Boundary Modelling


The ideal boundary model would allow complete control over the frequency- and direction-dependent absorption and scattering of a surface. Though this is reasonably straightforward to implement in geometric models, it is far from a solved problem for the digital waveguide mesh (DWM). Several possible implementations are discussed in the literature, each with unique drawbacks.

This chapter relies on terms defined in the Theory section. It begins by discussing the ideal behaviour of modelled acoustic boundaries. Then, the implementation for geometric models is discussed. Possibilities for DWM boundary models are investigated, and the final choice of method explained. The geometric and DWM implementations are evaluated and compared, to ensure equivalence.

Geometric Implementation

In Wayverb, surfaces may have different absorptions in each frequency band. Each ray starts with the same pressure in each band. During a specular reflection, the per-band absorptions are converted into per-band reflection factors. Then, the pressure in each band is adjusted using that band’s reflection coefficient. This is similar to the approach taken in graphical ray tracing, in which each ray carries separate red, green, and blue components. These components are modified independently, depending on the colour of the reflective surface.

By definition, image-source models find only specular reflections (i.e. image sources), so scattering is not implemented in these models. Scattering can be implemented in ray tracers, but there is no consensus on the optimum method. One option is to spawn two rays at every reflection: a specular ray, and a diffuse ray with random direction. Though this properly replicates the theory, it leads to an explosion in the number of rays which must be traced, so is impractical in most cases. A second option is to decide, using the scattering coefficient as a probability, whether the reflection should be specular or diffuse [1]. This solves the ray-explosion problem, but requires an additional random number to be generated per-reflection, which can be costly for large numbers of rays. An elegant solution is to simply mix the specular and diffuse rays together, using the scattering coefficient as a weighting [2], a technique known as vector based scattering [3]. This is the approach taken by Wayverb. A major drawback of all these scattering methods is that the scattering coefficient can only be frequency-dependent if a separate ray is traced for each band. If a single ray is used to carry all frequency components, then each component must be scattered in exactly the same way.

The plain scattering model affects only the ongoing ray direction and amplitude. However, it is worth considering that, at each reflection, the scattered energy may be directly visible to the receiver. This fact is exploited by the diffuse rain technique [4, pp. 61–66], in which each reflection is considered to spawn a “secondary source” which emits scattered energy towards the receiver. This scattered energy is recorded only if the secondary source is visible from the receiver.

The magnitude of scattered energy is proportional to the scattering coefficient, and the Lambert diffusion coefficient. It is also proportional to the fraction of the available hemispherical output area which is covered by the receiver volume. The absolute area covered by a spherical receiver \(A_{\text{intersection}}\) is found using the equation for the surface area of a spherical cap.

\[A_{\text{intersection}} = 2\pi r^2(1-\cos\gamma)\](1)

Then, the detected scattered energy can be derived [4, p. 64]:

\[ \begin{aligned} E_{\text{scattered}} & = E_{\text{incident}} \cdot s(1-\alpha) \cdot 2\cos\theta \cdot \left( \frac{A_{\text{intersection}}}{2\pi r^2} \right) \\ & = E_{\text{incident}} \cdot s(1-\alpha) \cdot 2\cos\theta \cdot \left( \frac{2\pi r^2(1-\cos\gamma)}{2\pi r^2} \right) \\ & = E_{\text{incident}} \cdot s(1-\alpha) \cdot 2\cos\theta \cdot (1-\cos\gamma) \end{aligned} \](2)

Here, \(\theta\) is the angle from secondary source to receiver relative against the surface normal, and \(\gamma\) is the opening angle (shown in fig. 1). The magnitude of the scattered energy depends on the direction from the secondary source to the receiver (by Lambert’s cosine law), and also on the solid angle covered by the receiver.

Figure 1: Angles used in the diffuse rain equation for a spherical receiver.
Figure 1: Angles used in the diffuse rain equation for a spherical receiver.

DWM Implementation

Possible Methods

Two methods from the literature were considered for use in Wayverb. A brief overview of each will be given here.

KW-Pipe Technique

This method is described in [5] and [6].

As described in the Digital Waveguide Mesh section, there are two technically-equivalent formulations of digital waveguides meshes, known as W-models and K-models. W-models allow for straightforward interaction with a variety of termination types, such as wave digital filters, which can be used to model frequency-dependent boundaries and air absorption. However, W-models use more than twice the memory of the equivalent K-model [7]. For large-scale simulations, K-models are preferable for their reduced memory usage. However, K-models cannot interact directly with wave digital filters.

The KW-pipe is a “converter” between wave- and Kirchhoff- variables, which is designed to allow the majority of a model (that is, the air-filled space inside it) to be constructed as a K-model waveguide mesh. At the boundaries of the model, the KW-pipe is used to connect K-model nodes to W-model nodes. These W-model nodes can then be connected to wave digital filters to simulate frequency-dependent absorption of wave energy. The complete model retains both the memory-efficiency of the K-model and the termination flexibility of the W-model, with the drawback of additional implementation complexity at the interface between the two model types.

This sounds extremely promising, but has a major drawback, as described by Kowalczyk and Van Walstijn [8]: while the inside of the mesh will be 2- or 3-dimensional, the boundary termination afforded by the wave-variable boundary is 1-dimensional. Each boundary node connects to just the closest interior node. As a result, the edges and corners are not considered to be part of the model, as these nodes do not have a directly adjacent interior node. Additionally, the 1D boundary termination equation implies a smaller inter-nodal distance than that of the 2D or 3D mesh interior. This means that when updating an interior node next to a boundary, the inter-nodal distance is greater than when updating the boundary node itself. For these reasons, the 1D termination is unphysical and can lead to large errors in the phase and amplitude of reflections [8].

Locally Reactive Surfaces Technique

This method, described in [8], aims to create physically correct higher-dimensional boundaries by combining a boundary condition, defined by a boundary impedance, with the multidimensional wave equation. This leads to a model for a locally reacting surface (LRS), in which boundary impedance is represented by an infinite-impulse-response (IIR) filter.

As noted above, a surface is locally reacting if the normal component of the particle velocity on the boundary surface is dependent solely upon the sound pressure in front of the boundary. In most physical surfaces, the velocity at the surface boundary will also be influenced by the velocity at adjacent points on the boundary, so LRS is not a realistic physical model in the vast majority of cases.

However, despite that it is not a realistic physical model, the implementation of the LRS modelling technique is both stable and accurate, as opposed to the 1D KW-pipe termination, which does not accurately model even locally-reacting surfaces.

The LRS model leads to an implementation that is efficient (as it is based completely on the K-model/FDTD formulation) and tunable (boundaries are defined by arbitrary IIR filters).

Choice of Boundary Technique for the DWM

The LRS technique was chosen, as it represented the best compromise between memory efficiency, customization and tuning, and physically-based behaviour (i.e. edges and corners are considered as well as flat surfaces). The particular strengths of this model are its performance and tunability, though as mentioned previously it is not physically accurate in many cases. That being said, neither of the boundary models considered are particularly realistic, so even for applications where physical modelling is the most important consideration, the LRS model seems to be the most appropriate.

LRS Implementation

See [8] and [9] for a more detailed explanation.

The reflectance of a LRS has been defined earlier, in terms of the normal-incidence specific impedance \(\xi_0\) (+???). For the geometric implementation, \(\xi_0\) was defined in terms of a single normal-incidence reflection coefficient \(R_0\) (+???). If \(R_0\) is replaced by a digital filter \(R_0(z)\), then the specific impedance may also be expressed as a filter \(\xi_0(z)\):


To create the filter \(R_0\), per-band normal reflection magnitudes are found using the relationship \(|R|=\sqrt{1-\alpha}\). Then, the Yule-Walker method is used to find infinite impulse response (IIR) coefficients for a filter with an approximately-matched frequency response. Then, this filter is substituted into eq. 3 to find IIR coefficients for the specific impedance filter. This impedance filter will eventually be “embedded” into the boundary nodes of the waveguide.

Surfaces with detailed frequency responses will require high-order filters. This generally leads to numerical instability in IIR filters. The usual solution to this problem would be to split the high-order filter into a series-combination of lower-order filters. However, the LRS requires access to intermediate values from the filter delay-line which makes this approach impossible. An alternative solution is suggested in [10], which suggests running the entire simulation multiple times, once for each octave band. This means that the boundary filters can be single-order, and resistant to accumulated numerical error. Compared to high-order boundary filters, this method gives much improved accuracy, but at the cost of running the entire simulation multiple times. In Wayverb, both approaches are possible, allowing the user to choose between a fast, inaccurate single-run simulation with high-order filters; or a slow, accurate multi-run simulation with low-order filters.

To implement the waveguide boundaries, the computed impedance filter coefficients are inserted into three special update equations, which are found by combining the discrete 3D wave equation with the discrete LRS boundary condition. These equations are used instead of the standard update equation when updating the boundary nodes. The exact update equations have not been reproduced here due to space constraints, but they can be found in [8], alongside a thorough derivation and explanation. The implementation in Wayverb does not make any modifications to these equations.

The three different update equations are chosen depending on the placement of the boundary nodes. In the case of a flat wall, the boundary node is adjacent to a single inner-node, and a “1D” update equation is used. Where two perpendicular walls meet, the nodes along the edge will each be adjacent to two “1D” nodes, and a “2D” update equation is used for these nodes. Where three walls meet, the corner node will be directly adjacent to three “2D” nodes, and a “3D” update equation is used for this node. The three types of boundary nodes are shown in fig. 2. Note that this method is only capable of modelling mesh-aligned surfaces. Other sloping or curved surfaces must be approximated as a group of narrow mesh-aligned surfaces separated by “steps”. For example, a wall tilted at 45 degrees to the mesh axes will be approximated as a staircase-like series of “2D” edge nodes.

Figure 2: The three types of boundary nodes, used to model reflective planes, edges, and corners. 1D nodes are adjacent to inner nodes, 2D nodes are adjacent to two 1D nodes, and 3D nodes are adjacent to three 2D nodes.
Figure 2: The three types of boundary nodes, used to model reflective planes, edges, and corners. 1D nodes are adjacent to inner nodes, 2D nodes are adjacent to two 1D nodes, and 3D nodes are adjacent to three 2D nodes.

Testing of the LRS Boundary for the DWM

The LRS waveguide boundary is complex to implement, as it embeds IIR filters into the waveguide boundaries, so it is worth ensuring that the boundary nodes behave as expected.

Although the 3D boundary equations are presented in [8], only 2D boundaries are tested. Therefore the test shown in this thesis is a novel contribution, as no previous empirical evidence exists for the 3D LRS boundary implementation in the waveguide mesh. The test used here is an extension of the test procedure presented in [8], but extended to three dimensions.


A mesh with dimensions \(300 \times 300 \times 300\) nodes, and a sampling frequency of 8kHz, was set up. A source and receiver were placed at a distance of 37 node-spacings from the centre of one wall. The source position was dictated by an azimuth and elevation relative to the centre of the wall, with the receiver placed directly in the specular reflection path. The simulation was run for 420 steps. The first output, \(r_f\), contained a direct and a reflected response. Then, the room was doubled in size along the plane of the wall being tested. The simulation was run again, recording just the direct response at the receiver (\(r_d\)). Finally, the receiver position was reflected in the boundary under test, creating a receiver image, and the simulation was run once more, producing a free-field response at the image position (\(r_i\)). Figure 3 shows the testing setup.

When testing on-axis reflections (where azimuth and elevation are both 0), the position of the receiver will exactly coincide with the position of the source. If a hard source is used, the recorded pressures at the receiver (\(r_f\) and \(r_d\)) will always exactly match the input signal, and will be unaffected by reflections from the boundary under test. It is imperative that the signal at the receiver contains the reflected response, so a hard source is not viable for this test. Instead, a transparent dirac source is used, which allows the receiver node to respond to the reflected pressure wave, even when the source and receiver positions match. The main drawback of the transparent source, solution growth, is not a concern here as the simulations are only run for 420 steps. The tests in the Digital Waveguide Mesh section showed that solution growth generally only becomes evident after several thousand steps.

Figure 3: The setup of the two room-sizes, and the positions of sources and receivers inside.
Figure 3: The setup of the two room-sizes, and the positions of sources and receivers inside.

The reflected response was isolated by subtracting \(r_d\) from \(r_f\), cancelling out the direct response. This isolated reflection is \(r_r\). To find the effect of the frequency-dependent boundary, the frequency content of the reflected response was compared to the free-field response \(r_i\). This was achieved by windowing \(r_r\) and \(r_i\) with the right half of a Hann window, then taking FFTs of each. The experimentally determined numerical reflectance was determined by dividing the magnitude values of the two FFTs.

To find the accuracy of the boundary model, the numerical reflectance was compared to the theoretical reflection of the digital impedance filter being tested, which is defined as:

\[R_{\theta, \phi}(z) = \frac{\xi(z)\cos\theta\cos\phi - 1}{\xi(z)\cos\theta\cos\phi + 1}\](4)

where \(\theta\) and \(\phi\) are the reflection azimuth and elevation respectively.

The test was run for three different angles of incidence, with matched azimuth and elevation angles of 0, 30, and 60 degrees respectively. Three different sets of surface absorption coefficients were used, giving a total of nine combinations of source position and absorption coefficients. The specific absorption coefficients are those suggested in [10], shown in table 1.

Table 1: Absorption coefficients of different materials at different frequencies, taken from [10].
band centre frequency / Hz 31 73 173 411 974
plaster 0.08 0.08 0.2 0.5 0.4
wood 0.15 0.15 0.11 0.1 0.07
concrete 0.02 0.02 0.03 0.03 0.03

The boundary filter for each material was generated by converting the absorption coefficients to per-band reflectance coefficients using the relationship \(R=\sqrt{1-\alpha}\). Then, the Yule-Walker method from the ITPP library [11] was used to calculate coefficients for a sixth-order IIR filter which approximated the per-band reflectance. This filter was converted to an impedance filter by \(\xi(z)=\frac{1+R_0(z)}{1-R_0(z)}\), which was then used in the boundary update equations for the DWM.


The results are shown in fig. 4. The lines labelled “measured” show the measured boundary reflectance, and the lines labelled “predicted” show the theoretical reflectance, obtained by substituting the impedance filter coefficients and angles of incidence into eq. 4. Although the waveguide mesh has a theoretical upper frequency limit of 0.25 of the mesh sampling rate, the 3D FDTD scheme has a cutoff frequency of 0.196 of the mesh sampling rate for axial directions. This point has been marked as a vertical line on the result graphs.

Figure 4: Measured boundary reflectance is compared against the predicted reflectance, for three different materials and three different angles of incidence.
Figure 4: Measured boundary reflectance is compared against the predicted reflectance, for three different materials and three different angles of incidence.


The most concerning aspect of the results is the erratic high-frequency behaviour. Even though the cutoff of the 3D FDTD scheme is at 0.196 of the mesh sampling rate, deviations from the predictions are seen below this cutoff in all the presented results. The cause of this error is unclear. One possibility is numerical dispersion, which is known to become more pronounced as frequency increases. However, the rapid onset of error around the cutoff suggests that the cause is not dispersion alone. Another possible explanation might be extra unwanted reflections in the outputs, although this seems unlikely. The use of a transparent source means that the source node does not act as a reflector, as would be the case with a hard source. In addition, the dimensions of the test domain were chosen to ensure that only reflections from the surface under test are present in the output. The recordings are truncated before reflections from other surfaces or edges are able to reach the receivers. Finally, it seems likely that such interference would affect the entire spectrum, rather than just the area around the cutoff. A final, more likely, explanation for the volatile high-frequency behaviour is that the filters used in this test are of much higher order than those tested in [8], leading to accumulated numerical error.

Whatever the cause of the poor performance at the top of the spectrum, the implications for Wayverb are minor, as the waveguide mesh is designed to generate low-frequency content. If wideband results are required, then the mesh can simply be oversampled. To prevent boundary modelling error affecting the results of impulse response synthesis in the Wayverb app, the mesh cutoff frequency is locked to a maximum of 0.15 of the mesh sampling rate.

Some of the results (especially concrete and wood at 60 degrees) show minor artefacts below 0.01 of the mesh sampling rate, where the measured responses diverge from the predictions. Kowalczyk and Van Walstijn note that some of their results display similar low-frequency artefacts, and suggest that the cause is that the simulated wave-front is not perfectly flat. However, flat wave-fronts are not easily accomplished. The experiments in [8] use large meshes (around 3000 by 3000 nodes, nine million in total) and place the sources a great distance away from the boundary being studied in order to maintain a mostly-flat wave-front. This is only practical because the experiments are run in two dimensions. For the 3D case, no experimental results are given. This is probably because running a 3D simulation on a similar scale would require a mesh of twenty-seven billion nodes, which in turn would require gigabytes of memory and hours of simulation time.

In conclusion, for the most part, the results presented adhere closely to the expected results, with the caveat that the surface reflectance is only accurate at low frequencies, below around 0.15 of the mesh sampling rate. Different absorption coefficients lead to clearly-different reflectance coefficients, which are additionally accurate at multiple angles of incidence. Whilst a more accurate method would be preferable, this model is both fast and tunable, making it a good candidate for boundary modelling in room acoustics simulations.


In the image source model, outgoing pressure from a boundary can be found as a function of incident angle and surface attenuation; scattering cannot be modelled. The ray tracer, however, is able to model scattering in two ways. Firstly, the average scattering coefficient across all frequency bands is used to govern the magnitude of random offset of the outgoing ray direction. Secondly, the diffuse rain technique is used to model frequency-dependent scattering.

Boundary models in the waveguide are more involved. Wayverb uses the LRS technique, as it is a better model of physical behaviour than the alternative KW-pipe technique. The LRS model uses special update equations, which contain embedded IIR filters, to calculate pressure values at designated boundary nodes. This is effective for boundaries which are aligned to the mesh, but for curved or angled surfaces the boundary will be quantised into steps. The effect of this quantisation has not been investigated.

The waveguide boundary implementation has been tested to see whether the measured reflectance matches the theoretical surface reflectance. In all tests, the match is reasonably close around the middle of the valid spectrum, between 0.01 and 0.1 of the mesh sampling rate. Outside this range, the results tend to deviate somewhat. As a result, the model crossover frequency in Wayverb has a maximum of 0.15 of the waveguide sampling frequency, and it is recommended to oversample the mesh if highly accurate boundary results are required. It is hoped that the low frequency artefacts will always occur below the audible range, although it is unclear whether this is really the case as tests have only been conducted at a single sampling frequency. Only 1D boundaries have been tested, as it is unclear how to test 2D and 3D boundaries in isolation. Given that all boundary types (1D, 2D and 3D) have the same derivation, it may be enough to assume that if the 1D boundaries function correctly, then the 2D and 3D boundaries will work too. However, this does not rule out the possibility of implementation mistakes in the higher-order boundaries.

Finally, the boundary model has not been tested in the context of an extended waveguide simulation. It would be expected that the absorption of surfaces in a room would have an effect on the overall reverb time in that room. This relationship has not been shown to be true of Wayverb’s waveguide. A particularly interesting test would be to set different absorption coefficients in each of the waveguide frequency bands, to estimate Sabine reverb times in each of those bands, and to see whether the measured reverb time in each band matches the predictions. Such a test was not possible due to time constraints, but would help to clarify some of the results shown in the Evaluation.


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