In the preceding sections, simulation results have been recorded using a virtual omnidirectional microphone model. This model records the sound pressure at a single point in space. The gain and frequency response of the receiver are not affected by the direction of the pressure gradient. Signals recorded with omnidirectional microphones are primarily useful for assessing the overall “character” of an acoustic space, including reverb time, intelligibility, and overall gain.

However, one of the goals of room acoustics simulation is *virtual acoustics*: recreating the modelled auditory environment in such a way that the listener believes they are hearing sounds within a physical space. Real reverbs are highly directional, containing direct sound from the source, and reflected sounds from the room boundaries. To produce a convincing virtual environment, these directional cues must be encoded, by adjusting the output gain and frequency response depending on the direction of the cue. This is achieved using a direction-sensitive receiver model.

Humans are able to detect the originating direction of sounds using two main techniques: Interaural Time Difference (ITD) defines the time delay between ears, when the sound source is located nearer to one ear than the other; and Interaural Level Difference (ILD) denotes the difference in sound level (which may vary by frequency) caused by attenuation by the outer ear, head, and torso. The suitability of impulse responses for stereo or multichannel playback depends on the ability to produce signals with appropriate ITD and ILD, in such a way that each reflection is perceived as coming from a certain direction.

When recording impulse responses of physical spaces, several techniques might be used to retain ITD and ILD information.

One option is to record the impulse response using a matched pair of microphones. An AB pair of spaced omnidirectional capsules will capture interchannel time difference, but will only capture interchannel level difference if the source is positioned near to the microphones. Alternatively, an XY or Blumlein pair (which consist of coincident cardioid or bidirectional capsules respectively) will capture level difference, but will be incapable of recording time difference because wave-fronts will always arrive at both capsules simultaneously.

Microphone pair methods are only suitable for recording stereo signals, as they only capture a two-dimensional “slice” through the modelled scene, where all directional information is restricted to the same plane as the microphone capsules. The technique can be extended to higher dimensions by using more microphone capsules. This is the basis of the ambisonic approach, which for B-format recordings uses four coincident microphone capsules, three bidirectional and one omnidirectional, to capture the three-dimensional directional pressure gradient and overall pressure level. Instead of being used directly, the recorded signals are post-processed depending on the configuration of the output speakers. For playback on headphones, the signals can be filtered with *head related transfer functions* (HRTFs), which modify the frequency content of the sound depending on its originating direction, mimicking the absorptive characteristics of the human head and torso [1].

If it is known that the recording will only be reproduced on headphones, the preferred method for capturing impulse responses which retain appropriate ILD and ITD is to use in-ear microphones, or a “dummy head” microphone pair. Recordings made in this way capture the ITD and ILD that are caused by sonic interactions with the outer ear, head, and torso, and produce a convincing reconstruction of the sonic space when played back over headphones. However, responses recorded using this technique are unsuitable for loudspeaker playback.

With the exception of the spaced-pair methods, all of these techniques require that the receiver gain is dependent upon the incident direction. The dummy head technique additionally requires that the frequency response of the receiver is dependent upon the incident direction. To allow reproduction on both headphones and arbitrary loudspeaker arrays, the receiver model in Wayverb should encompass all of the techniques described above. Given that spaced microphone techniques can be modelled simply by including multiple receivers in the simulation, each virtual capsule should have configurable direction-dependent gain and frequency response, allowing it to behave like a standard microphone, or like the ear of a dummy head.

Wayverb contains two different models for calculating an appropriate gain from an incident direction, described here.

The first method, the “perfect microphone”, is very simple. Given that the virtual microphone is pointing in direction \(\hat{p}\), and has a “shape” parameter \(0 \leq s \leq 1\), the direction-dependent attenuation \(a(\hat{d})\) from direction \(\hat{d}\) is given by

\[a(\hat{d}) = (1 - s) + s(\hat{d} \cdot \hat{p})\](1)

where \(\hat{p}\) and \(\hat{d}\) are unit vectors, and \(\cdot\) is the dot-product operator. When \(s\) is set to 0, the \(a(\hat{d})\) is equal to one for all values of \(\hat{d}\), modelling an omnidirectional polar pattern. When \(s\) is 1, the modelled polar pattern is bidirectional, with a gain of 1 in front of the microphone, 0 at either side, and -1 behind. When \(s\) is 0.5, the pattern is cardioid. Sub- and supercardioid patterns are created by setting \(s\) lower or higher than 0.5 respectively. This microphone model is flexible, allowing almost all standard microphone techniques (including the XY pair, Blumlein pair, and B-format ambisonic) to be simulated.

Note that this model has an ideally flat frequency response in every direction, so it can only be used to model perfect microphones. It is not suitable for modelling physical microphones with direction-dependent frequency responses. In particular, with this technique it is not possible to produce binaural dummy-head-style recordings, for immersive headphone playback.

A more general microphone model would allow specific per-frequency-band gains to be set for each incoming direction. Such a model is implemented in Wayverb, but is used only for the modelling of HRTF characteristics. In the future, the approach could easily be extended to simulate general non-flat microphones.

The method itself is based on a two-dimensional lookup table, indexed by the azimuth and elevation angles formed by the incident direction. The table is produced using experimentally-obtained HRTF data from the IRCAM Listen database [2]. The data takes the form of a collection of stereo audio files, containing the impulse responses measured using microphones placed in the left and right ears of a human volunteer. The recordings are made in an anechoic environment. Each file contains the response measured for an impulse originating at a specific azimuth and elevation.

To prepare the HRTF database for use in Wayverb, the audio files are multi-band-filtered, using the same 8-band frequencies used throughout Wayverb. Then, the per-band per-channel energy is recorded to a lookup table.

To find the receiver frequency response for a given incident direction, the direction vector is converted to an azimuth and an elevation. Then, this azimuth and elevation are rounded to the closest angles which exist in the lookup table. They are used to index the lookup table, and the energies at that index are returned.

This model contains two approximations: the first is the directional discretisation process, which “quantises” incoming angles to a set of discrete, equally-spaced bins. In the final implementation, fifteen-degree increments are used, matching the directional resolution of the experimentally-obtained data. However, humans can localise sounds from frontal sources to an accuracy of one degree [3, p. 22], so higher directional resolution may be required to produce a convincing binaural effect. This resolution is of course limited by the directional resolution of the input data, and HRTF data with one-degree resolution would be time-consuming to measure, and expensive to store.

The second approximation is in the frequency resolution of the lookup table. HRTF data is supplied as a set of finite-impulse-response filter kernels, which represent continuous frequency spectra, up to the sampling frequency of the kernels. Generally, these filter kernels would be used via convolution, resulting in a precise adjustment of the frequency content of the filtered signal. If this approach were to be taken in Wayverb, it would require creating a separate output signal for each of the incident directions, and then convolving each direction separately, before a final mixdown. This would be extremely costly: for separation angles of fifteen degrees, this means recording signals for each of 288 incident directions (24 azimuth angles, each with 12 elevation angles), which might easily occupy more than a gigabyte of memory (288 3-second impulse responses at 32-bits and 44.1kHz require 1160MB of memory), and then separately convolving and mixing them. While this is definitely possible, it would be extremely time consuming to run. By restricting the HRTF data to 8 frequency bands, the application architecture can be simplified, while also improving memory usage and runtime performance.

As described in the Image Source section, the magnitude of the contribution of each image source depends on the distance between the source and receiver, and the direction-dependent reflectance characteristics of any intermediate reflective surfaces. To model a directional receiver, the contribution magnitude is additionally scaled by the receiver’s gain in the direction of that contribution.

The direction of each image source is found by subtracting the receiver position from the image source position, and normalising the result. This direction can either be used to directly compute the appropriate attenuation factor, or it can be used to look up the attenuation in a pre-computed table (see Calculating Directional Gain below).

In the case that the frequency response (rather than overall gain) of the receiver depends on incident direction, a different attenuation function or look-up table is used for each frequency band. At the end of the simulation, the outputs in each band are band-pass filtered and mixed down.

Wayverb includes a fast estimator for ITD in the image source model, which has not been encountered in the literature. During the mix-down process for the image-source contributions, the time of each contribution is calculated based on the distance from the image source to the receiver. When calculating these distances for the left ear, the receiver position can be temporarily moved 10cm to the left, relative to the receiver orientation, and the same can be done for the right ear. The output signal will then incorporate small time delays between channels, depending on the distance and direction of each image source. This method relies on the assumption that all image sources are visible from both ear positions, and that the new ear positions do not affect the angle-dependent reflection characteristics of intermediate surfaces. This is a reasonable assumption, given that the distance between ears is generally small compared to the size of the modelled room. The benefit of this method is that the costly image-source-finding process only has to be run once in total, instead of once for each ear.

This image-source “offsetting” technique cannot be applied to Wayverb’s other simulation methods. In the waveguide, the new “ear” positions may not fall directly on grid nodes, and so the waveguide simulation would have to be run twice, with different grids. The raytracer would not benefit from interaural time difference at all, as the maximum possible interaural time delay is the interaural distance divided by the speed of sound, 0.2/340=0.0005s, which is less than the 1ms energy histogram sampling interval. Therefore, any measured ITD would be lost when quantising to the histogram interval.

The drawback of the offsetting method is that, if the user manually positions two receivers with appropriate interaural spacing, an extra offset will still be automatically applied. This will lead to an interaural spacing that is twice as wide as desired. This could be addressed by including an option in the software to enable or disable the additional offset. Due to time constraints this feature has not been implemented in the current version of Wayverb.

In the ray tracer, a very similar approach could be taken. The incident direction of each ray is known, so this direction might be used with a function or lookup-table to adjust the ray’s multiband intensity before it is added to the energy histogram (see Ray Tracer).

In terms of implementation, this approach would be needlessly expensive. In Wayverb, the initial simulation and directional processing stages are kept separate, allowing a single simulation to be processed by several different virtual microphone capsules. While flexible, it is also infeasible to store the direction of each incident ray, especially if there are hundreds of thousands of rays in the simulation. The ray tracer is used to estimate the late reverberation of the space, in which individual sonic events are “blurred” together, and in which there are few directional cues [3, p. 21], so this level of per-ray directional accuracy is unnecessary.

Instead, the surface of the spherical receiver is divided into discrete *directivity groups*, mirroring the approach of Schroder in [3, p. 72]. A separate multiband energy histogram is maintained for each directivity group, where the direction of ray incidence determines the histogram used to store that ray’s energy. These histograms are at a substantially lower sampling-frequency than the output sampling rate, so it is feasible to store one for each discrete incident direction, as long as the direction quantisation intervals are on the order of ten to twenty degrees. To generate audio-rate results, the directional histograms must be combined into a single energy histogram, which can then be used to weight a noise sequence as discussed in the Ray Tracer section. This is achieved by taking the centre direction of each directivity group, calculating or looking-up the correct attenuation for that direction, and then multiplying the entire histogram by that value. The final direction-weighted histogram is given by the elementwise sum of all individual weighted histograms.

Implementing directional receivers in the waveguide mesh is more complicated than for the geometric methods. Although absolute pressure values are available in the mesh, directional pressure values are not inherently supported. In order to simulate a directional receiver, the instantaneous direction of the sound field must be found at the receiver point. Several methods were considered, as outlined below.

The first method, described in [4] and [5], is designed with the goal of encoding the 3D sound-field by means of spherical harmonic decomposition to the second order, for use in ambisonic reproduction.

This method is based on sound intensity probe theory, which allows directional velocity components of a sound field to be captured using two closely-spaced omnidirectional receivers. In the DWM, each node has an associated pressure, so two closely-spaced nodes can be used to emulate a pair of omnidirectional receivers, and in turn used to generate directional velocity components. A full first-order B-format signal can be made using seven such “pressure sensors”, arranged in a cross shape, with a single node at the centre, and six nodes placed around it. The pressure field velocity components are found by subtracting nodes on “opposite” sides of the cross, while the centre node contributes the overall pressure at the receiver. A second-order output is achieved by adding further pressure-sensing nodes in between the first-order nodes.

Note that this technique does not allow the immediate production of directional responses. Instead, it is designed to produce outputs in ambisonic format, which are then post-processed to create a signal with the desired modelled directional response.

Additionally, this technique has some difficulty adapting to meshes with large grid spacings. In a rectilinear mesh, the seven first-order nodes can be placed exactly, as their positions will coincide with nodes in the mesh. However, the second-order nodes will not fall exactly on existing nodes, and instead their positions will need to be quantised to the closest mesh nodes. This problem becomes worse in other mesh topologies, such as the tetrahedral mesh, where it may be that very few of the sensor nodes can be exactly placed in the desired positions.

To facilitate more exact placement of sensor nodes, the spatial sampling frequency of the mesh must be increased (by reducing the grid spacing, the quantisation error in the sensor placement is also reduced). The example given in [5] uses a sensor spacing of 0.04 metres, giving a maximum valid sampling frequency of 4.25kHz. However, to allow accurate placement of these sensors, a grid with a spacing of 0.0027m is required. This increase in mesh density necessitates more than 3000 times the original number of nodes, which is only practical for very small modelled spaces. An alternative to oversampling is interpolation, which may be used to obtain signal values for positions that do not coincide with grid nodes, as in [6]. However, the interpolated approach given in this paper is only applicable in the 2D case, so it cannot be applied in Wayverb, which requires a general solution in three dimensions.

Finally, this method places an additional constraint on the portion of the spectrum of the output signal which may be considered valid. The maximum valid frequency of the output spectrum is determined by the ratio between the receiver spacing and the wavelength at that frequency. To raise the upper frequency limit, the receiver spacings must be made as small as possible, which requires costly mesh oversampling. Small receiver spacings reduce the sensitivity at the lower end of the spectrum (even when interpolation is used), so ideally the simulation must use several sets of receiver nodes with different spacings, in order to maintain a flat frequency response.

This technique seems suitable for calculating lower-order ambisonic signals in small modelled spaces. For higher order outputs, with consistent frequency responses across the spectrum, the technique quickly becomes too expensive (due to mesh oversampling) and complex to implement (as several receiver spacings and configurations are required for a single simulation output).

The second method considered for implementation in Wayverb is described by Hacıhabiboğlu in [7]. This method is based around estimating the acoustic intensity at the output node of the DWM.

The technique is much more straight-forward than the technique discussed above. The pressure differences between the “output” node and its surrounding nodes are calculated. These pressures are transformed depending on the relative positions of each surrounding node, and combined to produce a three-element vector containing an approximation for the three-dimensional pressure gradient. The equations describing these operations in the general case are not replicated here, however they may be greatly simplified in the rectilinear case, as will be shown in the Implementation section below. The velocity for each time step is found by adding the pressure gradient vector to the velocity at the previous time step, where the initial velocity is assumed to be zero. Intensity is found by multiplying this velocity by the pressure at the output node.

In some respects, this technique is very similar to the Blumlein difference technique. Both techniques calculate the instantaneous pressure gradient at the output node by subtracting the pressures of the surrounding nodes. The main difference is that the Blumlein method requires that the mesh pressure is found at exact locations relative to the output node, which often requires mesh oversampling or interpolation. The intensity calculation technique instead uses a more general matrix-based method, which incorporates the relative node positions into the calculation. In short, the Blumlein method requires that the mesh is designed to suit the receiver configuration, whereas the intensity calculation method can adapt to arbitrary mesh topologies and spacings.

The frequency range issues mentioned in [4] (where large mesh spacings reduce the output bandwidth, and low mesh spacings reduce low frequency sensitivity) are mentioned by Hacıhabiboğlu in [7]. It is unclear whether the intensity calculation technique solves this problem, although the results presented in [7] appear consistent across the spectrum. Some inaccuracy is evident at the top of the spectrum, but this is attributed to direction-dependent dispersion error, rather than to a problem with the frequency response of the model itself.

Under this scheme, the output of the waveguide mesh is actually two streams of information: a stream of scalar values describing the pressure at the output node; and a stream of 3D vectors denoting the directional intensity at that point.

To find the directional response for a single time-step, the intensity vector is used to look up the microphone response in the direction of the vector, which is a scalar value. The microphone response is squared, and multiplied by the magnitude of the intensity vector, yielding a positive scalar output. Finally, the sign of the actual pressure at the output node is copied to the output value. To find the full directional response, this process is repeated for every time-step of the simulation.

The intensity calculation technique can also model receivers with direction-dependent frequency responses. The intensity and pressure outputs are processed with a set of polar patterns corresponding to responses at different frequency bands. Then, these processed signals are band-passed with linear-phase filters, and summed together.

The intensity calculation technique was chosen for implementation in Wayverb, as it is simpler to implement, and less expensive to run. Also, the Blumlein difference technique is only practical in rectilinear meshes, whereas the intensity calculation technique can be adapted to any mesh topology. This was an important consideration at the beginning of the Wayverb project, which originally used the tetrahedral mesh due to its relative efficiency.

The implementation is very simple in the rectilinear mesh. The mesh is inherently aligned to the axial directions, which means that the matrix maths is greatly simplified. The process for finding the instantaneous intensity vector for each step of the waveguide simulation is shown in pseudocode below.

```
def compute_intensities_at_node(node_position,
mesh_spacing,
mesh_sample_rate,
ambient_density):
velocity_vector = (0, 0, 0)
pressures = []
intensities = []
(nx_position, px_position,
ny_position, py_position,
nz_position, pz_position) = compute_neighbouring_positions(node_position)
for step in waveguide:
// Find current pressure at output node.
p = get_node_pressure(node_position)
// Calculate the pressure differences between surrounding nodes.
nx_difference = (get_node_pressure(nx_position) - p) / mesh_spacing
px_difference = (get_node_pressure(px_position) - p) / mesh_spacing
ny_difference = (get_node_pressure(ny_position) - p) / mesh_spacing
py_difference = (get_node_pressure(py_position) - p) / mesh_spacing
nz_difference = (get_node_pressure(nz_position) - p) / mesh_spacing
pz_difference = (get_node_pressure(pz_position) - p) / mesh_spacing
// Find the instantaneous pressure gradient.
pressure_gradient_vector = ((px_difference - nx_difference) / 2,
(py_difference - ny_difference) / 2,
(pz_difference - nz_difference) / 2)
// Update the velocity accumulator.
velocity_vector -= (pressure_gradient_vector /
(ambient_density * mesh_sample_rate))
// Find the instantaneous intensity.
intensity_vector = velocity_vector * p
pressures.append(p)
intensities.append(intensity_vector)
return pressures, intensities
```

For each waveguide step, the pressure and intensity at the output node is found and stored. If there are multiple coincident capsules, then the same pressure-intensity data can be post-processed once for each virtual capsule, without needing to re-run the simulation. However, this approach may not be well-justified: according to Hacihabiboglu et al. this microphone model is only suitable for single-diaphragm directional microphones, and is unsuitable for coincident pressure gradient microphones with multiple elements [7]. Despite its unsuitability, there is no other possible way of modelling coincident capsules using this microphone model, so coincident capsules *are* allowed in Wayverb. Development of a microphone model with better support of coincident capsules is a topic for future research.

Having generated the pressures and the intensity vectors at the output node, the post-processing progresses by finding the magnitude of the intensity vector. This value is multiplied by the square of the polar-pattern attenuation in the direction of the intensity vector, and then the square root is taken. Finally, the sign of the pressure is copied to this output value. This process is repeated for each step of the simulation, resulting in a sequence of directionally-processed pressure values.

For binaural results, this process is repeated once for each frequency band, resulting in several differently-weighted broadband signals. The directional gains are found using the same lookup-table technique as the geometric methods, using the azimuth and elevation of the intensity vector as indices. The final output is created by bandpass filtering each of these broadband signals using a zero-phase frequency-domain filter, and then mixing them down. The filtering process is the same as that used throughout Wayverb (see Ray Tracer).

The testing procedure is similar to that described in [7]. A receiver is placed at the centre of a room measuring \(1.5 \times 1.5 \times 1.5\) metres. Sixteen equally-spaced source positions are generated in a circle with radius 1m around the receiver. For each source, the room is simulated using a rectilinear waveguide mesh with a sampling rate of 50kHz, for 294 steps. The step number is chosen so that the initial wave-front will reach the receiver, but reflections from the room walls will not. After each simulation, the directionally-weighted signal recorded at the receiver is split into frequency bands, and the normalised energy per band is calculated. The bands are split in the frequency domain, using the frequency-domain envelope method mentioned in the Ray Tracer section. The normalised energy of the band is calculated by dividing the sum of squared bin magnitudes in the output by the integrated frequency-domain envelope.

The per-band normalised energy can now be plotted against the angle of incidence, and compared to the ideal polar pattern of the modelled microphone. This is achieved by normalising the measured directional response relative to the response in the front direction. If there is a close match, then the model is successful.

Three different microphone polar patterns are simulated: omnidirectional, bidirectional, and cardioid. The results are shown in fig. 1 to 3. In these figures, the red lines show the measured energy in each direction, while the blue lines show the expected polar pattern, normalized so that the 0-degree level matches the experimentally-obtained level in that direction.

Results are consistent across all polar patterns tested. At the lower frequencies shown (100Hz to 1.5kHz), the match is particularly good, with virtually no error in all directions. As frequency increases, the error increases greatly. This happens as a result of frequency-dependent dispersion. At higher frequencies (above 2.7kHz), the speed of wave-fronts changes depending on their direction. In the rectilinear mesh, there is no dispersion along diagonal directions [8]. Along axial directions, however, high-frequency wave-fronts move more slowly. This is likely the cause of the predominant “spikes” seen in the diagonal directions: in these directions, all the acoustic energy reaches the receiver, while along axial directions, the wave-fronts are slower, and the full energy does not reach the receiver within the simulation window.

These results mirror those seen by Hacıhabiboğlu, who also records similar large errors in diagonal directions in the highest (8kHz) band. These errors are also attributed to directional dispersion. Therefore, error in the results is likely due to the properties of the waveguide and the particular receiver technique used, rather than an error in the implementation.

Although there is very large error in some directions at high frequencies, at low frequencies this method produces accurate directional responses. Even at higher frequencies (relative to the mesh sampling rate), the response follows the same overall shape as the desired response, so even here the results may provide a reasonable approximation of the directional room response. Finally, if accurate directional responses are required at higher frequencies, the mesh can be oversampled to reduce directional dispersion. Although costly, it is at least potentially possible to produce very accurate responses using this method.

The need for a virtual microphone model has been explained. Real-world microphone techniques rely on capsules with direction- and frequency-dependent responses, therefore a virtual model is put forward which is capable of simulating these same characteristics.

Directional information is inherent in the geometric models: each ray has a direction, which can be logged and used to attenuate each contribution according to the receiver polar pattern. These models already operate in multiple frequency bands, so to model frequency-dependent responses, the receiver can simply use a different directional response per band. A novel “offsetting” method has been put forward as an extension to the image-source model, which can reconstruct ITD information.

The waveguide does not model directional information natively. Instead, the pressure gradient at the output must be approximated using the pressures at surrounding nodes. The intensity calculation technique is used for this purpose in Wayverb, as it is simpler, faster, and more flexible than the main alternative (the Blumlein difference technique). It has been shown that Wayverb’s implementation is capable of reproducing three different polar patterns with reasonable accuracy across the spectrum, although numerical dispersion causes accuracy to worsen as frequency increases. If receiver accuracy is a major concern, the mesh can be oversampled, reducing dispersion in the output bandwidth.

The test results presented are encouraging but superficial. The test procedure is modelled on that presented in [7], which has the shortcoming that it doesn’t consider interactions with other aspects of the waveguide simulation. In particular, it has not been shown how the microphone model responds to source gain and distance, or whether it affects the measured frequency response at the receiver. These are important considerations which have implications for the validity of the waveguide model as a whole. Due to the number of components in Wayverb, and the time spent on implementation alone, there was not time to test all aspects of the program. However, the Evaluation contains a number of tests, all of which use this microphone model. In the future, more detailed tests should be conducted which focus solely on the microphone model.

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