COMBINING PHYSICAL MODELING AND ADDITIVE SYNTHESIS
AS A MAPPING STRATEGY FOR REALTIME CONTROL
Philippe Guillemain
LMA-CNRS 1
13 ch. Joseph Aiguier
13402 Marseille Cedex 20
France
ABSTRACT
This paper presents a mapping strategy for the control of a
signal-based digital synthesis model of the clarinet. This
strategy combines the use of a synthesis model based on
a physical model with the use of a signal model based
on additive synthesis. From the output of the physical
model, specific sound descriptors are extracted and used
to control an additive synthesis model based on the analysis of natural sounds. This approach allows to benefit both
from the control quality of the physical modeling with the
sound quality provided by the additive resynthesis of natural sounds. However, some improvements are still required to better interface and set the two models, particularly concerning transients.
1. INTRODUCTION
The control of self-sustained musical instruments such as
woodwinds is a challenging problem. The nonlinear nature of their functioning implies that subtle variations of
the very small number of continuous parameters controlled by the performer may have important effects on the
perceived timbre. Moreover, gesture capture devices have
been developed for some instruments such as the piano
whereas collecting performance datas from a wind instrument player is a very difficult task to handle.
Synthesis models based on the physics of the instruments have demonstrated their potential in terms of the
naturalness of the control, for now more than fifteen years.
This is largely because such models analytically link control parameters with physical continuous controls of the
performer such as blowing pressure or lip pressure on the
reed. However they somewhat lack naturalness in timbre:
they allow synthesis of the sound of a clarinet but not the
sound of this clarinet (which results from the unique combination of a specific player and a specific instrument).
On the other hand, signal models based on the analysis
of natural sounds (such as additive analysis/resynthesis)
1 Laboratoire de M6canique et d'Acoustique, Centre National de la
Recherche Scientifique.
2 Sound Processing and Control Laboratory.
3 Input Device for Musical Interaction Laboratory.
4 Centre for Interdisciplinary Research in Music Media and Technology.
Vincent Verfaille
SPCL 2 & IDMIL 3,
CIRMMT 4
McGill University
Montreal, Canada
offer the production of timbres perceptually identical to
natural sounds. However, these models lack ease of control, which can be achieved only at the price of building
and indexing additive databases (that represent as many
as real life situations as possible) and specific mapping
strategies. Another drawback is that the database indexing
is done with respect to 'guessed' controls obtained from
the database itself using interpolation mapping strategies,
and not with respect to the physical controls that are not
available. For instance, both Escher [1] and Ssynth [2] use
an additive database of sustained notes structured according to 'abstract parameters': instrument (related to timbre), performed pitch (related to mean fundamental frequency) and performed dynamics (related to mean sound
level and to note brightness).
This paper presents an approach that intends to combine the advantages of both physical models and signal
models by considering them as mapping layers of a synthesizer. It consists first in replacing the physical model
sound output by a realtime sound descriptors extraction,
thus providing an objective link between the physical controls and perceptually relevant features. Secondly, indexing the additive synthesis database with the 'guessed' controls is replaced by indexing based on the sound descriptors related to pitch, loudness and timbre. Sound is finally generated by additive synthesis. Ideally set, such a
physically-controlled additive resynthesis should result in
a sound that is perceptually identical to a natural sound
obeying the physical controls.
We now present and compare both synthesis models
(sec. 2). After developing the synthesis by physicallycontrolled signal models and the selected sound descriptor
set (sec. 3), we will present our first results and discuss
various limitations of the approach and its actual implementation.. We will finally draw our first conclusions and
propose some solutions for further investigations (sec. 5).
2. PHYSICAL MODELS AND SIGNAL MODELS
Physical models takes the viewpoint of the performer and
the control. On the other hand, signal models take the
auditor viewpoint. In this section, we summarize both approaches in the case of clarinet.
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