will be the analysis stage (and it will perhaps be the most substantial). Analysis must be conducted to discern what parameters should be used and in what format they should be related in when modelling acoustic response to musical stimuli. Sometime after the analysis is concluded, it will be necessary to create a simple way for instrument-players to model their own instruments - until I develop this application, I will model the instruments myself as best as possible. The last stage will entail creating the application that will employ any given model to process a MIDI sequence, causing the sequence to react to stimuli as the model entails. Limitations: The main limitation will be my ability to create a simple way for instrumentalists to create models for their instruments without compromising the detail of the general model. If the process is too complicated, the database of models will never grow large enough to provide the resource it has the potential to.