Patent ID: 12242972

DETAILED DESCRIPTION OF THE DRAWINGS

In one embodiment as shown inFIG.1the simulation100comprises an input step101and a pre-processing step102. The simulation subsequently branches into a DG (discontinuous Galerkin finite element method) module120, which implements an embodiment of a finite element method (FEM) as described herein, for simulating wave propagation at low-mid frequencies and a GA (geometrical acoustic) module130, which implements an embodiment of a geometrical acoustics solver as described herein, for simulating wave propagation at high frequencies.

The input is in the current embodiment a 3D model of the virtual space104for which a simulation is desired. As shown inFIG.1the virtual space is defined by a floor105, four walls106,107,108,109and a ceiling110such that the model is watertight. The outer boundaries defining the virtual space, i.e. walls, ceiling and floor are shown as transparent element in order to show the content of the virtual space. The 3D model of the virtual space further contains a window111arranged in one of the walls106, two tables112,113, a room divider114arranged between the two tables112,113and a sound absorbing ceiling tile115suspended from the ceiling110. The different elements of the virtual space are described by their 3D shape in the 3D model but also described by respective acoustic properties which will be described later. In other words, each surface describing the different element defines a boundary in the model and the boundary properties described how the simulated sound is affected when it interacts with the boundary.

A sound source116is positioned at one of the tables112. The sound source in the current embodiment is an omnidirectional sound source, i.e. the sound propagates in all directions. In other embodiments the sound source could for example be directional.

During pre-processing different data may be prepared for use in simulation. For example in the current embodiment the 3D model of the virtual space is converted into a curvilinear mesh model. The curvilinear mesh model has the advantage that sound reflected from rounded and curved surfaces are better simulated. Typically curved surfaces has been represented by a staircase model where horizontal and vertical faces approximates the curvature. From a modelling perspective this may be fine, e.g. if the resolution is high. However, when simulating acoustic waves this will always create an either vertical or horizontal surface on which the sound is reflected and even a high resolution cannot correct for this. Accordingly, by generated a curvilinear mesh model the boundary regions and surface can be simulated more detailed for curved and bent objects.

Also, the 3D model of the virtual space is described as a volumetric 3D model where the virtual space defining the 3D model is split/defined into discrete volumetric meshes or sub-domains providing a discretized volumetric mesh. In the current embodiment the volumetric meshes consist of an unstructured mesh of tetrahedral elements and the discretisation process will process the 3D model so that the tetrahedrals are as uniform as possible. As will be described, this advantageously improves the computation time when applying the DG method to the 3D model. As mentioned, other element shapes can also be used.

With the input received and prepared the simulation splits the simulation into two parts, a simulation at lower and mid frequencies using a waved based solver in the DG module120and a simulation at higher frequencies using a geometrical acoustics solver in the GA module130.

The DG module120excites the volumetric model with an impulse121at the sound source position. As the impulse is excited in the volumetric model the DG method122is applied to the discretized mesh and a boundary region module123and a time marching module124is used to simulate the wave propagation of the impulse in the 3D model.

Applying the DG method to discretized mesh has the advantage that the DG method can be applied to each volume of the 3D model separate and independently and thus allows the method to be computed in parallel for each volume. This allow the computing time to be reduced drastically while still allowing for waveform based simulation to be performed.

In particular this has been found to be an advantageous aspect of the finite elements methods (FEM), such as the discontinuous Galerkin finite element method, as these improves the processing time considerable since each volume can be processed independently and thus in parallel to each other.

One important aspect of wave based simulation is how the wave propagation reacts at a boundary. A boundary should be understood as any change in material or substance in which the wave travels or that it hits or encounter. E.g. a boundary region can for example be a surface that reflects the sound, a surface or material that absorbs the sound or a surface that diffuses the sound.

When applying the DG to the volumetric model122the boundary region module123holds models describing the acoustic properties of the different materials in the 3D model of the virtual space.

Also, discretely applying the DG method to the volumetric 3D model would result in a simulation only showing the result at a specific point in time. Thus, in order to provide a temporal simulation a time marching module124is provided. The time marching module comprises one or more time marching algorithms that connects the temporal aspect with the of the DG method over time. The implementation of a time marching algorithm has been previously discussed herein.

The time marching module tells the system how the wave based propagation is simulated over time, thus the system will reiterate steps122,123and124for the full duration of the simulation, i.e. until the wave based simulation is complete.

The duration of the simulation can be determined in different ways. One way is to simple decide to run the simulation for a predetermined time period. However, this will most likely create redundant simulation as the time will most likely be set such that it is ensured that the simulation is complete. E.g. where an impulse is simulated to determine an impulse response the time will be set such that it is ensured that all relevant information is obtained relevant to the impulse response.

However, in the current embodiment the global energy in the virtual space is determined each time the iteration arrives at step124, and if the global energy level is below a certain threshold then the method will proceed to step125.

When the DG has been applied to the volumetric model as described the result is a simulation of the wave propagation of the impulse from the sound source position and throughout the 3D model of the virtual space. Thus, for any listening position in the virtual space the impulse response can be derived125as the simulation can be played back while recording the impulse response of the sound source position at that listening point, for example at the table113as indicated by the listening point location117.

At the higher frequencies the simulation is performed in the GA module130. A discussed previously the advantage of using a geometrical acoustic method at higher frequencies is that the wave propagation is treated as ray propagation whereby ray tracing can be used and thus reduce the processing time considerably for the higher frequencies where the diffraction, diffusion and other wave based principles does not occur with the same effect as in the lower frequencies and thus can be considered as negligible. Depending on e.g. the application, use and type of rendering which the simulation is used for the frequency interval defining the lower and respective high frequency may differ and chosen in order to provide the best result and a desired speed for the specific case.

As the geometrical acoustics solver (GA module) considers the energy decay of the sound propagation the model is not excited with an impulse as in the DG module. Instead the decay information is derived and used to establish an impulse response. The simulation in the current embodiment is performed using an image source process in the image source module132and subsequently by a ray tracing algorithm in the ray tracing module133.

Both the image source process and the ray tracing algorithm estimates the sound propagation as rays, e.g. specular reflections. However, the image source process is more accurate as it determines the exact specular reflections wherein the ray tracing is less exact as it is a stochastic method. Thus, in the current embodiment the image source is used for the first couple of reflections of the rays where the energy of the ray is high and the ray tracing method is used for the remaining reflections until the simulation is stopped.

The result from the image source simulation and the ray tracing simulation is then hybridized into one simulation in the hybridization module134whereby a simulation of the wave propagation of the impulse at the high frequencies can be established.

Similarly to the DG module step of deriving the impulse response125, the impulse response of the GA method is derived in step135.

The DG impulse response and the GA impulse response are merged or hybridized136into one simulated impulse response.

The simulated impulse response can be used to render sound140. As discussed rendering the sound can be done in many different way. In the current embodiment one of the simplest way is shown where the simulated impulse response is convoluted with the sound source audio141. This is a common way to provide a sound scape to a sound file and thus provide an audio rendering142that gives an audible experience of standing at the position where the impulse responses were registered117and listening to the audio coming from the sound source position.

FIGS.2and3shows two different embodiments of boundary absorption models that can be used to describe the acoustic properties of materials as the acoustic wave hits the surface/boundary of the material.

FIG.2illustrates a boundary surface model where a sound wave200emitted from a sound source201travelling through a material202and hits a second material203. The direct sound wave200′ is indicated by broken circle segment and the 1storder reflection200″ of the sound wave is indicated by broken circle segments having a higher number of broken segments.

The boundary surface model is in the current embodiment considered at two points A and B on the surface of the second material203. However it should be understood that the incident sound wave200hits the surface of the second material across the entire surface facing the sound source201. Thus similar considerations can be done for any point on the surface of the second material.

At point A first reflection occurs along the first incident direction204at an incident angle α. The sound wave is reflected of the surface along the first reflected direction205. Some of the energy is absorbed by the second material as indicated by the arrow206.

Similarly, at point B a first reflection occurs long a second incident direction207at an incident angle β. As can be seen the incident angle β is different from the incident angle α. The sound wave at point B as reflected of the surface along a second reflected direction208and some of the energy is absorbed by the second material as indicated by the arrow209.

In the current embodiment a boundary absorption model is used where the sound reflected205,208is same and independent of the incident angle and thus, the sound and energy absorbed206,209is the same.

Such an absorption model is suitable for highly reflective materials, e.g. hard even surface such as glass, brick and metal.

An identical setup, but with a different second material303is shown inFIG.3where the energy absorbed and reflected changes depending on the angle of the incident sound wave at the specific point on the surface.

Similarly toFIG.2,FIG.3illustrates a sound wave300emitted from a sound source301travelling through a material302and hits a second material303. The direct sound wave300′ is indicated by broken circle segment and the 1storder reflection300″ of the sound wave is indicated by broken circle segments having a higher number of broken segments.

The boundary surface model is in the current embodiment considered at two points A and B on the surface of the second material303. However it should be understood that the incident sound wave300hits the surface of the second material across the entire surface facing the sound source301. Thus similar considerations can be done for any point on the surface of the second material.

At point A first reflection occurs along the first incident direction304at an incident angle α. The sound wave is reflected of the surface along the first reflected direction305. Some of the energy is absorbed by the second material as indicated by the arrow306.

Similarly, at point B a first reflection occurs long a second incident direction307at an incident angle β. As can be seen the incident angle β is different from the incident angle α. The sound wave at point B as reflected of the surface along a second reflected direction308and some of the energy is absorbed by the second material as indicated by the arrow309.

In the current embodiment a boundary absorption model is used where the sound reflected305,308is different dependent on the incident angle and thus, also the sound and energy absorbed306,309is different.

This characteristic is for example found for surfaces of porous materials, such as many sound insulating materials, as the uneven surface breaks and scatters the reflection, in particular when the incident angle of the sound wave is low.

Also, some materials may further propagate a wave, e.g. if the sound wave initially travels through air and hits a water body, the transition between air and water will be described with a boundary absorption model as discussed above, however, the water will further propagate the sound wave with different characteristics than air, for example sound in water travels faster than sound in air and the energy loss is smaller.

Although not shown in the above models as they focus on illustrating the boundary absorption and reflection between two materials there will typically also be an energy loss considered in the simulation that is dependent on the distance travelled by the sound wave. Thus, the energy of the direct sound wave at point B in the above embodiment will typically be less than the energy of the direct sound wave at A since the sound wave have travel a longer distance when reaching point B.

FIG.4shows a diagram of an embodiment of a method used to spatially render signals from wave-based simulations. Preferably, a broadband wave-based acoustics framework is illustrated inFIG.4, where the broadband wave-based acoustics framework may be used to obtain spatial room impulse responses. The spatial room impulse responses can then be encoded into spherical harmonics contributions402, potentially resulting in a high-order ambisonics formulation, which can be combined with free-field head-related transfer functions (HRTFs)403,405for binaural rendering407. The binaural rendering or binaural auralization can in this embodiment be understood as being a combination of transfer functions and impulse responses, e.g. the spatial impulse response generated for the specific room at a listening and an HRTF. An anechoic signal can be convolved with the binaural auralization (not shown) in order to generate the rendered sound at the listening point.

The broadband wave-based acoustics framework may also be used for multi-channel rendering, where a high-order ambisonics formulation can be combined with transfer functions comprising more than two channels. In other terms, while HRTF comprises two receivers or two channels, other transfer functions could potentially comprise more than two receivers or channels, and may be used in the broadband wave-based acoustics framework as illustrated inFIG.4, thereby enabling a multi-channel rendering.

Wave-Based Simulation

The wave-based simulation401that may be used in the embodiment as illustrated inFIG.4may be the wave-based simulation as described herein. Preferably, the wave-based simulation401may be a discontinuous Galerkin finite element method, which can be also described as a discontinuous Galerkin method. Preferably, the wave-based simulation can simulate the acoustic wave over a large frequency range, such as the first frequency range, such as between 20 Hz to 20 kHz, such as between 20 Hz and 10 KHz, such as between 20 Hz and 8 kHz, such as between 50 Hz and 8 kHz, such as between 100 Hz and 8 kHz. As described herein, the discontinuous Galerkin finite element method may have a benefit of being able to handle efficient parallelization and the ability of handling complex geometries, which can be generated in either 2D or 3D.

As shown inFIG.4, a wave-based simulation401is first executed with one or several virtual spherical microphone arrays402embedded into a 3D model of a domain, centered on a given location. The 3D model of the domain may also comprise at least one sound source and acoustic properties of a plurality of boundaries. The wave-based simulation401can simulate the sound field from the at least one sound source into the 3D model of the domain. The given location would preferably be the listening point.

The wave-based simulation may be executed by a wave-based solver, as described herein. The wave-based solver may determine a wave impulse response of the wave based propagation of an impulse emitted at the at least one sound source in the 3D model of the domain and received at the listening point within a first frequency range.

Spatial Rendering and Further Processing

The wave amplitude density â(ω, Γk) where ω represents the angular frequency and (Γk) being the angle pair informs about the spatial properties of the sound field around a given location. More information about the wave amplitude density can be found in [18]. The wave amplitude density can be processed, for instance, to allow binaural auralizations by reconstructing the sound field that would have reached the eardrums of a virtual listener embedded into a virtual room, and preferably located at a listening point or a given location within the virtual room. Such a spatial rendering may be obtained by weighting the different wave contributions based on their direction of incidence (Γk), which can be described mathematically as a multiplication between the wave amplitude density and a complex transfer function H(ω, Γk) accounting for variations in amplitude and phase. H may be the HRTF405associated with each ear. The rendered signal {circumflex over (p)}r(ω) can be expressed directly from the plane-wave expansion as
{circumflex over (p)}r(ω)=+H(ω,Γk)â(ω,Γk)dΓk,  (14)
Where the dependence on the position r is omitted since the expansion can be evaluated, without loss of generality, at r=0 (i.e., the center of the head is assumed to be located at the origin of the coordinate system). In other words, â(ω, Γk) is here used to represent the sound field at the location of the center of the head, while by definition H(ω, Γk) may relate the center of the head to each eardrum.

Equation (14) can be evaluated directly in the spherical Fourier domain by first introducing Hnm(ω), the projection of H(ω, Γk) against the basis functions Ynm:

H⁡(ω,Γk)=∑n=0∞∑m=-nnHnm(ω)⁢Ynm(Γk)(15)

After replacing in Equation (14), it can be shown that thanks to the orthogonality property of the Ynmfunctions, the spatial rendering equation407becomes after some algebra:

p^r(ω)=∑n=0∞∑m=-nna^nm(ω)⁢Hnm(ω)(16)

Finally, after truncation up to order N, this amounts in the time domain to computing (N+1)2convolution integrals of the form

pr(t)≃∑n=0∞∑m=-nn(anm*hnm)⁢(t)(17)
with anm(t) and hnm(t) the impulse response functions of ânm(ω) and Hnm(ω), respectively; both impulse responses are real-valued for the real basis functions defined previously. The coefficients anm(t) are more commonly known as “the ambisonics coefficients” and can be defined as a spatial impulse response.

The ability to spatially render simulation data may require an estimate of the wave amplitude density.

Array of Receivers and Processing

An array of secondary receivers402may be placed around the sampling location or the listening point to encode the sound field, as illustrated inFIG.4. The secondary receivers can be microphones. Consider an open sphere array of radius R consisting of Q receivers sampling the domain at location rq=(R, Γq) with 1≤q≤Q. The signal ŝ(rq,ω) recorded by microphone q follows a relation in the form

s^(rq,ω)=∑n=0∞∑m=-nnanm(ω)⁢bn(k⁢R)⁢Ynm(Γq)(18)
with the radial functions bn(kR). For an ideal nondirectional receiver of unity gain, the sampled signal is identical to the local pressure fluctuations and thus, the radial functions are given as bn(kR)=4πinjn(kR).

Taking the inverse Fourier transform of (18) directly yields an estimate for the ambisonics coefficients as

a^nm(ω)=1bn(kR)⁢∮S2s^(rq,ω)⁢Ynm(Γq)⁢d⁢Γq(19)

The surface integral in (19) can either be evaluated via a direct integration or the coefficients can be estimated globally in a least-square sense. Equation (19) shows that a division by bnis required to compute the coefficients anmof matching order n. This operation is inherently ill-conditioned whenever the radial functions are zero or have a low magnitude, and can constitute a fundamental limitation of the open-array design considered here since the spherical Bessel functions jn(kR) can be zero for some frequencies. Rigid-sphere array configurations can be used in measurements to circumvent the nulls of the radial functions, but such an approach is less suitable in simulations considering it requires meshing the surface of a sphere. Moreover, the resulting array would not be acoustically transparent.

Another solution to enhance the robustness of the spatial encoding procedure may consist in an open-sphere array of directional receivers with a first-order cardiod directivity pattern. For the latter, it can be shown that the radial functions become
bn(kR)=4πin[jn(kR)−ij′n(kR)]  (20)
where j′n(⋅) denotes the derivative of jn(⋅) with respect to the argument. This relation effectively prevents the nulls occurring with nondirectional receivers. A first-order cardiod directivity pattern can easily be obtained from simulations by sampling the pressure field p(rq, ω) as well as the radial particle velocity {circumflex over (v)}r(rq, ω). The signal recorded at the sampling locations can be obtained as
ŝ(rq,ω)={circumflex over (p)}(rq,ω)+ρ0c{circumflex over (v)}r(rq,ω)  (21)
which can be understood as the superposition of an omnidirectional and a bidirectional microphone. The radial functions for the open cardioid array configuration can still have a low magnitude: an additional regularization procedure is therefore applied to the radial functions, to prevent an excessive amplification of the noise present in the simulations.

The number of receivers Q in the array directly determines the maximum truncation order N that can be considered for the spherical harmonics decomposition. The sound field must be sampled with at least as many receivers as the number of terms used for the expansion, leading to Q≥(N+1)2. This condition is necessary to avoid undersampling but does not necessarily guarantee accuracy. The optimal number of sampling points depends on the chosen quadrature rule; an exact integration can for instance be performed with a Gauss quadrature for Q≥2(N+1)2, but other approaches can require less samples. Another constraint may pertain to spatial aliasing, which can occur if the recorded sound field ŝ(rq, ω) is not order-limited but admits non-zero spherical harmonics coefficients for orders higher than N. In this case, the higher modes will be aliased to the lower modes and may degrade the quality of the decomposition. Aliasing can be mitigated by choosing a sufficiently large N, or with dedicated anti-aliasing spatial filters.

FIG.4shows a diagram of an embodiment of a method used to spatially render acoustic signals from wave-based simulations. Once the wave-based simulation is completed, the results are post-processed to extract the wave amplitude density coefficients by solving Eq. (19), based on a projection against spherical harmonics basis functions. The spatial rendering equation (16), or, equivalently, (17), can be evaluated to account for a given binaural HRTF dataset (with a given orientation) in the wave-based simulation. A spatial rendering can then be obtained by convolving the binaural HRTF dataset in the wave-based simulation with an audio signal to be spatially rendered. A plurality of balloon plots are illustrated in the center of the figure to depict the shape of the considered spherical harmonics basis functions up to n=5.

The HRTF dataset405may comprise different orientation data. Preferably, the HRTF dataset can be simulated or measured with a HRTF reference. The HRTF reference may be uncorrelated with the ambisonics coefficient, thereby potentially unmatching the ambisonics coefficient reference when solving the spatial rendering equation. Thereby, a rotation of the HRTF reference406may be necessary in order to align the HRTF reference with the ambisonics coefficient reference. If the HRTF reference is not aligned with the ambisonics coefficient reference, the rendered signal may be generated with an offset, thereby generating a wrong audio rendering feeling to the user.

In parallel to the wave-based simulation and binaural auralization as described, a geometrical acoustics based simulation may also be performed for a second frequency range. The final result can thus be a hybridization of a geometrical-acoustics based simulation with a wave-based simulation. The hybridization can for example be the hybridization as described in the present disclosure.

FIG.5shows a system500in accordance with an embodiment of the present invention. System500includes a computer system502. Computer system502includes specialized hardware and/or software modules that execute on a processor504coupled to a memory506. The computer system502may also be communicatively coupled to a communications network508. Network508may be a public network, such as the internet, or it may be a private network, such as a network internal to a company. Network508also may be a combination of public and/or private networks. The computer system502may be coupled to the network508directly, for example via an Ethernet cable or via wireless connection such as Wi-Fi. Computer system502may also be coupled to the network508in any other way known to the skilled person, for example indirectly through another device (not shown), such, as, but not limited to, a router, a switch, a hub, a separate computer system, a mobile device, a modem, and/or a combination of these devices. The processor504is configured to execute any of the methods described in detail throughout the present disclosure.

The computer system502further includes one or both of an impulse response generation module512, a neural network training module514and a HRTF generation module524, both executing on processor504. While all modules are shown inFIG.5, it is expressly noted that only the impulse response generating model512or only the neural network training module514or the HRTF generation module524may be present. The impulse response generation module512is configured to execute one or more methods for generating an impulse response as described above in detail. Exemplarily, the impulse response generation module may also include at least one of a wave-based solver and a geometrical acoustics-based solver. The neural network training module514is configured to execute one or more methods for training a machine learning model for audio compensation as described above in detail. The HRTF generation module524is configured to execute one or more methods for generating an HRTF as described above in detail. In some embodiments, modules512,514and524are specialized sets of computer software instructions programmed onto one or more dedicated processors in computer system502and can include specifically designed memory locations and/or registers for executing the specialized computer software instructions.

Although modules512,514and524are shown inFIG.5as executing within the same computer system502, it is expressly noted that the functionality of modules512,514and524can be distributed among a plurality of computer systems. Computer system502enables modules512,514and524to communicate with each other in order to exchange data for the purpose of performing the described functions. It should be appreciated that any number of computing devices, arranged in a variety of architectures, resources, and configurations (e.g., cluster computing, virtual computing, cloud computing) can be used without departing from the scope of the invention. Exemplary functionality of modules512,514and524is described in detail throughout the specification.

In some embodiments, a machine learning model510is coupled to the network508, as shown inFIG.5, or included in computer system502. The machine learning model510may be a single architecture or it may be a combination of a plurality of neural network architecture. For example, the machine learning model510may include a first neural network architecture and a second neural network architecture that are different entities. The machine learning model510is configured to provide and/or execute the functions described in detail above. The machine learning model510may be coupled to the network508as shown here and communicate with computer system502over the network508, but it is also expressly contemplated that the machine learning model is part of computer system502. The impulse response generation module512and/or the neural network training module514and/or the HRTF generation module524communicate with the machine learning model510in order to exchange data for the purpose of performing their described functions. It is also expressly noted that the functionality of the machine learning model510may be distributed among a plurality of computer systems. Similar to what is noted above with reference to modules512,514and524, any number of computing devices, arranged in a variety of architectures, resources, and configurations may be used without departing from the scope of the invention.

In some embodiments, an audio device516is coupled to the network508. The audio device516includes one or both of a microphone518and a speaker520. The audio device516may also include a processing system522. The audio device and516and/or its processing system522may be configured to apply machine learning-based audio compensation as described in detail above. To this end, the audio device516and/or the processing system522may be communicatively coupled to a machine learning model, such as machine learning model510, to transmit data to the model and receive data from the model. Illustratively, the audio device516is shown inFIG.5as coupled to network508, for example by use of a communication module. However, it is expressly contemplated that the audio device516may not be coupled to network508. Illustratively, the audio device516may include a machine learning model substantially similar to machine learning model510configured to apply machine learning-based audio compensation as described in detail above. While only one microphone518and one speaker520is shown inFIG.5, it is noted that the audio device516may include more than one microphone and/or more than one speaker.

REFERENCE LIST

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Further Details of the Invention

1. A wave based solver for simulating the propagation of sound in at least one model of a virtual domain based on at least one sound source in the virtual domain and at least one acoustic property of the virtual domain, wherein the wave based solver comprises a finite element method (FEM), such as a discontinuous Galerkin finite element method (DGFEM) or a spectral element method (SEM).2. The wave based according to item 1, wherein the virtual domain is a one dimensional (1D) domain, a two dimensional (2D) domain or a three dimensional (3D) domain such as a three dimensional model (3D) of a virtual space.3. The wave based solver according to item 1, wherein the 3D model of the virtual space defines boundary surfaces of an air volume.4. The wave based solver according to item 1 or 2, wherein the boundary surfaces are meshes, thus representing the 3D model of a virtual space as a 3D mesh model.5. The wave based solver according to item 4, wherein the 3D mesh model is curvilinear.6. The wave based solver according to any one of the items 3-5, wherein the air volume is represented as a volumetric 3D mesh model.7. The wave based model according to item 6, wherein a qualitative evaluation is provided on the volumetric 3D mesh model evaluating critical areas of sound propagation.8. The wave based solver according to any one of the items 1-7, wherein the virtual space is numerically discretized into sub-domains using an acoustic wave equation module.9. The wave based solver according to any one of the items 2-8, wherein the volumetric 3D mesh model of the air volume represents discretized sub-domains.10. The wave based solver according to item 8 or 9, wherein the FEM is applied to each sub-domain separately.11. The wave based solver according to any one of the items 1-10, wherein the wave based solver comprises a locally-reacting frequency dependent boundary modelling sub-module for simulating the propagation of sound at at least one first boundary region comprising a first material.12. The wave based solver according to any one of the items 1-11, wherein the wave based solver comprises an extended-reacting frequency sub-module for simulating the propagation of sound at at least one second boundary region comprising a second material.13. The wave based solver according to item 12, wherein the extended-reacting frequency dependent boundary modelling sub-module is configured to model the second material with fluid structure acoustic simulations.14. The wave based solver according to any one of the items 12-13, wherein the second material is a porous material.15. The wave based solver according to any one of the items 1-14, wherein the 3D model comprises at least one directive sound source for emitting sound in a defined direction.16. The wave based solver according to any one of the items 1 to 15, wherein the wave based solver comprises a time marching sub-module comprising at least a first time marching method for simulating the propagation of sound in time and space.17. The wave based solver according to item 16, wherein the time marching sub-module comprises a second time marching method for simulating the propagation of sound in time and space, wherein the first time marching method comprises a standard low-storage explicit Runge-Kutta algorithm and the second time marching method comprises an implicit-explicit time marching algorithm.18. The wave based solver according to any one of the items 1-17, wherein each sub-domain is solved independently on a central processing unit (CPU) and/or a graphical processing unit (GPU).19. The wave based solver according to any one of the items 1-18, wherein a message passing interface (MPI) is configured to handle communication between central processing units (CPUs).20. The wave based solver according to item 19, wherein the communication handled by the MPI is performed following a halo exchange.21. The wave based solver according to any one of the items 1-20, wherein the wave based solver further comprises extracting one or more wave impulse response(s) based on the simulation of the propagation of sound.22. The wave based solver according to item 21, wherein the wave impulse response(s) is/are spatial impulse response(s).23. The wave based solver according to any one of the items 1-22, wherein the wave based solver renders the simulation of the propagation of sound for one or more output format(s), such as visual and/or graphical and/or numerical, and/or audible output format(s).24. The wave based solver according to item 23, wherein the graphical output comprises visualising at least a part of the simulation of the propagation of sound in a cutting plane of the 3D model of a virtual space.25. The wave based solver according to item 23, wherein the numerical output comprises one or more format(s), such as impulse responses, frequency responses, energy decay curves and/or acoustic parameters such as reverberation time, clarity, sound pressure level and/or speech intelligibility.26. The wave based solver according to item 23, wherein the audible output comprises playing the impulse response convolved with a soundfile.27. A computer implemented method for simulating the propagation of sound in at least one model of a virtual domain based on at least one sound source in the virtual domain and at least one acoustic property of the virtual domain, wherein the method applies a wave based solver for the steps of:providing a wave based simulation by simulating the propagation of sound using at least a finite element method (FEM), such as a discontinuous Galerkin finite element method (DGFEM) or a spectral element method (SEM).28. The computer implemented method according to item 27, wherein the virtual domain is a one dimensional (1D) domain, a two dimensional (2D) domain or a three dimensional (3D) domain such as a three dimensional model (3D) of a virtual space.29. The computer implemented method according to item 28, wherein the 3D model of the virtual space is defined by boundary surfaces of an air volume.30. The computer implemented method according to item 28 or 29, wherein the method represents boundary surfaces as meshes, thereby representing the 3D model of a virtual space as a 3D mesh model.31. The computer implement method according to item 30, wherein the 3D mesh model is curvilinear.32. The computer implemented method according to any one of the items 29-31, wherein the method represents the air volume as a volumetric 3D mesh model.33. The computer implemented method according to item 32, wherein the method performs a qualitative evaluation on the volumetric 3D mesh model evaluating critical areas of sound propagation.34. The computer implemented method according to any one of the items 27-33, wherein the method numerically discretizes the virtual space into sub-domains using an acoustic wave equation module.35. The computer implemented method according to item 34, wherein the method represents the volumetric 3D mesh model of the air volume as discretized sub-domains.36. The computer implemented method according to item 34 or 35, wherein the method applies the FEM to each sub-domain separately.37. The computer implemented method according to any one of the items 27-36, wherein the method comprises simulating the propagation of sound at an at least one first boundary region comprising a first material using a locally-reacting frequency condition.38. The computer implemented method according to any one of the items 27-37, wherein the method comprises simulating the propagation of sound at an at least one second boundary region comprising a second material using an extended-reacting frequency sub-module.39. The computer implement method according to item 38, wherein the extended-reacting frequency sub-module is configured to model the second material with fluid structure acoustic simulations.40. The computer implemented method according to item 38 or 39, wherein the second material is a porous material.41. The computer implemented method according to any one of the items 27-40, wherein the method obtains at least one directive sound source in the 3D model, where the at least one directive sound source is arranged for emitting sound in a defined direction.42. The computer implemented method according to any one of the items 27-41, wherein the method comprises the step of providing a time marching for simulating the propagation of sound in time and space.43. The computer implemented method according to item 42, wherein the method further comprises the step of providing a second time marching method for simulating the propagation of sound in time, wherein the first time marching method comprises a standard low-storage explicit Runge-Kutta algorithm and the second time marching method comprises an implicit-explicit time marching algorithm.44. The computer implemented method according to any one of the items 27-43, wherein each sub-domain is solved independently on a central processing unit (CPU) and/or a graphical processing unit (GPU).45. The computer implemented method according to any one of the items 27-44, wherein a message passing interface (MPI) is configured to handle communication between central processing units (CPUs).46. The computer implemented method according to item 44 or 45, wherein the communication handled by the MPI is performed following a halo exchange.47. The computer implemented method according to any one of the items 27-46, wherein the method further comprises a step of extracting one or more wave impulse responses based on the wave based simulation.48. The computer implemented method according to item 47, wherein the wave impulse response(s) is/are spatial impulse response(s).49. The computer implemented method according to any one of the items 27-48, wherein the method comprises a step of rendering the wave based simulation for one or more output format(s), such as graphical and/or numerical, and/or audible output format(s).50. The computer implemented method according to item 49, wherein the step of rendering the graphical output comprises visualising at least a part of the wave based simulation in a cutting plane of the 3D model of a virtual space.51. The computer implemented method according to item 49, wherein the step of rendering the numerical output comprises generating one or more format(s) such as impulse responses, frequency responses, energy decay curves and/or acoustic parameters such as reverberation time, clarity, sound pressure level and/or speech intelligibility.52. The computer implemented method according to item 49, wherein the step of rendering the audible output comprise playing the impulse response convolved with a soundfile.53. A geometrical acoustics solver for simulating the propagation of sound in at least one model of a virtual domain based on at least one sound source in the virtual domain and at least one acoustic property of the virtual domain, wherein the geometrical acoustics solver comprises:at least one image source module for determining at least one image source simulation by simulating the propagation of sound;at least a first acoustic ray tracing module for determining an at least first ray tracing simulation by simulating the propagation of sound;a hybridization module for combining the at least first ray tracing simulation and the at least one image source simulation to a geometrical acoustics simulation of the propagation of sound; andan output module for preparing the output of the geometrical acoustics simulation.54. The geometrical acoustics solver according to item 53, wherein the virtual domain is a one dimensional (1D) domain, a two dimensional (2D) domain or a three dimensional (3D) domain such as a three dimensional model (3D) of a virtual space.55. The geometrical acoustics solver according to item 54, wherein the 3D model of the virtual space defines boundary surfaces of an air volume.56. The geometrical acoustics solver according to any one of the items 53-55, wherein the at least one image source module determines at least one image source simulation of a first part of the propagation of sound and the at least first acoustic ray tracing module determines an at least first ray tracing simulation of a second part of the propagation of sound.57. The geometrical acoustics solver according to any one of the items 53-56, wherein the first part of the propagation of sound is the primary set of reflections of the propagation of sound and the second part of the propagation of sound is the subsequent reverberations of the propagation of sound.58. The geometrical acoustics solver according to any one of the items 53-57, wherein the primary set of reflections are the first, second and third reflections and the subsequent reverberations are the fourth, fifth and following reflections.59. The geometrical acoustics solver according to any one of the items 53-58, wherein the geometrical acoustics solver further comprises a second acoustic ray tracing module for determining a second ray tracing simulation by simulating the propagation of sound, and that the hybridization module further combines the second ray tracing simulation with the first ray tracing simulation and the at least one image source simulation to the geometrical acoustics simulation.60. The geometrical acoustics solver according to any one of the items 53-59, wherein the first acoustic ray tracing module determines a first subpart of the first part of the propagation of sound and the second acoustic ray tracing module determines a second subpart of the first part of the propagation of sound.61. The geometrical acoustics solver according to any one of the items 53-60, wherein the geometrical acoustics solver is further modified for high performance computing implementation, such as parallelizing propagation of each ray or parallelizing a first and a second frequency range.62. The geometrical acoustics solver according to any one of the items 53-61, wherein the geometrical acoustics solver further comprises a geometrical impulse response module for extracting a geometrical impulse response based on the geometrical acoustics simulation.63. The geometrical acoustics solver according to any one of the items 53-62, wherein the output module prepares the geometrical acoustics simulation for one or more output format(s), such as visual and/or graphical and/or numerical, and/or audible output format(s).64. The geometrical acoustics solver according to item 63, wherein the graphical output comprises visualising at least a part of the geometrical acoustics simulation in a cutting plane of the 3D model of a virtual space.65. The geometrical acoustics solver according to any one of the items 53-64, wherein the geometrical acoustics solver relies on impedance of the boundaries of an air volume.66. The geometrical acoustics solver according to any one of the items 53-65, wherein the geometrical acoustics solver approximates diffraction of acoustic waves in the virtual domain.67. The geometrical acoustics solver according to any one of the items 53-66, wherein the 3D model comprises at least one directive sound source for emitting sound in a defined direction.68. A computer implemented method for simulating the propagation of sound in at least one model of a virtual domain based on at least one sound source in the virtual domain and at least one acoustic property of the virtual domain, wherein the method applies a geometrical acoustics solver for the steps of:providing a ray tracing simulation by simulating the propagation of sound using at least one acoustic ray tracing method;providing an image source simulation by simulating the propagation of sound using at least one image source method;providing a geometrical acoustic simulation of the propagation of sound by combining the ray tracing simulation and the image source simulation.69. The computer implemented method according to item 68, wherein the virtual domain is a one dimensional (1D) domain, a two dimensional (2D) domain or a three dimensional (3D) domain such as a three dimensional model (3D) of a virtual space.70. The computer implemented method according to item 69, wherein the 3D model of the virtual space is defined by boundary surfaces of an air volume.71. The computer implemented method according to item 68, wherein the method comprises the step of determining the image source simulation of a first part of the propagation of sound and the step of determining the at least first ray tracing simulation of a second part of the propagation of sound.72. The computer implemented method according to any one of the items 68-71, where the first part of the propagation of sound is the primary set of reflections of the propagation of the propagation of sound and the second part of the propagation of sound is the subsequent reverberations of the propagation of sound.73. The computer implemented method according to item 72, wherein the primary set of reflections are the first, second and third reflections and the subsequent reverberations are the fourth, fifth and following reflections.74. The computer implemented method according to any one of the items 68-73, wherein the method further comprises providing a second acoustic ray tracing module for determining a second ray tracing simulation by simulating the propagation of sound, and that the hybridization method further comprises the step of combining the second ray tracing simulation with the first ray tracing simulation and the image source simulation to the geometrical acoustics simulation.75. The computer implemented method according to any one of the items 68-74, wherein the method comprises the step of determining a first subpart of the first part of the propagation of sound using the first acoustic ray tracing method and the step of determining a second subpart of the first part of the propagation of sound using the second acoustic ray tracing method.76. The computer implemented method according to any one of the items 68-75, wherein the computer implemented method is further modified for high performance computing implementation, such as parallelizing propagation of each ray or parallelizing a first and a second frequency range.77. The computer implemented method according to any one of the items 68-76, wherein the method further comprises the step of extracting a geometrical spatial impulse response based on the geometrical acoustics simulation.78. The computer implemented method according to any one of the items 68-77, wherein the method further comprises the step of preparing the geometrical acoustics simulation for one or more output format(s), such as visual and/or graphical and/or numerical, and/or audible output format(s).79. The computer implemented method according to item 78, wherein the graphical output comprises visualising at least a part of the geometrical acoustics simulation in a cutting plane of the 3D model of a virtual space.80. The computer implemented method according to any one of the items 68-79, wherein the computer implemented method is configured to rely on impedance of the boundaries of an air volume.81. The computer implemented method according to any one of the items 68-80, wherein the computer implemented method approximates diffraction of acoustic waves in the virtual domain.82. The computer implemented method according to any one of the items 68-81, wherein the 3D model comprises at least one directive sound source for emitting sound in a defined direction.83. A computer implemented method for acoustic simulation in a virtual domain, wherein the method comprises the steps of:(a) obtaining input data comprising at least one model of a virtual domain, at least one sound source in the virtual domain, and at least one acoustic property of the virtual domain;(b) obtaining a wave based solver for determining the wave based propagation of sound in the virtual domain based on the at least one sound source and the at least one acoustic property within a first acoustic frequency range;(c) obtaining a geometrical acoustics based solver for determining the ray based propagation of sound in the virtual domain based on the at least one sound source and the at least one acoustic property within a second acoustic frequency range; and(d) outputting the wave based propagation and the geometrical acoustics based propagation.84. The computer implemented method according to item 83, wherein the virtual domain is a one dimensional (1D) domain, a two dimensional (2D) domain or a three dimensional (3D) domain such as a three dimensional model (3D) of a virtual space.85. The computer implemented method according to item 83, wherein the first and second acoustic frequency ranges are different.86. The computer implemented method according to item 83 or 84, wherein the first acoustic frequency range are low-mid acoustic frequencies, such as 20 Hz-2 kHz, and the second acoustic range are high acoustic frequencies, such as 2 kHz-20 KHz.87. The computer implemented method according to item 83, wherein the first and second acoustic frequency ranges partly overlap.88. The computer implemented method according to item 83, wherein the first and second acoustic frequency ranges completely overlap.89. The computer implemented method according to any one of the items 83-88, wherein the wave based propagation and the geometrical acoustics based propagation are merged together.90. The computer implemented method according to any one of the items 83-89, wherein the lowest frequency of the second frequency range and/or the highest frequency of the first frequency range is manually selected by a user.91. The computer implemented method according to any one of the items 83-90, wherein the method comprises a method for applying a wave based solver according to any one of the items 26-52 and/or a method for applying a geometrical acoustic solver according to any one of the items 68-82.92. A method of training machine-learning driven audio algorithms comprising the steps of:obtaining input data comprising at least one model of a virtual domain, at least one sound source located in the at least one model of a virtual domain and at least one acoustic property of the at least one model of a virtual domain;performing at least one sound propagation simulation in a virtual space from the at least one sound source to at least one sound receiver located in the at least one model of a virtual domain;obtaining at least one sound propagation simulation output from the at least one sound propagation simulation; andtraining a machine-learning model for machine-learning driven audio algorithms with the at least one sound propagation simulation output.93. The method according to item 92, wherein the virtual domain is a one dimensional (1D) domain, a two dimensional (2D) domain or a three dimensional (3D) domain such as a three dimensional model (3D) of a virtual space.94. The method according to item 93, wherein the at least one 3D model comprises at least one internal surface, such as surfaces of at least one virtual object located in the 3D model.95. The method according to item 92, wherein the at least one sound receiver comprises a plurality of oriented receivers.96. The method according to item 95, wherein the plurality of oriented receivers are arranged at the location of the at least one sound receiver and wherein a first oriented receiver is oriented with a first angle and a second oriented receiver is oriented with a second angle.97. The method according to any one of the items 92-96, wherein the at least one sound source is at least one directive sound source.98. The method according to any one of the items 92-97, wherein the method comprises a step of obtaining at least one raw sound data.99. The method according to item 98, wherein the at least one raw sound data is an audible sound, such as a music and/or a human voice.100. The method according to item 98 or 99, wherein the at least one raw sound data does not comprise echo and/or reverberation.101. The method according to item 99, wherein the audible sound is comprised between 20 Hz and 20 KHz.102. The method according to any one of the items 98-101, wherein the at least one raw sound data is emitted by the at least one sound source in the 3D model of a virtual space.103. The method according to any one of the items 98-102, wherein the at least one sound propagation simulation determines at least one modified raw sound data captured/received at the at least one sound receiver.104. The method according to any one of the items 98-103, wherein the at least one modified raw sound data is the at least one raw sound data, which is convolved with a simulated impulse response obtained by the at least one sound propagation simulation.105. The method according to any one of the items 98-104, wherein the machine-learning model is trained by comparing the at least one raw sound data to the at least one modified raw sound data.106. The method according to any one of the items 98-105, wherein the machine-leaning model is trained by convolving the at least one raw sound data with a simulated impulse response obtained by the at least one sound propagation simulation.107. The method according to any one of the items 98-106, wherein the input data comprises a plurality of three-dimensional model of a virtual space, preferably three or more three-dimensional models of a virtual space, more preferably ten or more three-dimensional models of a virtual space.108. The method according to any one of the items 98-107, wherein the at least one sound source is substantially located at the same location as the at least one sound receiver.109. The method according to any one of the items 98-108, wherein the at least one sound propagation simulation has at least one simulation time.110. The method according to item 109, wherein the at least one simulation time is calibrated according to 3D model(s) properties.111. The method according to item 110, wherein the 3D model(s) properties comprises 3D model(s) geometry and/or size.112. The method according to any one of the items 92-111, wherein the method is configured to be used for speech recognition, echo cancelling, blind source separation, blind room response modeling or feature extraction.113. The method according to any one of the items 92-112, wherein the at least one sound propagation simulation is configured to use the solver(s) and/or the computer-implemented method(s) according to items 1-91.114. A method of determining a head-related transfer function comprising:(a) obtaining a geometry of a user's head;(b) performing a simulation of sound propagation from at least one audio source to the geometry of the user's head, wherein the simulation of sound propagation is based on a sound propagation simulation using a wave-based solver; and(c) determining a head-related transfer function (HRTF) for the user's head based on the simulation of sound propagation.115. The method according to item 114, wherein the geometry of a user's head comprises at least one ear geometry.116. The method according to any one of the items 114-115, wherein the wave-based solver is a discontinuous Galerkin finite element method-based solver.117. The method according to any one of the items 114-116, wherein the wave-based solver determines the simulation of sound propagation simulation within an acoustic frequency range.118. The method according to item 117, wherein the acoustic frequency range is comprised between 20 Hz and 20 KHz.119. The method according to any one of the items 114-118, wherein the wave-based solver determines the simulation of sound propagation within a first acoustic frequency range.120. The method according to item 119, wherein the first acoustic frequency range is comprised between 20 Hz and 200 Hz.121. The method according to any one of the items 114-120, wherein the wave-based solver determines the simulation of sound propagation within a second acoustic frequency range.122. The method according to item 121, wherein the second acoustic frequency range is comprised between 200 Hz and 2 KHz.123. The method according to any one of the items 114-122, wherein the wave based solver determines the simulation of sound propagation within a third acoustic frequency range.124. The method according to item 123, wherein the third acoustic frequency range is comprised between 20 Hz and 20 kHz, preferably between 20 Hz to 6 kHz, more preferably between 20 Hz to 10 KHz.125. The method according to any one of the items 114-124, wherein the simulation of sound propagation using a wave-based solver is configured to use the wave-based solver according to any one of the items 1-26 and/or the computer implemented method according to any one of the items 27-52.126. A computer-implemented method of generating a binaural auralization at a listening point, wherein the method comprises the step of:(a) determining a head-related transfer function (HRTF) for a user's head according to any of the items 114-125;(b) receiving a 3D model of a domain, a position of at least one sound source in the domain, and acoustic properties of a plurality of boundaries in the 3D model of the domain;(c) arranging an array of secondary receivers around the listening point;(d) executing a wave based solver for determining a wave impulse response of the wave based propagation of an impulse emitted at the at least one sound source in the 3D model of the domain and received at the listening point within a first acoustic frequency range;(e) determining wave density coefficients based on a projection against spherical harmonics basis functions at the listening point;(f) generating a binaural auralization at the listening point, by evaluating a spatial rendering equation, wherein the spatial rendering equation combines the wave density coefficients with the HRTF.127. The computer-implemented method according to item 126, wherein the method further comprises the step of convolving the binaural auralization with an anechoic sound, thereby generating a rendered sound at the listening point.128. The computer-implemented method according to any one of item 126 or 127, wherein the array of secondary receivers is an open-sphere array.129. The computer-implemented method according to item 128, wherein the secondary receivers are arranged on the open-sphere array.130. The computer-implemented method according to any one of the items 126-129, wherein the secondary receivers are directional receivers.131. The computer-implemented method according to item 130, wherein the directional receivers are directional receivers with a first-order cardiod directivity pattern.132. The computer-implemented method according to any one of the items 126-131, wherein the method further comprises the step of executing a geometrical acoustics based solver for determining a geometrical acoustic impulse response of a ray based propagation of the impulse emitted at the at least one sound source in the 3D model of the domain and received at the listening point within a second acoustic frequency range.133. The computer-implemented method according to item 132, wherein the method further comprises a step of generating the impulse response by combining the wave impulse response and the geometrical impulse response.134. The computer-implemented method according to item 132 or 133, wherein the method further comprises a step of determining geometrical acoustics spherical harmonics coefficients from the at least one sound source to the listening point.135. The computer-implemented method according to item 132, 133 or 134, wherein the method further comprises a step of combining the geometrical acoustics spherical harmonics coefficients and the wave density coefficients, thereby obtaining hybridized spherical harmonics coefficients within the first and the second frequency range.136. The computer-implemented method according to any one of items 126 to 135, wherein the method further comprises a step of rotating a HRTF reference in order to establish a given orientation at the listening point.137. The computer-implemented method according to any one of the items 126-136, wherein the first and the second frequency range are the first and the second frequency range according to any one of items 114-125.138. A computer implemented method generating an impulse response for a listening point in a room, wherein the method comprises the steps of:(a) receiving a 3D model of the room, the position of at least one sound source in the room, and acoustic properties of the boundaries in the 3D model of the room;(b) executing a wave based solver for determining a wave impulse response of the wave based propagation of an impulse emitted at the least one sound source in the 3D model of the room and received at the listening point within a first acoustic frequency range;(c) executing a geometrical acoustics based solver for determining a geometrical impulse response of the ray based propagation of an impulse emitted that the at least one sound source in the 3D model of the room and received at the listening point within a second acoustic frequency range; and(d) generating the impulse response by combining the wave impulse response and the geometrical impulse response.139. A computer implemented method for generating an impulse response for a listening point in a room, wherein the method comprises the steps of:(a) receiving a 3D model of the room, the position of at least one sound source in the 3D model of the room, and acoustic properties of at least one boundary in the 3D model of the room;(b) using a wave based solver for determining a wave based impulse response of a wave based propagation of an impulse emitted at the at least one sound source in the 3D model of the room and received at the listening point within a first acoustic frequency range;(c) using a geometrical acoustics based solver for determining a geometrical impulse response of the ray based propagation of an impulse emitted at the at least one sound source in the 3D model of the room and received at the listening point within a second acoustic frequency range; and(d) generating the impulse response by merging the wave impulse response and the geometrical impulse response.140. The computer implemented method according to item 139, wherein the first and second acoustic frequency ranges partly overlap.141. The computer implemented method according to item 139 or 140, wherein the virtual domain comprises at least one directive sound source for emitting sound in a defined direction.142. The computer implemented method according to any one of items 139-141, wherein the computer implemented method further comprises performing a mesh model of the 3D model of the room, wherein the 3D mesh model is a 3D curvilinear mesh model.143. The computer implemented method according to any one of items 139-142, wherein the wave based solver applies a discontinuous Galerkin finite element method (DGFEM) or a spectral element method (SEM).144. The computer implemented method according to any one of the items 139-143, wherein the method further comprises a calibration step, wherein the power level of the at least one sound source is adjusted such that the sound level received at a predetermined distance from the at least one sound source is the same in the wave based solver and in the geometrical acoustic solver.145. The computer implemented method according to any one of the items 139-144, wherein an upper frequency of the first acoustic frequency range and a lower frequency of the second frequency range overlap at a transition frequency.146. The computer implemented method according to item 145, wherein the step of merging the wave based impulse response and the geometrical impulse response comprises applying a low pass filter to the wave based impulse response.147. The computer implemented method according to item 145 or 146, wherein the step of merging the wave based impulse response and the geometrical impulse response comprises applying a high pass filter to the geometrical impulse response.148. The computer implemented method according to item 146 or 147, wherein the low pass filter and/or the high pass filter comprises a cut off frequency at the transition frequency.149. The computer implemented method according to any one of the items 139-148, wherein the wave based solver and/or the geometrical acoustic solver comprises extracting one or more wave based impulse response(s) and/or one or more geometrical impulse response(s) based on the simulation of the propagation of sound and wherein the wave based impulse response(s) is/are spatial impulse response(s).150. The computer implemented method according to item 149, wherein the spatial impulse response(s) comprises a plurality of single channel impulse responses, wherein each one of the plurality of single channel impulse responses records the wave impulse response from a specific direction or angle at a same listening point.151. The computer implemented method according to any one of the items 139-150, wherein a spherical receiver array is arranged around the listening point, wherein the spherical receiver array comprises a plurality of receivers.152. The computer implemented method according to item 151, wherein the spherical receiver array is an open spherical array of cardioid receivers.153. The computer implemented method according to item 151 or 152, wherein the spherical receiver array comprises at least 2 receivers, preferably at least 4 receivers, more preferably at least 8 receivers, even more preferably at least 16 receivers, most preferably at least 32 receivers, even most preferably at least 64 receivers.154. The computer implemented method according to any one of items 151-153, wherein the number of receivers is determined based on the maximum truncation order N, such that the number of receivers is higher or equal to (N+1)2.155. The computer implemented method according to any one of items 139-154, wherein the computer implemented method further comprises convolving the generated impulse response with a base audio signal such that a convolved audio signal is generated.156. The computer implemented method according to any one of items 139-155, wherein the computer implemented method further comprises rendering a base audio signal by convolving the base audio signal with the generated impulse response, thereby creating a rendered audio signal.157. The computer implemented method according to item 156, wherein the base audio signal is a speech, a music, an environmental sound, an impulse sound or any combinations thereof.158. The computer implemented method according to any one of items 155-157, wherein the rendered audio signal provides an audio rendering of the base audio signal in the 3D model of the room at the listening point.159. A system for generating an impulse response for a listening point in a room, the system comprising:a computer system having a processor coupled to a memory, the processor configured to:receive a 3D model of the room, the position of at least one sound source in the 3D model of the room, and acoustic properties of at least one boundary in the 3D model of the room;determine, using a wave-based solver, a wave-based impulse response of a wave-based propagation of an impulse emitted at the at least one sound source in the 3D model of the room and received at the listening point within a first acoustic frequency range;determine, using a geometrical acoustics-based solver, a geometrical impulse response of a ray-based propagation of an impulse emitted at the at least one sound source in the 3D model of the room and received at the listening point within a second acoustic frequency range; andgenerate the impulse response by merging the wave impulse response and the geometrical impulse response.160. A computer implemented method for training a machine learning model for audio compensation, wherein the method comprises the steps of:receiving 3D models of a plurality of rooms, each of the 3D models comprising at least one sound source and at least one acoustic property,receiving a plurality of impulse responses at a listening position in each of the plurality of rooms,training the machine learning model for audio compensation using at least the plurality of impulse responses as input.161. The computer implemented method according to item 160, wherein the plurality of impulse responses are pre-processed to generate a plurality of modified impulse responses for training the machine learning model.162. The computer implemented method according to item 161, wherein the pre-processing comprises applying a filter to enhance a voice range in the impulse response.163. The computer implemented method according to item 162, wherein the voice range is between 3 kHz-17 kHz or between 350 Hz-17 kHz.164. The computer implemented method according to any one of the items 160-163, wherein the method further comprises providing a plurality of reverberating audio signals using the plurality of reverberating audio signals as input for training the machine learning model.165. The computer implemented method according to item 164, wherein the reverberating audio signal is provided by convolving each of the plurality of impulse responses with a base audio signal.166. The computer implemented method according to item 164, wherein the reverberating audio signal is provided by recording an audio signal received at an at least one speaker of an audio device.167. The computer implemented method according to any one of the items 160 to 166, wherein the method further comprises using the 3D models of the plurality of rooms as input for training the machine learning model.168. The computer implemented method according to any one of the items 160-167, wherein the method further comprises receiving a digital model of the audio device, and using the digital model of the audio device as an input for training the machine learning model.169. The computer implemented method according to any one of the items 160-168, wherein the at least one preferred listening position in each of the plurality of rooms is used as input for training the machine learning model.170. The computer implemented method according to any one of the item 160-169, wherein training the machine learning model comprises using the plurality reverberating audio signals according to item 164, 165 or 166 as input, wherein the training comprises reestablishing a base audio signal as an output.171. The computer implemented method according to item 170, wherein the 3D models of the plurality of rooms is used as input and the at least one preferred listening point in each of the plurality of rooms is used as input, wherein the method further comprises reestablishing the base audio signal at the at least one preferred listening point as an output.172. The computer implemented method according to any one of the items 160-171, wherein training the machine learning model comprises generating a compensation impulse response as an output.173. The computer implemented method according to item 172, wherein generating the compensation impulse response is based on the reverberation audio signals according to item 164, 165 or 166 and a base audio signal as an input for training the machine learning model.174. The computer implemented method according to item 172 or 173, wherein the 3D models of the plurality of rooms is used as input and the at least one preferred listening point in each of the plurality of rooms is used as input, wherein the method further comprises generating the compensation impulse response at the at least one preferred listening point as an output.175. The computer implemented method according to any one of the item 160-174, wherein the method further comprises that receiving the plurality of impulse responses comprises generating the impulse responses according to the method of item 139-158 for each 3D model of the plurality of rooms.176. The computer implemented model according to any one of the items 160-175, wherein the method further comprises receiving a digital model of the audio device.177. The computer implemented method according to any one of the items 160-176, wherein training the machine learning model comprises a neural network.178. The computer implemented method according to item 177, wherein the neural network comprises an auto encoder for encoding any of the inputs to the model or for generating a compressed input for use in training the machine learning model for audio compensation.179. The computer implemented method according to item 177 or 178, wherein the neural network further comprises training a general adversarial network (GAN) for generating any one of the input for training the machine learning model for audio compensation.180. The computer implemented method according to item 177, 178 or 179, wherein the neural network comprises a deep neural network, a convolutional neural networks and/or transformer for training the machine learning model for audio compensation.181. A method for providing a machine learning model for audio compensation in an audio device, wherein the audio device comprises at least one microphone, wherein the machine learning model is trained by the method according to any of the items 160-180, and wherein the method comprises receiving an audio signal at the at least one microphone and generating a compensated audio signal by using the machine learning model for audio compensation.182. The method for providing a machine learning based audio compensation in an audio device comprising at least one microphone, wherein the machine learning based audio compensation has been trained using a computer implemented method for training a machine learning model according to any one of the items 160-180, wherein the method for providing a machine learning based audio compensation comprises receiving an audio signal at the at least one microphone and generating a compensated audio signal by using the trained machine learning model on the received audio signal.183. The method according to item 181 or 182, wherein the compensated audio signal is compensated by convolving the received audio signal with the compensation impulse response of item 172, 173 or 174.184. An audio device comprising at least one microphone, wherein the audio device comprises a processing system for applying a machine learning based audio compensation according to the method of any one of items 181-183, wherein the audio device is configured to receive an audio signal at the at least one microphone and applies the machine learning based audio compensation to the audio signal to generate a compensated audio signal.185. The audio device according to item 184, wherein the audio device comprises a communication module configured to transmit the compensated audio signal.186. The audio device according to item 184 or 185, wherein the audio device is configured to transmit the compensated audio signal to a cloud, an internet and/or a network storage center.187. The audio device according to item 184, 185 or 186, wherein the audio device is further configured to transmit the compensated audio signal to a remote audio device, wherein the remote audio device comprises at least one remote speaker for outputting the compensated audio signal.188. A system for training a machine learning model for audio compensation, the system comprising:a computer system having a processor coupled to a memory, the processor configured to:receive 3D models of a plurality of rooms, each of the 3D models including at least one sound source and at least one acoustic property;receive a plurality of impulse responses at a listening position in each of the plurality of rooms; andtraining the machine learning model for audio compensation using at least the plurality of impulse responses as input.189. A computer implemented method of determining a head-related transfer function comprising:(a) receiving a 3D model of a user's head and the position of at least one sound source representing the ear drum or an approximation thereon in the 3D model;(b) using a wave based solver for determining a plurality of wave based impulse responses from an impulse emitted at the at least one sound source, wherein the plurality of wave based impulse responses is determined at a plurality of digital representation of head receivers; and(c) determining a head-related transfer function (HRTF) for the user's head based on the plurality of wave based impulse responses being determined at the plurality of digital representation of head receivers.190. The computer implemented method according to item 189, wherein the wave-based solver uses a discontinuous Galerkin finite element method (DGFEM) or a spectral element method (SEM).191. The computer implemented method according to any one of items 189-190, wherein the computer implemented method further comprises obtaining a head mesh model representing a geometry of the user's head.192. The computer implemented method according to item 191, wherein the head mesh model is a curvilinear head mesh model.193. The computer implemented method according to any one of items 189-192, wherein the computer implemented method further comprises arranging a digital representation of a head array comprising the plurality of digital representations of head receivers around the head mesh model, such that the distance between any of the digital representations of the head receivers and the head mesh model is not below a predetermined distance.194. The computer implemented method according to any one of items 189-193, wherein the computer implemented method further comprises determining on the head mesh model a first closest mesh element, which is closest to the ear drum.195. The computer implemented method according to any one of items 189-194, wherein the computer implemented method further comprises arranging a digital representation of a first source correction microphone located at a first source distance from the first closest mesh element, wherein the first source distance is smaller than the predetermined distance.196. The computer implemented method according to any one of items 189-195, wherein the computer implemented method further comprises digitally emit a first impulse signal using the first closest mesh element as a sound source.197. The computer implemented method according to any one of items 189-196, wherein the computer implemented method further comprises determine a first source correction signal using the wave-based solver, wherein the first source correction signal describes the first impulse signal as received at the first source correction microphone.198. The computer implemented method according to any one of items 189-197, wherein the computer implemented method further comprises determine a plurality of first source corrected head impulse responses by source correcting each of the plurality of wave based impulse responses using the first source correction signal.199. The computer implemented method according to any one of items 189-198, wherein the computer implemented method further comprises generate the head related transfer function of the user's head for the first ear drum by combining the plurality of first source corrected head impulse responses.