## Modal Expansion

A well known approach to transfer-function modeling is called
*modal synthesis*, introduced in §1.3.9
[5,299,6,145,381,30].
Modal synthesis may be defined as constructing a source-filter
synthesis model in which the filter transfer function is implemented
as a sum of first- and/or second-order filter sections (*i.e.*, as a
parallel filter bank in which each filter is at most
second-order--this was reviewed in §1.3.9). In other
words, the physical system is represented as a superposition of
individual modes driven by some external excitation (such as a pluck
or strike).

In acoustics, the term *mode of vibration*, or *normal
mode*, normally refers to a single-frequency spatial eigensolution
of the governing wave equation. For example, the modes of an ideal
vibrating string are the harmonically related sinusoidal string-shapes
having an integer number of uniformly spaced zero-crossings (or
*nodes*) along the string, including its endpoints. As first
noted by Daniel Bernoulli (§A.2), acoustic vibration
can be expressed as a superposition of component sinusoidal
vibrations, *i.e.*, as a superposition of modes.^{9.6}

When a single mode is excited by a sinusoidal driving force, all points of the physical object vibrate at the same temporal frequency (cycles per second), and the mode shape becomes proportional to the spatial amplitude envelope of the vibration. The sound emitted from the top plate of a guitar, for example, can be represented as a weighted sum of the radiation patterns of the respective modes of the top plate, where the weighting function is constructed according to how much each mode is excited (typically by the guitar bridge) [143,390,109,205,209].

The impulse-invariant method (§8.2), can be considered a
special case of modal synthesis in which a continuous-time -plane
transfer function is given as a starting point. More typically, modal
synthesis starts with a measured *frequency response*, and a
second-order parallel filter bank is fit to that in some way. In
particular, any filter-design technique may be used (§8.6),
followed by a conversion to second-order parallel form.

Modal expansions find extensive application in industry for determining parametric frequency responses (superpositions of second-order modes) from measured vibration data [299]. Each mode is typically parametrized in terms of its resonant frequency, bandwidth (or damping), and gain (most generally complex gain, to include phase).

Modal synthesis can also be seen as a special case of
*source-filter synthesis*, which may be defined as any signal
model based on factoring a sound-generating process into a filter
driven by some (usually relatively simple) excitation signal. An
early example of source-filter synthesis is the *vocoder*
(§A.6.1). Whenever the filter in a source-filter model is
implemented as a parallel second-order filter bank, we can call it
modal synthesis (although, strictly speaking, each filter section
should correspond to a resonant mode of the modeled resonant system).

Also related to modal synthesis is so-called *formant synthesis*,
used extensively in speech synthesis
[219,255,389,40,81,491,490,313,363,297].
A *formant* is simply a filter resonance having some
center-frequency, bandwidth, and (sometimes specified) gain. Thus, a
formant corresponds to a single mode of vibration in the vocal tract.
Many text-to-speech systems in use today are based on the Klatt
formant synthesizer for speech [255].

Since the importance of spectral formants in sound synthesis has more
to do with the way we hear than with the physical parameters of a
system, formant synthesis is probably best viewed as a *spectral
modeling* synthesis method [456], as opposed to a
physical modeling technique [435]. An exception to this
rule may occur when the system consists physically of a parallel bank
of second-order resonators, such as an array of tuning forks or
Helmholtz resonators. In such a case, the mode parameters correspond
to physically independent objects; this is of course rare in practice.

In the linear, time-invariant case, only modes in the range of human hearing need to be retained in the model. Also, any ``uncontrollable'' or ``unobservable'' modes should obviously be left out as well. With these simplificiation, the modal representation is generally more efficient than an explicit mass-spring-dashpot digitization (§7.3). On the other hand, since the modal representation is usually not directly physical, nonlinear extensions may be difficult and/or behave unnaturally.

As in the impulse-invariant method (§8.2), starting with an
order transfer function describing the input-output
behavior of a physical system, a modal description can be obtained
immediately from the *partial fraction expansion* (PFE):

where denotes the th pole, is the th zero, and is the residue of the th pole [449]. (For simplicity of notation, Eq.(8.9) is written for the case and distinct. See [449] for the details when this is not the case.) Since the system is real, complex poles will occur in complex conjugate pairs. This means that for each complex term , the PFE will also include the complex-conjugate term . Such terms can be summed to create half as many real second-order terms:

(9.10) |

Let denote the number of complex pole pairs, and be the number of real poles. Then the modal expansion can be written as

The real poles, if any, can be paired off into a sum of second-order sections as well, with one first-order section left over when is odd. Note that each component second-order mode consists of one zero and two poles. The zero is necessary to adjust the phase of the resonant mode. The mode phase affects the frequency response at frequencies between resonances (from smoothly varying to the appearance of a deep notch somewhere between them).

A typical practical implementation of each mode in the sum is by means
of second-order filter, or *biquad section* [449]. A
biquad section is easily converted from continuous- to discrete-time
form, such as by the impulse-invariant method of §8.2. The
final discrete-time model then consist of a parallel bank of
second-order digital filters

where when the number of real poles is even, or otherwise. The filter coefficients and are now digital second-order section coefficients which can be computed from the continuous-time filter coefficients, if they are known, or they can be directly estimated from discrete-time data.

Expressing complex poles of in polar form as
, (where now we assume denotes a pole in the
plane), we obtain the classical relations [449]

These parameters are in turn related to the modal parameters resonance frequency (Hz) and bandwidth (Hz) by

Often and are estimated from a measured log-magnitude frequency-response (see §8.8 for a discussion of various methods).

It remains to determine the overall section gain in Eq.(8.12), and the coefficient determining the zero location. If (as is common in practice, since the human ear is not very sensitive to spectral nulls), then can be chosen to match the measured peak height. A particularly simple automatic method is to use least-squares minimization to compute using Eq.(8.12) with and fixed (and ). Generally speaking, least-squares minimization is always immediate when the error to be minimized is linear in the unknown parameter (in this case ).

To estimate in Eq.(8.12), an effective method is to work
initially with a *complex*, first-order partial fraction
expansion, and use least squares to determine complex gain
coefficients for first-order (complex) terms of the form

Sections implementing two real poles are not conveniently specified in terms of resonant frequency and bandwidth. In such cases, it is more common to work with the filter coefficients and directly, as they are often computed by system-identification software [288,428] or general purpose filter design utilities, such as in Matlab or Octave [443].

To summarize, numerous techniques exist for estimating modal
parameters from measured frequency-response data, and some of these
are discussed in §8.8 below. Specifically, resonant modes can be
estimated from the magnitude, phase, width, and location of peaks in
the Fourier transform of a recorded (or estimated) impulse response
Another well known technique is Prony's method [449, p.
393], [297]. There is also a large literature on other
methods for estimating the parameters of exponentially decaying
sinusoids; examples include the *matrix-pencil method*
[274], and *parametric spectrum analysis* in general
[237].

#### State Space Approach to Modal Expansions

The preceding discussion of modal synthesis was based primarily on
fitting a sum of biquads to measured frequency-response peaks. A more
general way of arriving at a modal representation is to first form a
*state space model* of the system [449], and then convert
to the modal representation by *diagonalizing* the state-space
model. This approach has the advantage of preserving system behavior
between the given inputs and outputs. Specifically, the similarity
transform used to diagonalize the system provides new input and output
gain vectors which properly excite and observe the system modes
precisely as in the original system. This procedure is especially
more convenient than the transfer-function based approach above when
there are multiple inputs and outputs. For some mathematical details,
see [449]^{9.7}For a related worked example, see §C.17.6.

#### Delay Loop Expansion

When a subset of the resonating modes are nearly harmonically tuned,
it can be much more computationally efficient to use a *filtered
delay loop* (see §2.6.5) to generate an entire
*quasi-harmonic series of modes* rather than using a biquad for each
modal peak [439]. In this case, the resonator
model becomes

*distributed*1D propagation medium such as a vibrating string or acoustic tube. More abstractly, a superposition of such quasi-harmonic mode series can provide a computationally efficient

*psychoacoustic equivalent approximation*to arbitrary collections of modes in the range of human hearing.

Note that when is close to instead of , primarily only odd harmonic resonances are produced, as has been used in modeling the clarinet [431].

**Next Section:**

Frequency-Response Matching Using Digital Filter Design Methods

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Pole Mapping with Optimal Zeros