SYNTHESIZING TRUMPET PERFORMANCES

This paper presents our latest results in synthesizing high quality trumpet performances. Our approach identifies the continuous control over the sound as a fundamental element in the synthesis process. We developed our synthesis model as a cooperative system of two sub-models, continuous control parameters providing the interface between them. The two sub-parts are the instrument model, which takes sources of continuous control signals as input and produces an audio output, and the performance model, which takes some symbolic representation of music as an input, and produces control signals. Our goal is to develop a synthesis model capable of rendering highly realistic trumpet performances. Our impression is that current synthesis techniques fail to achieve that goal for a couple of reasons. The trumpet (among other instruments) can be characterized by the fact that the player exercises continuous control over the course of notes and phrases. This results in a continuous evolution of spectrum as a function of that control. It follows naturally that template-based synthesis is not able to synthesize highly realistic performances of this type of instrument. (By template based synthesis we mean storing and combining individual recorded notes. Most of the current commercially available synthesis systems are sample based and belong to this category.) A successful synthesis technique has to be able to render sound based on continuous control. Also, the specific realization of such a control depends strongly on the musical context in which the actual note is embedded. As an example, we can show that the amplitude envelope shape of a single note is dependent upon the pitch contour of the containing phrase. (Dannenberg, Pellerin, and Derenyi 1998) From this, it follows that synthesis of single notes (which is the practice followed by most synthesis research) is not adequate for our purposes either. We believe that a more holistic integration of control and synthesis is necessary for realistic synthesis and to create appropriate control functions for the synthesis. As we pointed out, the continuous control signals have to play a key role in the synthesis process. The idea of control signals is quite common and its use can be identified in most of the synthesis techniques. However, the problem of how to produce appropriate control signals remains. There are two "directions" in which we would like to derive the control signals. During testing, we would like to measure "reference" control signals from real performances and compare them to synthetic control signals. FM synthesis is a good example how problematic this issue can be. During synthesis, as an ultimate goal, we would like to derive our control signals from symbolic data. If those control signals are closely tied to musical concepts such as amplitude or pitch, then rules to produce those control signals can be derived by hand or by machine learning techniques. However, if the control signals represent peculiarities of the synthesis technique (such as with different physical modeling synthesis techniques, then control signals are more difficult to derive. We propose a new technique, which addresses these requirements.