EP3348079A1

EP3348079A1 - Method and system for developing a head-related transfer function adapted to an individual

Info

Publication number: EP3348079A1
Application number: EP16736088.2A
Authority: EP
Inventors: Slim GHORBAL; Renaud Seguier; Xavier Bonjour
Original assignee: 3d Sound Labs
Current assignee: Mimi Hearing Technologies GmbH
Priority date: 2015-09-07
Filing date: 2016-07-05
Publication date: 2018-07-18
Anticipated expiration: 2036-07-05
Also published as: US20180249275A1; CN108476369B; EP3348079B1; WO2017041922A1; FR3040807B1; CN108476369A; FR3040807A1; US10440494B2

Abstract

Method for developing a head-related transfer function (Sj) adapted to an individual, with the help of a database (OHi) comprising 3D or 2D ear data (0i) and corresponding head-related transfer functions (Hi), the method comprising the steps consisting in: - performing a statistical analysis (S2) of the 3D or 2D ear space of the database; - performing a statistical analysis (S3) of the head-related transfer space of the database; - performing an analysis of the links (S4) between parameters of the statistical analysis of the 3D or 2D ear space and parameters of the statistical analysis of the head-related transfer function space; and - determining (S5), with the help of said analysis of the links and of said statistical analysis of the 3D or 2D ear space, a function (OHi) for calculating a head-related transfer function (S j) with the help of data representative of at least one ear.

Description

Method and system for developing a head related transfer function adapted to an individual

The invention relates to a method and a system for generating a head-related transfer function adapted to an individual.

The present invention relates to the personalization of sound spatialization methods, also known as binaural listening. More particularly, it is a method of individualization of transfer functions related to the head or "Head-Related Transfer Functions" in English, acronym HRTF, pillars of the three-dimensional hearing of any individual.

Binaural listening is a field of research aimed at understanding the mechanisms that allow humans to perceive the spatial origin of sounds. Starting from the assumption that this origin is determined by the morphology of each, binaural listening states that the position and shape of the ears of an individual are key elements. These last effect indeed as frequency and directional filters on the sounds reaching us.

Although the relationship between morphology and hearing has long been studied, for almost a quarter of a century there has been a growing interest in the scientific community for the problem of individualization, that is to say of taking into account the specificities of each.

In particular, attention has been focused on the individualization of head-related transfer functions or HRTFs, mathematical representations of the frequency pattern of sounds we perceive. Frequency coloration is understood to mean the spectral power density variations of the sound signals. Spectra of white, pink or gray noises are examples. Many methods are now known that can be classified into two large families: synthetic methods, which aim at calculating or recreating games or sets of HRTFs, and adaptive methods, which seek to discover, among a given set and the possible price of minor transformations, the transfer function best suited to an individual.

Among the methods of synthesis, one can first distinguish exact calculations from statistical and probabilistic approaches.

Developed for more than twenty years, the family of finite element methods aims to model and then solve the problem with partial derivatives posed by the propagation of sound from the source to the subject's eardrums. This family includes the variants known under the English names: "Direct Boundary Element Method", acronym DBEM, "Indirect Boundary Element Method" acronym IBEM, "ln_nite- Finite Element Method" acronym IFEM, or "Fast -Multipole Boundary Element Method "acronym FM-BEM.

Known as offering exact solutions to the problem dealt with, these methods nevertheless suffer from some notable handicaps. First of all, they require the data of a 3D mesh of the subject all the finer that one wishes to calculate the HRTFs in the high frequencies, and that the calculation time becomes quickly prohibitive as one refines the mesh (and therefore that one wishes to have reliable results in the high frequencies). High frequencies are frequencies above 4 kHz. Finally, the physical modeling of the problem requires introducing a lot of priori and approximations. Thus, each surface is assigned a proper impedance (reflecting absorption / reflection phenomena) whose value is empirical. Similarly, the hair is classically modeled by an impedance surface different from the skin, thus not taking into account its volume nature.

An alternative approach to the direct calculation of HRTFs consists, from a representative set of real HRTFs, of showing the main modes of variation. This is particularly the work of Sylvain Busson

("Individualization of Acoustic Indices for Binaural Synthesis"; PhD thesis, University of the Mediterranean-Aix-Marseille II, 2006.) on Artificial Neural Networks (ANN). The idea developed is to make a prediction of HRTFs from the measurement of a limited number of them. This includes the joint use of a Kohonen map and an Ascending Hierarchical Classification, acronym CHA, prior to the election of representative HRTFs. Subsequently, a Multi Layer Perceptron neuron network in the English language, acronym MLP, with three layers, is constructed and the representative HRTFs of 44 subjects of the CIPIC database used as a learning set. Although promising, this study does not manage to release universal representatives, ie common to all individuals, nor does it show any psychoacoustic validation of the results. In addition, it is also necessary to have a means of access to said representatives.

The statistical methods for the synthesis of HRTFs may, alternatively, be based on the principal component analysis, of acronym ACP.

Kistler and Wightman (The Journal of the Acoustical Society of America, 91 (3): 1 637-1 647, 1992) were the first to propose to break down the HRTFs according to this method. All HRTFs are then seen as a vector subspace of the measurement space. The knowledge of a base of this subspace then makes it possible to reach any representative, ie any HRTF, by simple linear combination of the basic vectors. This is what ACP allows by providing an orthonormal basis of the space generated by learning HRTFs. The final step in solving the problem of individualization then consists in making the link between the morphological parameters of the individuals and the reconstruction coefficients by the eigenvectors of the database. For this, multiple linear regressions are conventionally used. Based on the work of Kistler & Wightman, Xu and associates (Song Xu, Zhizhong Li, and Gavriel Salvendy, "Acoustical Science and Technology, 29 (6): 388 {390 , 2008.) proposed to group the HRTFs of different individuals measured according to the direction (azimuth, elevation) pointed before PCR (one per group), thus hoping to reduce the estimation error.

Zhang and associates (RA Kennedy, M. Zhang and TD Abhayapala, "Statistical method to identify key anthropometry parameters in hrtf individualization", in addition, proposed a statistical method. estimating the most relevant anthropomorphic parameters to perform the regression step.

In 2007, Vast Audio Pty Ltd filed a patent (G. Jin, P. Leong, J. Leung, S. Carlile, and A. Van Schaik, "Generation of customized three dimensional sound effects for individuals", April 24 2007, US 7209564) inspired by these ideas. In practice, the latter firstly describes the creation of a base of HRTFs and a base of morphological parameters. The use of a statistical analysis method is then invoked to break down parameter spaces and HRTFs into elementary components, in the same way that ACP allows. Subsequently, using another method of statistical analysis, the links between the reconstruction coefficients of the morphological parameters and those of the HRTFs are determined. Each variant proposed so far has generally improved the results of the previous methods without, however, offering a satisfactory performance from a psycho-acoustic point of view, ie under real conditions. In particular, the number and location of morphological parameters needed are very imprecise. Moreover, in the case of simultaneous analysis of the morphology and the HRTFs, the discovery of the links between the coefficients of the two spaces is all the more complex as the data are left raw. Another type of synthesis method, notable for its innovative nature, is the reconstruction of HRTFs according to a Bayesian approach. Presented by Hofman & Van Opstal (Paul M Hofman and John Van Opstal, Bayesian, "Reconstruction of the localization of responses to random spectra", Biological cybernetics, 86 (4): 305-31 6, 2002), who wants to recreate potential HRTFs from a probabilistic analysis of the subjects' responses to specific stimuli. More specifically, the idea is to make subjects listen to sounds convoluted by filters mimicking the types of variations observable in real HRTFs and broadcast by a speaker located right in front of them. The instruction given is to direct the gaze in the direction from which the sound seems to come.

Although innovative, this method has many constraints that work against it such as the time required for experimentation or the impossibility of addressing the HRTFs out of the field of view, the subject being forced to designate the directions of the HRTFs. where the sounds seem to come from.

While the methods of synthesis mentioned above aim to create new games of HRTFs (without even having ever observed real ones, as is the case for finite element methods), the adaptive methods aim, on the contrary , to stay closer to the existing. The underlying idea is to perform measurements on real subjects to get HRTFs games that are at least adapted to a person. They therefore necessarily contain enough location indices to be usable, which synthetic methods can not promise.

Selective methods do not cause any alteration of the measurements; their common principle is the election of one set of HRTFs among several according to certain criteria. The latter are most often psychoacoustic, without being limited to it. Among the psychoacoustic criteria, Shimada et al.'S work (Shoji Shimada, Nobuo Hayashi, and Shinji Hayashi, "A clustering method for sound localization functions ", Journal of the Audio Engineering Society, 42 (7/8): 577-584, 1994), with a consequent base of HRTFs, the latter intend to achieve groupings between similar HRTFs. a cepstral composition of 1 6 coefficients The Euclidean distance naturally associated with this space with 1 6 dimensions then allows the grouping of the HRTFs in classes (8 in number), HRTFs games are then randomly selected within the classes and the subjects invited to elect the class or classes that offer the best impression of outsourcing and directivity More recently, we can refer to the work of Tame and associates (Robert P Tame, Daniele Barchiese, and Anssi Klapuri; Improved localization and externalization of nonindividualized hrtfs by cluster analysis, "in Audio Engineering Society Convention 133, Audio Engineering Society, May 2012.) or those of Xie and Associates (Bosun Xie and Zhao jun Tian; "Improving binaural reproduction of 5.1 channel surround sound using individualized hrtf cluster in the wavelet domain", in Audio Engineering Society Conference: 55th International Conference: Spatial Audio, Audio Engineering Society, August 2014.) which use respectively Gaussian and a wavelet decomposition to bring together the HRTFs.

Once the class (or cluster in English language) selected, another selection step can be added to select a specific game. Again, multiple methods have been published. Thus, Y. Iwaya (Yukio Iwaya, "Individualization of head-related transfer functions with tournament-style list ning test: Listening with other ears," Acoustical science and technology, 27 (6): 340-343, 2006.) describes a selection procedure of a set of HRTFs out of 32 available using the principle of chess tournaments. A sound path in the horizontal plane is simulated by convolving a pink noise with HRTFs games. A pink noise is a noise whose sound power is constant for a given frequency bandwidth in a logarithmic space (eg the same power output on the 40-60Hz band as on the 4000-6000Hz band). 32 trajectories are thus obtained and put in competition. At each meeting, the subject declares winner one of the two trajectories according to whether it looks the most or not to the set path. The outgoing winner of the tournament is said to be the most suited to the subject.

Another approach, by Seeber and Associates (Bernhard U Seeber and Hugo Fastl, "Subjective selection of non-individual head-related transfer functions", July 2003), presents a two-step selection of a game among 12. The objective is to be fast without prior training while providing a result that minimizes the impression of his intra-cranial. The first step is to designate the 5 games with a better rendering in terms of spatialization in the frontal zone. The second consists of eliminating 4 depending on whether they sin to reproduce different behaviors such as moving a sound source at constant speed, constant elevation or constant distance. Ten minutes is required to complete the procedure.

Finally, the work of Martens (William L Martens, "Rapid psychophysical calibration using bisection scaling for individualized control of source elevation in auditory display" in Proc., Int., Conf. On Auditory Display, pages 199-206, July 2002 ) known as scaling bisection. The idea is to create, using a psycho-acoustic test, a correspondence table between the real directions associated with a set of HRTFs and the directions perceived by the subject. In practice, for a given azimuth it is necessary to find the HRTF corresponding best to the sensation of an elevation at 45 °. The extremal elevations (0 ° and 90 °) being supposed correctly perceived, a polynomial interpolation of the second order is then operated to build the table evoked above. Other protocols have been proposed by the scientific community but none can avoid the drawbacks inherent in this type of methodology. Indeed, even if the objective is not to find the exact HRTFs of the subject (it would be necessary to appeal to the methods of synthesis) but to select or to adapt as best as possible to the existing one, it does not remain unless the quality of the best possible solution is always limited by the variability of HRTFs games open for selection. Thus, for a given protocol, the results are even better than the input database is important. But the increase of the latter lengthens the duration experimentation, which is all the more embarrassing because it relies on the active participation of the subject.

Emphasizing the importance of each one's own morphology, Zotkin et al. (DN Zotkin, J. Hwang, R. Duraiswaini, and LS Davis; "Hrtf personalization using anthropometry measurements", in Applications of Signal Processing to Audio and Acoustics , 2003 IEEE Workshop on, pages 157-160, Oct. 2003.) describe the ear through seven morphological parameters measurable on a profile view of the ear. These parameters allow you to define a distance between individuals that is used to select the nearest neighbor in the CIPIC database of a given subject. It should be noted that the HRTFs thus selected have subsequently been modified for frequencies below 3 kHz. Indeed, for the low frequencies (f <500Hz), a model Head and Torso, of acronym HAT for "Head-And-Torso" in English language is used to synthesize the HRTFs. Between 500 Hz and 3 kHz, an affine recollement is operated to progressively move from synthetic HRTFs to selected HRTFs.

In 2001, the company Arkamys and the CNRS filed a patent (BF Katz and D. Schônstein, "Method for selecting perceptually optimal hrtf filters in a database from morphological parameters", WO201 1 128583) relating to a method of morphological selection. The idea is to build three databases. The first contains the HRTFs of a set of individuals, the second contains a set of morphological parameters of these individuals and the third contains the listening preferences of these individuals, ie for each subject, the classification he makes of HRTFs. from the first base. Once this is done, a study of the correlations between the second and third databases is performed to rank the morphological parameters in order of importance. On the side of the HRTFs, a dimensional analysis of the space is carried out (for example a PCA) to obtain a base in which they become representable. The links between K most important morphological parameters and the coordinates of HRTFs in the aforementioned space are then calculated, establishing a link between morphology and HRTFs. Given a new individual, the measurement of K morphological parameters brought to light previously allows then to position itself in the space of the HRTFs. The nearest neighbor in the base is searched for and is the result of customization.

The problem encountered by previous methods using morphological parameters, namely, to define their number and their location. Indeed, the notion of the height of an ear, for example, is not natural and its measurement will be very dependent on the subjectivity of the experimenter who will first of all have to determine whether the ear should be turned and where locate his "lowest" and "high" points. Moreover, the question arises of the criteria of definition of the distance used because it is from this last that depends the result of the selection.

Finally come the methods of selection adapted, whose most explicit representative is probably the frequency scaling or "Frequency Scaling" in English, introduced by Middlebrook (John C Middlebrooks, "Virtual localization improved by scaling nonindividualized external -ear transfer functions in frequency ", The Journal of the Acoustical Society of America, 106 (3): 1493-1510, 1999); this operation is based on the idea that the interaction of a sound wave of given frequency with a solid depends on the dimensions of the latter. In particular, any homothety performed on the object must be accompanied, if one always wishes to observe the same interaction, a homothety of inverse ratio on the frequency. Applied to individualization, this idea amounts to saying that by knowing the HRTFs of a reference individual (or even a manikin) and the scale ratio ("scaling factor" in English Ingue) between the morphology of this reference and that of a subject to individualize, it is possible to improve the feeling of location provided by the reference HRTFs by applying a reverse ratio scaling.

In parallel with frequency scaling or "Frequency Scaling", Maki and Furukawa (Katuhiro Maki and Shigeto Furukawa), "Reducing individual differences in the external-ear transfer functions of the Mongolian gerbil," The Journal of the Acoustical Society of America, 1 18 (4), 2005) have shown that, starting from the angle data between a reference earlobe and a test flag, a rotation of the coordinate system giving the direction of HRTFs can significantly reduce inter-individual differences. In other words, this method uses the fact, by restricting it to the ear flag, that a rotation of the subject induces the same rotation at the measured HRTFs. These approaches, however useful they may be, can not in themselves constitute complete customization processes. This would reduce the variability of HRTFs to only 1 or 2 parameters. However, they can be seen as good complements to other methods. Despite the multiplicity of known approaches to personalize binaural listening, none has yet managed to stand out clearly from others by its efficiency and simplicity. In addition, problems can arise as prohibitive personalization times or a lack of reliability solutions, if not both simultaneously.

An object of the invention is to develop a head-related transfer function (HRTF) adapted to an individual with improved speed and reliability.

In the remainder of the description, the term "ear data", "ear space" or "ears" means 2D photos of ears or 3D ears represented by a 3D point cloud describing the surface of the hear.

Also, it is proposed, according to one aspect of the invention, a method of developing a head-related transfer function or HRTF adapted to an individual, from a database comprising ear data. 3D or 2D and corresponding transfer functions relating to the head, the method comprising the steps of:

- perform a statistical analysis of the space of the ears 3D or 2D, of the database;

perform a statistical analysis of the space of the transfer functions relating to the head, of the database; performing an analysis of the links between said statistical parameters of the 3D or 2D ear space and said statistical parameters of the space of the transfer functions relating to the head; and

determining, from said link analysis and said statistical analysis of the space of the 3D or 2D ears, a function of calculating a transfer function relating to a head from data representative of at least one ear .

Thus, the relationship between HRTFs and ear data being determined upstream, it is possible to use them in real-time applications. Moreover, the statistical nature of the analyzes makes it possible to dispense with the simplifications introduced by the physical models and the approximations that result from them. Of course, an HRTF is related to a direction of the space, and to recreate a complete auditory virtual environment, it is therefore necessary to have HRTFs for a significant number of directions, which makes it possible to do the present invention for any number desired directions. According to one embodiment, the method further comprises a step of densely matching, or "dense registration in English," points relating to respective positions of the ears of the database. implementation, the method further comprises a step of calculating a transfer function relating to the head, adapted to the individual, from said calculation function and from at least one photograph of at least one The use of the calculation function makes it possible to determine the transfer function in a time compatible with a real-time application.

According to one embodiment, said step of calculating a transfer function relating to the head is iterative. In one embodiment, said iterative step of calculating a transfer function relating to the head comprises:

a first iterative sub-step for estimating at least one setting parameter of the individual during said one or more photographs; and

a second iterative sub-step of estimation of optimized statistical parameters representing at least one ear of the individual in the space of the ears.

Thus, it is possible to reconstruct an ear in 3D from a photograph that does not require the user to take special precautions when taking the snapshot. According to one embodiment, said data representing 3D ears are point clouds.

Thus, the visualization and the study of the properties, particularly geometric properties, of the data are facilitated.

In one embodiment, said disclosed steps are used to develop a transfer function, for high frequencies above a threshold, relating to the head adapted to the individual, said method comprising, in addition, a step of development of a transfer function, for low frequencies below said threshold, relating to the head adapted to the individual.

Thus, each part of the frequency spectrum is adapted according to the physical structures that impact it the most.

According to one embodiment, said step of developing a transfer function, for low frequencies below said threshold, relating to the head adapted to the individual comprises the following substeps, consisting of: - to sample ranges of possible values of human morphological parameters from a database relating to human morphology,

determining a parametric model mesh of said morphological parameters,

calculating low frequency mask transfer functions, associated with said mesh,

estimating the value of the morphological parameters of the individual from at least one photo of the individual face or profile, and

calculating a transfer function, for low frequencies, relating to the head, adapted to the individual from the estimated value of the morphological parameters and said calculated functions of low frequency mask transfer. Thus, most calculations are conducted upstream, allowing the use of the process within applications in real time.

In one embodiment, a transfer function relating to the head of the individual is developed from said transfer functions respectively for high and low frequencies and said one or more photos of the individual face or profile , comprising the steps of:

- estimate, from said one or more photos of the individual face or profile, the size of ears relative to the rest of the body of the individual; - Frequency scaling the transfer functions relating to the head, for high frequencies; and

merging the transfer functions, respectively for high and low frequencies, to obtain the transfer function relating to the head of the individual.

For an individual, the photo of a single ear, may be sufficient, assuming symmetry of the ears of an individual, but alternatively, a better accuracy is obtained with photos of the two ears of an individual. It is also proposed, according to another aspect of the invention, a system for developing a transfer function relating to the head or HRTF adapted to an individual, from a database comprising data of ears. and corresponding transfer functions relating to the head, comprising a computer configured to implement the method according to one of the preceding claims.

The invention will be better understood from the study of some embodiments described by way of non-limiting examples and illustrated by the accompanying drawings in which Figures 1 to 4 schematically illustrate the method according to the invention.

In Fig. 1, an OHi database includes ear data Oi and corresponding transfer functions Hi relative to the head. "Corresponding" refers to the fact that for this database, the data representative of the ears of the people at the base, as well as their functions, are recorded for the individuals used to design the database. transfer relative to the head, keeping the link between the ear data and the corresponding transfer function of the database.

Oi data of ears can be point clouds. An optional step S1 makes it possible to closely match points relating to respective positions of the ears Oi of the database OH-i.

By dense matching is meant the specification of the correspondences between the constituent points of a cloud or the pixels of a 2D image of the ear and those constituting another cloud or other 2D ear image. For example, if the end of the lobe is represented by point 2048 on one ear and point 157 on another, the specification of this role equivalence constitutes a mapping. We can speak of equivalence class, all the points of the same class playing a similar role within their ear of belonging. It is possible to use only one ear, assuming a symmetry of the ears of a user.

A step S2 then makes it possible to perform a statistical analysis of the ear space O-i, of the OH-i database. This statistical analysis can be done using techniques using a sample ear basis and performing a dimension reduction (principal component analysis, independent component analysis, sparse or parsimonious type coding, self-coding neuron networks). . These techniques make it possible to convert the representation of a 2D or 3D ear (in the form of a cloud of points or pixels in an image) into a vector of restricted number statistical parameters.

A step S3 makes it possible to carry out a statistical analysis of the space of the transfer functions relating to the head H-i, of the database OH-i. This statistical analysis is of the same type as that described in the previous paragraph. It thus makes it possible to represent the HRTFs by a vector of statistical parameters of restricted number.

A step S4 makes it possible to perform an analysis of the links between said statistical parameters of the ear space of step S2 and said statistical parameters of the space of the transfer functions relating to the head of step S3.

Finally, a step S5 makes it possible to determine, from said link analysis of the step S4, and from said statistical analysis of the ear space of the step S2, a calculation function OH1 of a transfer function If relative to a head from data representative of at least one ear.

The statistical analyzes S2 and S3 must lead to the creation of parametric representations of the ears and transfer functions relating to the head. In particular, the training data of the database OHi must be able to be reconstructed from the outputs of the analysis. It is possible in particular to use, in the analysis steps S2 and S3, principal component analyzes of acronym ACP.

By way of example, when the PCA is chosen to carry out the size reduction, it consists in calculating, from a base of examples of the data to be analyzed, the eigenvectors which best represent these data in the sense of the least squares. The statistical parameters that represent the data to be analyzed (3D ear or 2D or transfer function relative to the head) are neither more nor less than the projection coefficients this projected data on the eigenvectors. Alternatively, any type of linear or non-linear dimensional analysis is suitable, provided that it meets the above-mentioned reconstruction requirement, such as independent component analysis methods, with ACI acronym, or sparse coding or "sparse" -coding "in the English language. The analysis of the links of the step S4 between the sets of statistical parameters of the ear space and the statistical parameters of the space of the transfer functions relating to the head, in a nominal configuration, can be done by linear regression multivariate on the values of the parameters used for the reconstruction of the training data of the database OHi.

Alternatively, any method making it possible to find the values of the parameter set of the transfer functions relating to the head from the values of the set of statistical parameters and ensuring a good reconstruction of the transfer functions relating to the head of the OH-database. i, as methods based on neural networks, based on multiple component analysis, ACM acronym, or partitioning in k-means.

As illustrated in FIG. 2, the method may furthermore comprise a calculation step S6 of a transfer function Si relative to the head, adapted to the individual, from said calculation function OH 1 and from less a photograph Ui of an ear of the individual. The calculation step S6 of a transfer function Si relative to the head may be iterative, and comprise a first iterative sub-step S7 for estimating at least one setting parameter of the individual during said one or more photographs, and a second iterative sub-step S8 of estimation of optimized statistical parameters representing at least one ear of the individual in the space of the ears.

Of course, the iterative computational step S6 of a transfer function Si relative to the head also then comprises a substep S6a for initializing or updating the statistical parameters of shape and of the setting parameters, as well as a sub-step S6b of convergence test of the calculation step S6 or reaching a limit number of iterations.

The first and second iterative substeps S7 and S8 of course each include a convergence test of the respective estimate or of reaching a limit number of iterations. The pose parameters referred to refer to the angles under which the user's ears are photographed.

The first and second iterative sub-steps S7 and S8 of estimation involve active models of appearance or "active appearance models" in English, acronym AAM. In a nominal configuration, they are based on the use of regression matrices.

Alternatively, it is possible to use any method to converge the 2D projection of the model to the user 2D images as AAMs based on gradient descent, genetic algorithms or simplex.

As illustrated in FIG. 3, said disclosed steps are used to develop a transfer function S _H , for high frequencies greater than a threshold, relating to the head adapted to the individual, said method comprising, in addition, a step of development of a transfer function S _B , for low frequencies below said threshold, relating to the head adapted to the individual. The step of developing a transfer function S _B , for low frequencies below said threshold, relating to the head, adapted to the individual comprises the following substeps, consisting of:

- sampling S9 possible ranges of human morphological parameters of a database M-ι relating to human morphology,

determining S10 a parametric model mesh of said morphological parameters,

calculating S1 1 of the low frequency mask transfer functions (M {) associated with said mesh,

estimating S12 the value of the morphological parameters of the individual from at least one U ₂ photo of the individual face or profile, and

calculating S13 a transfer function S _B , for low frequencies, relating to the head, adapted to the individual from the estimated value of the morphological parameters and said calculated functions of low frequency mask transfer.

The low-frequency mask transfer functions M [are calculated offline and serve as a reference base for transfer functions relating to the head at low frequencies (frequencies below a threshold, for example 2 kHz).

For example, it is possible to use a snowball or "snowbaN" model in English. As a variant, any parametric model with few inputs and making it possible to obtain a mesh of the head and the torso is suitable, such as a modeling of the head and the torso by ellipsoids of revolution.

For example, the macroscopic parameters may be the width of the shoulders and the diameter of the head. The choice of the parameters is dictated by the choice of the model used for the calculation of the templates.

As illustrated in FIG. 4, a transfer function relating to the head S 1 of the individual is elaborated from said transfer functions S _H , S _B , respectively for high and low frequencies and of said said photos U ₂ of the face or profile individual, comprising the steps of:

estimating S14, from said U ₂ photos or photos of the individual face or profile, the ear size of the individual;

use said estimated ear size of the individual to adjust the transfer functions relating to the head S _H to the most suitable frequency band according to the frequency scaling method in English, for high frequencies;

to merge S1 6 transfer functions S _H , S _B , respectively for high and low frequencies, to obtain the transfer function relative to the head Si of the individual.

The dimensions of the ear can be normalized, in which case it is necessary to rescaling the frequency spectrum generated for the ear.

Indeed, two ears identical to a scale factor have similar HRTFs opposite the same scaling factor. This is very important when working with a standard ear model and without information, at least at the beginning of the algorithm, on the actual dimensions of the subject's ear. Therefore, if the model reconstructs an ear 5 cm high where the subject's ear was 10 cm, it will compress the HRTFs by a factor of 0.5.

Alternatively, if the ears are not sized in size, scaling step 1 becomes pointless. The fusion of the two parts of the spectrum by their summation after application of a high-pass filter and a low-pass filter respectively to the high frequency spectrum and the low frequency spectrum.

The steps of the method described above may be performed by one or more programmable processors executing a computer program for performing the functions of the invention by operating on input data and generating output data. A computer program can be written in any form of programming language, including compiled or interpreted languages, and the computer program can be deployed in any form, including as a stand-alone program or as a subroutine, element or other unit suitable for use in a computing environment. A computer program can be deployed to run on one computer or multiple computers at a single site or spread across multiple sites and interconnected by a communications network.

The preferred embodiment of the present invention has been described. Various modifications can be made without departing from the spirit and scope of the invention. Therefore, other implementations are within the scope of the following claims.

Claims

1. Computer-implemented method for developing a head-related (Si) transfer function adapted to an individual from a database (OH-i) comprising 3D ear data (Oi) or 2D and corresponding transfer functions (Hi) relating to the head, the method comprising the steps of: performing a statistical analysis leading to a reduction in size (S2) of the space of the 3D or 2D ears of the base of data (OH-i) and represent each ear 3D or 2D by a vector of statistical parameters whose values of the components are the values of the projections of each ear in the space of the ears of reduced dimension;

performing a statistical analysis leading to a size reduction (S3) of the space of the transfer functions relating to the head, of the database (OH-i) and representing each transfer function by a vector of statistical parameters whose component values are the values of the projections of each transfer function in the space of the reduced-size transfer functions;

performing a link analysis (S4) between said statistical parameters of the 3D or 2D ear space and said statistical parameters of the space of the transfer functions relating to the head; and determining (S5), from said link analysis and said statistical analysis of the 3D or 2D ear space, a calculation function (OH) of a transfer function (Si) relating to a head from representative of at least one ear.

The method of claim 1, further comprising a step of densely matching (S1) points relating to respective positions of the ears of the database (OH-i);

The method according to claim 1 or 2, further comprising a calculation step (S6) of a transfer function (Si) relating to the head, adapted to the individual, from said calculation function ( OH ^) and at least one photograph (U1) of at least one ear of the individual.

The method of claim 3, wherein said step of calculating (S6) a transfer function (S-i) relating to the head is iterative.

The method of claim 4, wherein said iterative step of calculating a head-related transfer function comprises:

a first iterative sub-step (S7) for estimating at least one setting parameter of the individual during said one or more photographs; and

a second iterative sub-step (S8) for estimating optimized statistical parameters representing at least one ear of the individual in the space of the ears.

6. Method according to one of the preceding claims, wherein said data (O-i) representing ears are scatter plots.

7. Method according to one of the preceding claims, wherein said disclosed steps are used to develop a transfer function (S _H ), for high frequencies above a threshold, relating to the head adapted to the individual, said method further comprising a step of generating a transfer function (S _B ), for low frequencies below said threshold, relating to the head adapted to the individual.

The method according to claim 7, wherein said step of generating a transfer function (SB) for low frequencies below said threshold relating to the head adapted to the individual comprises the following substeps, consists in: sampling (S9) ranges of possible values of human morphological parameters of a database (Mi) relating to human morphology,

determining (S1 0) a parametric model mesh of said morphological parameters,

calculating (S1 1) low-frequency mask transfer functions associated with said mesh,

estimating (S1 2) the value of the morphological parameters of the individual from at least one photo (U ₂ ) of the individual from the front or from the profile, and

calculating (S1 3) a transfer function (S _B ), for low frequencies, relating to the head, adapted to the individual from the estimated value of the morphological parameters and said calculated functions of low frequency mask transfer.

9. The method of claim 8, wherein a transfer function relating to the head (Si) of the individual is developed from said transfer functions (S _H , S _B ), respectively for high and low frequencies and said one or more photos (U ₂ ) of the face or profile individual, comprising the steps of: estimating (S14), from said one or more photos (U ₂ ) of the individual face or profile, the size of the ears relative to the rest of the body of the individual;

scaling (S1 5) the head transfer functions (SH) for high frequencies; and

merging (S1 6) the transfer functions (SH, SB), respectively for high and low frequencies, to obtain the transfer function relating to the head (S-i) of the individual.

1 0. System for developing a head-related transfer function adapted to an individual, from a database comprising ear data and corresponding transfer functions relating to the head, comprising a calculator configured to implement the method according to one of the preceding claims.