# ASSESSMENT OF SIMULATIONS IN FAUST AND TASCAR FOR THE DEVELOPMENT OF AUDIO ALGORITHMS IN ACOUSTIC ENVIRONMENTS

Felix Holzmüller

Institute of Electronic Music and Acoustics
University of Music and Performing Arts
Graz, Austria
holzmueller@iem.at

Christian Blöcher

Institute of Electronic Music and Acoustics
University of Music and Performing Arts
Graz, Austria
bloecher@iem.at

Alois Sontacchi

Institute of Electronic Music and Acoustics
University of Music and Performing Arts
Graz, Austria
sontacchi@iem.at

## ABSTRACT

Testing real-time capable audio algorithms in a time-variant acoustic environment can be a tedious process, often requiring specialised hardware to fulfil latency constraints. In this publication, an alternative approach based on an acoustic scene simulation in TASCAR and signal processing using FAUST is assessed and tested against actual measurements. In dry acoustic environments, a good match of level distribution and spatial coherence at various evaluated points can be observed when modelling direct sound and early reflections. A simulation of an ANC algorithm shows also comparable performance to a measurement-based reference. Larger deviations are observed for reverberant and anisotropic scenarios. The proposed toolchain can be used for low-cost development and assessment of real-time capable algorithms in time-variant acoustic scenarios, as all parameters and virtual positions can be updated during operation.

## 1. INTRODUCTION

The development and testing of real-time capable audio algorithms can be a complex undertaking. Some algorithms like active noise control (ANC) are especially time critical and cannot be tested in the real world with common audio hardware and processing on an ordinary computer. Therefore, specialised hardware such as Field Programmable Gate Arrays (FPGAs) or digital signal processors (DSPs) are usually needed, which are often associated with considerable programming effort. Although dedicated rapid prototyping platforms are lowering the entry barrier by supporting higher-level code written in FAUST [1, 2, 3] or MATLAB/Simulink, they are generally costly and not always easily accessible. Another issue of testing systems in reality is a limited repeatability. Even small variations, for example inaccuracies in the geometrical arrangement of acoustic sources and receivers, can change the exact outcome of an experiment. A viable alternative to testing algorithms in the real world can be the combination of a time-variant, real-time capable acoustic simulation environment with an implementation of the algorithm under test in FAUST for a realistic simulation of its behaviour.

TASCAR<fnref n="1" /> [4] is an open-source program for rendering acoustic scenes. Originally developed for hearing aid research, it features time-domain methods like a geometric room-acoustic simulation using an image source model (ISM), an implementation of a simple feedback-delay network (FDN) for diffuse reverberation, and various source and receiver types. A unique feature compared to other simulation tools is the ability to run simulations in real-time

with inputs and outputs provided as JACK Audio<fnref n="2" /> connections. All parameters of objects like position and orientation can be changed via Open Sound Control (OSC)<fnref n="3" /> during operation. This enables researchers to conduct experiments in virtual scenes with variable positions in real-time.

As TASCAR only handles acoustic scene creation, the algorithm under test has to be implemented separately. Here FAUST's ability to compile applications for various architectures comes into play. FAUST has already been used successfully for implementing active noise control algorithms [2, 3]. After an algorithm is implemented in FAUST, it can be compiled as standalone JACK application with the faust2ja[...] tools. This application can receive signals from TASCAR, process them, and send them back to the acoustic scene simulation. Due to JACK's real-time capability, only a small delay equal to the audio device's block size is introduced.

The aim of this publication is to assess the capability of TASCAR and FAUST for testing and evaluation of time-variant audio algorithms compared to a co-simulation, based on recorded signals and measurements. In section 2, the acoustic scene simulation in TASCAR and the performed measurements are described. Section 3 compares acoustic properties of the recorded sound field and the acoustic scene simulation for different rooms and varying level of detail in the simulation. Section 4 showcases the use of TASCAR and FAUST for an ANC algorithm before summarising the findings in section 5. Measurements and code are openly accessible [5].

## 2. MEASUREMENTS AND SIMULATIONS

To assess the validity and accuracy of the simulation, acoustic properties and the ANC algorithm are evaluated for two different rooms:

* An acoustically treated measurement chamber with non-parallel walls, dimensions of approximately 5 m × 4 m × 3 m, and a reverberation time of less than 50 ms above 300 Hz and less than 30 ms above 1 kHz.

* A meeting room with an overall reverberation time of about 0.9 s and anisotropic acoustic properties (surfaces with absorbers, glass, plasterwall, and wooden cabinets).

Figure 1 shows the floor plan of both rooms.

### 2.1. Measurements

In the measurement chamber and meeting room, respectively, an arrangement of eight and four Genelec 8020B loudspeakers was em-

<footnote n="1">¹https://www.tascar.org</footnote>

<footnote n="2">²https://jackaudio.org/</footnote>

<footnote n="3">³https://opensoundcontrol.stanford.edu</footnote>
<figure><img src="image_1.png" /><figcaption>Figure 1: Floor plans of the evaluated rooms with positions of primary loudspeakers (gray), secondary loudspeakers (orange), and the microphone array (black bar) with indicated central position (red cross).</figcaption></figure>

ployed as acoustic source. It was ensured that the level differences between loudspeakers were below 1 dB.

The sound field in the rooms was recorded via an arrangement of 25 measurement microphones (NTi MA2230), uniformly spaced with a distance of 5.5 cm on a straight line, extending from the centre of the loudspeaker arrangement. The microphones are class 1 certified according to IEC 61672 and equipped with a 1/2" capsule. After measurements, all microphones were calibrated using a Brüel & Kjær 4231 calibrator.<fnref n="4" /> The microphone arrangement is shown in figure 2.

<figure><img src="image_2.png" /><figcaption>Figure 2: Microphone arrangement in the meeting room.</figcaption></figure>

A DirectOut ANDIAMO AD/DA converter and microphone preamplifier provided in- and outputs for microphones and loud-

speakers. The device, running at 44.1 kHz sample rate, was connected via MADI with an RME sound card (HDSPe MADI in the measurement chamber, Madiface Pro in the meeting room) to the measurement PC. Any occurring latency caused by the AD/DA conversion was measured and compensated on the recordings.

The sound field was captured for several primary source configurations with loudspeakers playing uncorrelated uniform white noise. In the first configuration all loudspeakers were playing, while in the second configuration only the loudspeaker pointed at by the microphone arrangement was active. With this source position defined as 0°, an additional configuration with four active loudspeakers at {0°, 90°, 180°, 270°} was recorded in the measurement chamber. Additionally, impulse responses between each loudspeaker and microphone were obtained using multiple exponential sine-sweeps [6]. For the ANC co-simulation, an additional loudspeaker acting as a secondary source positioned closer to the central position was set up as measured as indicated in figure 1. This speaker was not present during measurements of the primary sound sources.

## 2.2. Simulation

The geometry and transducer locations of both assessed rooms were recreated in TASCAR for acoustic scene simulations. As receivers in simulations do not influence the sound field nor have any placement constraints, a finer grid of measurement positions with a spacing of 0.5 cm, that also extended closer to the source at 0°, was chosen.<fnref n="5" />

To assess the capability of TASCAR for acoustic sound field simulations, different degrees of accuracy were implemented. In the most basic case, only a free field scenario without any reflections is considered. In the next case, early reflections are simulated based on a 2^nd^ order ISM. Estimations for the frequency dependent absorption coefficients are used based on the surface materials. Values were used from the manufacturer's data sheet on the absorber material in the measurement chamber [7] and from a comprehensive, openly accessible database of common materials [8]. To increase the grade of detail further, in the next case the sources' directivity is adjusted. TASCAR has a *cardioidmod* source type, where a cardioid directivity pattern is achieved at a defined frequency. Towards lower frequencies an omnidirectional, towards higher frequencies a narrower directivity is modelled. To get an estimate for the frequency at which the cardioid pattern should be reproduced, the magnitude response of the physical loudspeaker was measured at 0° and 90° horizontal angle. The frequency at which both responses started to diverge by 6 dB, 940 Hz, was used as the value for the simulation. In the last case, a diffuse FDN reverb [9] with the reverberation time taken from the measurements is added. The different degrees of accuracy for all cases are summarised in table 1.

<table><caption>Table 1: Description of the simulations' degree of accuracy.</caption><thead><tr><td>Case</td><td>Description</td></tr></thead><tbody><tr><td>0</td><td>Free-field conditions</td></tr><tr><td>1</td><td>2<sup>nd</sup> order image source model (ISM)</td></tr><tr><td>2</td><td>2<sup>nd</sup> order ISM + source directivity</td></tr><tr><td>3</td><td>2<sup>nd</sup> order ISM + source directivity + FDN reverb</td></tr></tbody></table>

<footnote n="4">⁴Due to a too high preamplifier gain in the measurement chamber, the calibrator's signal at 94 dB caused clipping. Therefore, the digitally set-table amplifier gain has been reduced by 10 dB for calibration in this case. Subsequently, the lower gain was used for the meeting room recordings.</footnote>

<footnote n="5">⁵The simulations are split into several segments with smaller numbers of receivers to reduce computational load and memory requirements.</footnote>
For the room acoustic evaluation, the acoustic scene was rendered offline on a high performance computing node for faster calculation as the input signal is known beforehand. Real-time simulations are performed on an ordinary notebook.

### 3. ROOM ACOUSTIC PARAMETERS

To assess the quality and accuracy of the recreated, simulated sound field, two properties are evaluated and compared to measurements for every source setup - the level distribution and the spatial coherence along the microphone arrangement.

#### 3.1. Level Distribution

As quite simple yet effective measure, the level distribution is based on 30 s long recordings of uncorrelated uniform white noise for each source configuration (one, four or eight loudspeakers). After applying the individual calibration for each microphone, the respective root mean square (RMS) levels are calculated. The same noise realisation has also been used as input signal for the simulations in TASCAR for the respective source configurations.

<figure><img src="image_3.png" /><figcaption>(a) 1 loudspeaker</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>(b) 4 loudspeakers</figcaption></figure>

Figure 3: Comparison of measured and simulated sound level distributions relative to the levels at the central position in the meeting room along the microphone array.

Figures 3 and 4 show the level distributions with different source configurations and rooms. All curves are normalised to the level at the central position. In the measurement chamber with

<figure><img src="image_5.png" /><figcaption>(a) 1 loudspeaker</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>(b) 4 loudspeakers</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>(c) 8 loudspeakers</figcaption></figure>

Figure 4: Comparison of measured and simulated sound level distributions relative to the levels at the central position in the measurement chamber along the microphone array.

almost free field conditions towards higher frequencies, only minor differences between simulations and measurements can be observed. However, in the more reverberant meeting room, differences between the simulation cases are more obvious. With only one source active, simulation case 0 with only free field conditions shows a too steep rise in level, as the influence of the room reflections are neglected here. With four active sources in figure 3(b), cases 2 and 3 result in a decrease in level when leaving the central position.
Most likely *cardioidmod* sources exhibit too much directivity towards high frequencies, generating a too narrow sweet spot at the central position. Over all scenarios, the simulations with early reflections and omnidirectional source directivity (case 1) deliver the most accurate results. Small ripples in the measured levels around the central positions can be observed, most likely due to variances between the measurement microphones.

## 3.2. Spatial Coherence

A second parameter often used to describe sound fields is the spatial correlation between two points. It was initially proposed as additional parameter to describe the acoustics of a closed room [10]. Various authors derived analytical results for the correlation in various scenarios [11, 12, 13]; In the most notable finding by Cook et al. [10], it takes the form

$$R = \frac{\sin(kr)}{kr} \qquad (1)$$

in a pure-tone diffuse sound field, where $k = \frac{2\pi}{\lambda}$ is the wave number with wavelength $\lambda$ and $r$ the distance between the two observed points.

Measuring the spatial correlation can be a tedious and time consuming process, as usually only a single frequency can be assessed at once. A faster, alternative approach is to play and record a broadband signal and calculate the coherence. It has been shown by Jacobsen and Roisin [14], that the coherence at the evaluated frequency corresponds to the squared correlation, if a sufficiently fine analysis bandwidth is chosen. With this technique, all excited frequencies can be evaluated using only a single measurement.

The coherence is analysed and compared for three different frequencies of {300, 1000, 1700}Hz, all of which lie above the Schroeder frequency [15] for both assessed rooms (approximately 60 Hz in the measurement chamber and 260 Hz in the meeting room). Figure 5 shows the results for the measurement chamber. Unsurprisingly, the coherence in these close to free field conditions is estimated perfectly as unity over all positions for a single source. Also for four and eight sources a good resemblance of the measurements can be observed over the whole assessed range. Interestingly, simulation case 0 without any early reflections performs best at 300 Hz. This could be explained by a sub-optimal choice of absorption coefficients for the ISM.

The coherence in the meeting room in figure 6 shows larger differences between measurements and simulations. While simulations perform relatively well at lower frequencies at positions close to the reference location, less resemblance is seen with increased frequency and distance. This behaviour can be explained by the lack of proper diffuse reverberation. Discrepancies between measurement and simulations with a single source at low frequencies are most likely the result of room modes which cannot be reproduced by a 2nd order ISM.

# 4. APPLICATION FOR ACTIVE NOISE CONTROL

To show the capabilities of the proposed toolchain for real-time processing, a co-simulation of an active noise control algorithm based on actual measurements is compared to a full simulation in TASCAR and FAUST. Before describing the details of the simulation setup, the basics of signal processing for active control are presented.

## 4.1. Feedforward Filtered-X Least Mean Squares Filter

ANC can be implemented using different fundamental designs. As this section only highlights the relevant portions of the algorithm for the investigated scenario, the interested reader may be referred to more detailed literature [16, 17].

A feedforward system can be used to control both deterministic and random disturbances. A time-advanced reference signal $x[n]$ is captured, filtered by a so-called control filter $W(z)$, and played back via transducers, also referred to as secondary sources. The control signal $u[n]$, the signal for the secondary sources, can therefore be described in $z$ domain as

$$U(z) = W(z)X(z), \qquad (2)$$

where e.g. $X(z)$ corresponds to the $z$-transform of $x[n]$. The residual error signal at the listening position is now found as the superposition of the primary disturbances $d[n]$ and the control signal $u[n]$, whereby the alterations to the signal by the acoustic plant $G(z)$ between the secondary source and the listening position have to be considered as well. This results in

$$E(z) = D(z) + G(z)U(z) = D(z) + G(z)W(z)X(z) \qquad (3)$$

for the $z$-transform of the residual error.

ANC often relies on adaptive filters to compensate for slight variations in the acoustic scenario. An ordinary least mean squares (LMS) filter cannot be used in this case, as the output of the control filter is altered by the acoustic transfer path $G(z)$ before summation with the disturbances [18]. However, a trick can be used to find a suitable structure. Assuming both the acoustic plant $G(z)$ and the control filter $W(Z)$ are in a modelling phase linear and time-invariant systems, the position of the two blocks can be switched due to their commutative property. Now the reference signal is first filtered by the plant, producing the so-called filtered reference signal $r[n]$, which is then used as input for the control filter. In time domain, this results in the error signal

$$e[n] = d[n] + \mathbf{w}^T \mathbf{r}[n], \qquad (4)$$

where $\mathbf{w} = [w_0, w_1, ..., w_{N-1}]^T$ holds the coefficients of the control filter, $\mathbf{r}[n] = [r[n], r[n-1], ..., r[n-N+1]]^T$ is a buffer of filtered reference signals, and $N$ corresponds to the control filter order.

As the control filter output now directly contributes to the residual error signal, its coefficients can be adapted with an LMS approach as

$$\mathbf{w}[n+1] = \mathbf{w}[n] - \mu \mathbf{r}[n]e[n], \qquad (5)$$

where $\mu$ is the adaptation stepsize.

As in a real physical system plant and control filter cannot be switched, the reference signals are filtered for adaptation with an estimate or measurement of the plant response $\hat{G}(z)$. This algorithm is known as *filtered-x* or *filtered-reference LMS* (FxLMS) filter.
<figure><img src="image_8.png" /><figcaption>(a) 1 loudspeaker, f = 300 Hz</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>(b) 1 loudspeaker, f = 1000 Hz</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>(c) 1 loudspeaker, f = 1700 Hz</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>(d) 4 loudspeakers, f = 300 Hz</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>(e) 4 loudspeakers, f = 1000 Hz</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>(f) 4 loudspeakers, f = 1700 Hz</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>(g) 8 loudspeakers, f = 300 Hz</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>(h) 8 loudspeakers, f = 1000 Hz</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>(i) 8 loudspeakers, f = 1700 Hz</figcaption></figure>

Figure 5: Comparison of the spatial coherence of measured and simulated sound fields in the measurement chamber between the central position and points along the microphone array at distance *r* for analysis frequencies *f* with corresponding wavenumber *k*.
<figure><img src="image_17.png" /><figcaption>Figure 6: Comparison of the spatial coherence of measured and simulated sound fields in the meeting room between the central position and points along the microphone array at distance r for analysis frequencies f with corresponding wavenumber k.</figcaption></figure>
## 4.2. Simulation Setup

The goal of the ANC experiment is to determine FAUST's and TASCAR's ability to perform simulations for audio algorithms. In this context, the real-time capability of standalone applications written in FAUST constitutes a vital prerequisite for the success of the combined simulation approach. As the focus lies on both feasibility and comparability to measurements, only a setup with a single primary source at 0° and a single receiver at the central position is assessed for both rooms and all degrees of detail for the simulations at 22 050 Hz sample rate. All experiments are performed on an ordinary notebook (Intel Core i7-13700H with 32 GB RAM, running Ubuntu Studio 24.04).

The FxLMS algorithm described in section 4.1 is implemented in FAUST. The secondary transfer path $\hat{G}(z)$ is obtained by rendering an impulse response in TASCAR between the secondary source and the receiver position. Both the adaptive filter and the plant $\hat{G}(z)$ are implemented as FIR filters with 128 taps<fnref n="6" /> ($\hat{G}$ is 5.8 ms). For more efficiency, the predelay, i.e. the acoustic time of flight between source and receiver, is cut from the secondary path and modelled as a simple delay. A white noise innovation signal is played back by pure data (pd). For optimal performance, it is used both as signal for the primary source in TASCAR and as reference signal $x[n]$ for the FxLMS algorithm. The secondary path filter $\hat{G}(z)$ and the adaptive filter itself are compiled as two separate programs. The FxLMS filter generates the control signal $u[n]$ for the secondary sources, which is routed to TASCAR as well. TASCAR renders the signal at the virtual microphone position sample by sample in real-time, based on the primary and secondary sources' signals with the selected degree of detail. The resulting residual error signal $e[n]$ is then recorded in pd and transmitted to the FxLMS filter for adaptation as well. Figure 7 shows the signal flow of the ANC simulation. For bulk processing and better repeatability, central control of the ANC experiment (e.g. initialisation of all applications and scenes, setting parameters via OSC) is implemented in Python, while the JACK connections are built automatically with the `jcmess` utility.

<figure><img src="image_18.png" /><figcaption>Figure 7: Signal flow of the ANC simulation using pure data (white), FAUST (blue) and TASCAR (orange).</figcaption></figure>

All FAUST scripts are compiled as standalone JACK application by the `faust2jaqt` tool. The signals are routed using JACK Audio with the virtual dummy driver at 22 050 Hz sample rate and a block size of 16 samples. Due to the recursion of the error signal

back into the FxLMS block for adaptation and to pd for recording, a latency of one block (16 samples $\hat{=} 0.726$ ms) is introduced on the error signal $e[n]$. This latency has to be taken into account for adaptation, meaning $r[n]$ has to be delayed by the same amount. For better comparability, the RMS output signal levels in TASCAR have been calibrated for each case to match those of the actual measurements.

For the measurement-based reference, the innovation signal is convolved with the impulse responses measured in the physical rooms to obtain the disturbance signal $d[n]$ at the central position. The disturbance signal as well as the innovation signal, used as reference signal $x[n]$, are processed in a Python co-simulation of the FxLMS ANC algorithm. The same set of parameters (step size $\mu$, FxLMS filter order $N$, secondary path order) is chosen for the ANC algorithm in Python, the loopback delay caused by JACK in the TASCAR and FAUST simulation is modelled as well. For a realistic scenario, a full length version of the secondary path response $G(z)$ is convolved sample by sample with the calculated control signal $u[n]$ before summation with the primary disturbances $d[n]$ to get the error signal $e[n]$.

## 4.3. Results

Table 2 and figure 8 show a comparison of simulations and measurement based co-simulations in terms of error signal levels before and after adaptation. In a simulation with free field conditions (case 0), the residual error decays towards zero and approaches numerical limits. However, in a real room, even a measurement chamber, this perfect cancellation is not reached due to unavoidable reflections and reverberation. In the measurement chamber, simulation scenarios with at least early reflections (cases 1 to 3) exhibit more realistic results. A relatively similar level with engaged ANC is achieved this way compared to the co-simulation, although with about 5 dB to 6 dB more attenuation. It is also noticeable, that the simulation in FAUST and TASCAR adapts slower compared to the measurements. A look at the spectrum of the error signal before and after adaptation in figure 9 shows that the ISM in TASCAR causes an increased level towards low frequencies, as the extrapolated absorption coefficients in this frequency range exhibit lower values. This behaviour is intensified in the signal level after adaptation as spectral tilt, most likely as early reflections in the secondary path are not considered by the ANC algorithm with the chosen filter order.

In the meeting room, results of both measurement and simulations with early reflections show no meaningful noise reduction. This is due to the fact, that early reflections with strong contributions cannot be controlled as they are exceeding the 128 samples length of both adaptive filter and modelled secondary path.

<table><caption>Table 2: Comparison of ANC performance in terms of error signal root mean square (RMS) values in dB.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Measurement Chamber</th><th colspan="2">Meeting Room</th></tr><tr><th>ANC off</th><th>ANC on</th><th>ANC off</th><th>ANC on</th></tr></thead><tbody><tr><th>Meas</th><td>-39.8</td><td>-51.6</td><td>-44.7</td><td>-44.8</td></tr><tr><th>Sim 0</th><td>-39.8</td><td>-103.3</td><td>-44.7</td><td>-122.1</td></tr><tr><th>Sim 1</th><td>-39.8</td><td>-57.9</td><td>-44.7</td><td>-45.1</td></tr><tr><th>Sim 2</th><td>-39.8</td><td>-57.2</td><td>-44.7</td><td>-45.1</td></tr><tr><th>Sim 3</th><td>-39.8</td><td>-57.2</td><td>-44.7</td><td>-45.1</td></tr></tbody></table>

<footnote n="6">⁶Higher filter orders would lead to an unreasonably long build time of the adaptive filter in its current implementation.</footnote>

<footnote n="7">⁷https://github.com/synthnassizer/jcmess</footnote>
<figure><img src="image_19.png" /><figcaption>Figure 8: Comparison of error signals for one noise realisation with ANC adaptation enabled from 5 s onwards.</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>Figure 9: Comparison of 1/3 octave smoothed error magnitude spectra for one noise realisation with and without ANC.</figcaption></figure>
## 5. CONCLUSION AND OUTLOOK

This study assesses the capability of combining FAUST and TASCAR for signal processing and acoustic scene simulations in order to develop and test real-time capable audio algorithms. Simulation results from these tools are compared to measurements in two real rooms: a measurement chamber with an extremely short reverberation time, and a meeting room with anisotropic acoustic properties. Room acoustic evaluations based on the level distribution along a microphone arrangement show a good match between measurements and simulations just considering early reflections and omitting source directivity. The spatial coherence is highly similar between simulations and measurements in the measurement chamber as well. Greater differences are observed in the more diffuse meeting room, most likely caused by the ISM order and lack of proper diffuse reverberation.

An active noise control algorithm is tested in TASCAR for acoustic scene simulation and FAUST for signal processing on an ordinary PC, using JACK Audio for signal routing. Compared to a measurement based co-simulation, a full scale simulation in TASCAR and FAUST exhibits comparable results when considering at least early reflections. However, the low filter order of the implemented FxLMS algorithm only permits the evaluation of relatively dry acoustic scenarios.

The full potential of a combined simulation approach with FAUST and TASCAR lies in time-variant simulations, as all parameters and virtual positions can be updated during operation via OSC. Future works may include the integration of head-tracking devices, for example to develop and improve position-dependent local ANC algorithms [19, 20].

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