EP1836876B1 - Verfahren und vorrichtung zur individualisierung von hrtfs durch modellierung - Google Patents

Verfahren und vorrichtung zur individualisierung von hrtfs durch modellierung Download PDF

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EP1836876B1
EP1836876B1 EP06709051.4A EP06709051A EP1836876B1 EP 1836876 B1 EP1836876 B1 EP 1836876B1 EP 06709051 A EP06709051 A EP 06709051A EP 1836876 B1 EP1836876 B1 EP 1836876B1
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directions
hrtfs
individual
model
measurements
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EP1836876A2 (de
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Rozenn Nicol
Sylvain Busson
Vincent Lemaire
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Orange SA
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Orange SA
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04S—STEREOPHONIC SYSTEMS 
    • H04S1/00—Two-channel systems
    • H04S1/002—Non-adaptive circuits, e.g. manually adjustable or static, for enhancing the sound image or the spatial distribution
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04S—STEREOPHONIC SYSTEMS 
    • H04S7/00—Indicating arrangements; Control arrangements, e.g. balance control
    • H04S7/30—Control circuits for electronic adaptation of the sound field
    • H04S7/301—Automatic calibration of stereophonic sound system, e.g. with test microphone
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04S—STEREOPHONIC SYSTEMS 
    • H04S1/00—Two-channel systems
    • H04S1/007—Two-channel systems in which the audio signals are in digital form
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04S—STEREOPHONIC SYSTEMS 
    • H04S2420/00—Techniques used stereophonic systems covered by H04S but not provided for in its groups
    • H04S2420/01—Enhancing the perception of the sound image or of the spatial distribution using head related transfer functions [HRTF's] or equivalents thereof, e.g. interaural time difference [ITD] or interaural level difference [ILD]

Definitions

  • the present invention relates to the modeling of individual transfer functions called HRTFs (for "Head Related Transfer Functions” ), relating to the hearing of an individual in the three-dimensional space.
  • HRTFs for "Head Related Transfer Functions”
  • the invention is particularly in the context of telecommunication services offering spatialized sound broadcasting (for example an audio conference between several speakers, a movie trailer).
  • spatialized sound broadcasting for example an audio conference between several speakers, a movie trailer.
  • the most effective technique for positioning sound sources in space is then binaural synthesis.
  • Binaural synthesis is based on the use of so-called " binaural " filters, which reproduce the acoustic transfer functions between the sound source and the listener's ears. These filters are used to simulate auditory location indices, indices that allow a listener to locate sound sources in real listening situations. These filters take into account all the acoustic phenomena (in particular the diffraction by the head, the reflections on the roof of the ear and the top of the torso) which modify the acoustic wave in its path between the source and the ears of the listener. These phenomena vary greatly with the position of the sound source (mainly with its direction) and these variations allow the listener to locate the source in the space.
  • Binaural techniques using binaural filters define the field of binaural synthesis in an advantageous context of the present invention.
  • Binaural synthesis is based on binaural filters that model the propagation of the acoustic wave between the source and the two ears of the listener. These filters represent acoustic transfer functions called HRTFs that model the transformations generated by the torso, head and horn of the listener on the signal coming from a sound source. At each sound source position is associated a pair of HRTFs (one HRTF for the right ear, one HRTF for the left ear). In addition, HRTFs carry the acoustic fingerprint of the morphology of the individual on which they were measured.
  • HRTFs therefore depend not only on the direction of the sound, but also on the individual. They are thus a function of the frequency f, the position ( ⁇ , ⁇ ) of the sound source (where the angle ⁇ represents the azimuth and the angle ⁇ the elevation), of the ear (left or right) and the individual.
  • HRTFs are obtained by measurement.
  • left and right HRTFs are measured by means of microphones inserted at the entrance of a subject's ear canal. The measurement must be performed in an anechoic chamber (or " deaf room ").
  • M directions we obtain, for a given subject, a database of 2M acoustic transfer functions representing each position of the space for each ear.
  • the spatialization effect is based on the use of HRTFs which, for optimal performance, must take into account acoustic propagation phenomena between the source and the ears, but also the individual specificities of the morphology of the listener.
  • the experimental measurement of HRTFs directly on an individual is, at the moment, the most reliable solution to obtain binaural filters of quality and really individualized (taking into account the individual specificities of the morphology of the individual). It is recalled that it is a matter of measuring the transfer function between a source located at a given position ( ⁇ 1, ⁇ 1) and the two ears of the subject by means of microphones placed at the entrance of the auditory ducts of this person.
  • An embodiment of this document provides in particular to enrich the morphological data of an individual, at the input of the model, by some HRTFs measured on this individual and in respective specific directions. Thus, only a small number of measurement directions are useful for obtaining the HRTFs of the individual in all directions of space.
  • the conditions and directions in which the representative functions of the HRTFs are to be measured can be arbitrarily set in the learning step.
  • arbitrarily refers to the fact that these measures are not necessarily privileged directions for the model to give better results. It will therefore be understood that these conditions and / or these measurement directions can be chosen for reasons independent of the proper functioning of the model. In addition, the measurement conditions are not necessarily optimal. This is why we are talking here about " representative measures of HRTFs " instead of " HRTFs measurements ".
  • step c1) on any individual, must preferentially be reproducible with those which made it possible to constitute the model in step b).
  • these measurement conditions can be chosen according to criteria that are completely independent of the operation of the model, the essential point being that they are reproducible between the moment when the model is constituted, in step b), and the moment when the measurements are taken on any individual in step c).
  • obtaining complete HRTFs from any individual can be achieved by roughly measuring its HRTFs in only a few directions, with a lean measurement procedure (ie that is, involving only a reduced number of measuring directions and / or a simplified measuring device).
  • the output vector Y of the model consists of coefficients associated with a given representation of an HRTF.
  • the vector Y may correspond to the frequency coefficients describing the spectrum modulus of an HRTF, but other representations may be considered (principal component analysis, IIR filter, or others).
  • the method of the invention is preferably based on statistical learning algorithms and, in a preferred embodiment, on network type algorithms. artificial neurons. These algorithms are briefly presented below.
  • Statistical learning algorithms are tools for predicting statistical processes. They have been used successfully for the prediction of processes for which several explanatory variables can be identified. Artificial neural networks define a particular category of these algorithms. The interest of neural networks lies in their ability to capturing high-level dependencies, that is, dependencies that involve multiple variables at once. The process prediction takes advantage of the knowledge and exploitation of high-level dependencies. There is a wide variety of application domains of neural networks, especially in financial techniques to predict market fluctuations, in pharmaceuticals, in the banking field for the detection of credit card fraud, in marketing to predict behavior. consumers, or others. Neural networks are often considered as universal predictors, in the sense that they are capable of predicting arbitrary data from any explanatory variables, provided that the number of hidden units is sufficient. In other words, they make it possible to model any mathematical function of in if the number of hidden units m is sufficient.
  • a neural network consists of three layers: an input layer 10, a hidden layer 11 and an output layer 12.
  • the input layer 11 corresponds to the explanatory variables, that is to say the variables of input (the aforementioned vector X), from which the prediction is made, and which will be described in detail later.
  • the output layer 12 defines the predicted values (the above-mentioned vector Y).
  • a first step 111 consists in calculating linear combinations of the explanatory variables so as to combine the information coming potentially from several variables.
  • a second step 112 consists in applying a non-linear transformation (for example a function of the " hyperbolic tangent " type) to each of the linear combinations in order to obtain the values of the hidden units or neurons that constitute the hidden layer. This nonlinear transformation defines the activation function of the neurons.
  • the hidden units are recombined linearly, at step 113, to calculate the value predicted by the neural network.
  • neural networks There are different categories of neural networks distinguished by their architecture (type of interconnection between neurons, choice of activation functions, or other) and the learning mode used.
  • Neural networks are not used for prediction purposes only. They are also used for classification and / or grouping of Clustering in a perspective of information reduction. Indeed, a network of neurons is able, in a set of data, to identify common characteristics between the elements of this set, to group them according to their resemblance. Each group thus formed is then associated with an element representative of the information contained in the group, called " representative ". This representative can then be substituted for the entire group. The set of data can thus be described by means of a reduced number of elements, which constitutes a reduction of data. Kohonen maps or self-organizing maps (SOM in English for "Self Organizing Map”) are neural networks dedicated to this clustering task.
  • SOM self-organizing maps
  • the method that seemed the most immediate was a uniform selection in which a subset of directions was chosen by trying to cover the entire 3D sphere as homogeneously and evenly as possible. This method was based on a regular sampling of the 3D sphere. However, it turned out that the HRTFs did not vary in a uniform way depending on the direction. From this point of view, a uniform selection of HRTFs was not really effective.
  • the clustering procedure also provides additional information as to the directions associated with the representative HRTFs, this information making it possible to define a selection of HRTFs intended to feed the input of the HRTFs calculation model. This selection is a priori non-uniform, but more efficient, and guarantees a better " representativeness " of the entire 3D sphere.
  • the present invention proposes the use, as input parameters of the model, of a selection of HRTFs corresponding to directions in the sense that these directions are not necessarily " representative " (in the sense of the clustering technique described above). However, these directions remain exploitable in that the model is able to extract specific information relating to each individual.
  • the invention uses " artificial neural network " type statistical learning algorithms, as a modeling tool for calculating HRTFs (for example with a " Multi Layer Perceptron " or MLP type neuron network). ).
  • the input parameters of the neural network are at least the azimuth angle ( ⁇ 1) and the elevation angle ( ⁇ 1) specifying the direction of an HRTF to be calculated. These parameters are possibly supplemented by " individual " parameters associated with the individual whose HRTFs are to be calculated. These individual parameters include a selection of HRTFs from the individual that have been previously measured. Nevertheless, it is not excluded to add morphological parameters of the individual to the input of the model to enrich the information to be provided to the model.
  • the output parameters of the model are then the coefficients of the vector describing the HRTF for the direction ( ⁇ 1, ⁇ 1) and for the individual specified in input.
  • a risk of the learning phase is the over-learning which is expressed as follows: the neural network learns " by heart " the learning set and tries to reproduce variations specific to the learning set, then they do not exist at the global level.
  • the validation phase 22 is conducted in conjunction with the learning phase 21. Referring to the figure 3 it consists in evaluating the prediction error of the neural network on a validation set (distinct from the training set), which defines the validation error. During learning, the Err_valid validation error begins to decrease and then starts to grow again when over-learning occurs. The minimum MIN of the validation error therefore determines the end of the learning.
  • test phase is conducted once the training is complete and consists in evaluating the prediction error on the test set. This error, called “test error” , finally describes the final performance of the neural network.
  • an operational neural network is available, to which it suffices to submit input parameters to obtain the HRTFs of an individual in one direction.
  • the method in the general sense of the invention therefore comprises a step a) during which a database is formed by measuring a plurality of HRTFs in a multiplicity of directions of the space and for a plurality of individuals.
  • This measurement step referenced 40 on the figure 4a consists in collecting the HRTFs measurements in N directions of space, for several individuals preferentially of different morphology (or " morphotype "), to obtain a complete data base according to the specificities of the individuals. More generally, the number of individuals taken into account during learning is high and better are the performance of the neural network, especially in terms of " universality ".
  • step b) consists in learning the model using the database 20.
  • steps 41 arbitrary steps i of measurements representative of HRTFs in a restricted number n (with n ⁇ N) are arbitrarily selected. This step 41 will be described in detail later, with reference to the figure 4c .
  • the three learning phases 21, validation 22 and test 23 are then conducted to build the model in step 44. It will be noted that it is possible to adjust the limited number of measurements n to avoid the phenomenon of over-learning described above. Thus, it is possible to determine an optimum number Nopt of measurements necessary for the proper functioning of the model (step 42) and to adopt this optimum number (step 43) for the definition of the model.
  • the neural network 44 for calculating the HRTFs.
  • the neural network 44 is then able to calculate the HRTFs of any individual, in any direction, provided that there are a few HRTFs of the individual in the predetermined directions ⁇ i mes , ⁇ i mes .
  • step c1) the measurement conditions of step c1) must be substantially reproducible with the measurement conditions for HRTFs in the directions i (step 41 of FIG. figure 4a ).
  • the database 20 must be constituted under the most conventional and standard conditions to offer, at the output of the model, quality HRTFs that can be applied to rendering devices by providing satisfactory listening comfort.
  • These measures " degraded " are denoted HRTF ( ⁇ i mes , ⁇ i mes ) and carried out at a step 48 of the figure 4c .
  • the model compares these calculated HRTFs with the HRTFs of the database 20 in the same directions ( ⁇ j cal , ⁇ j Cal ). If the deviation is considered too large (arrow n), the learning model 44b is perfected until this difference is reduced to an acceptable error (arrow o): the model then becomes definitive (end step 44).
  • IND is placed in a CAB that is not necessarily anechoic. He has a CAS helmet with at least one MIC microphone attached to one of his ears. Preferably, the CAS helmet is carried by a telescopic rigid rod in height (along the y axis). This rod is also attached to a REP1 mark of the cab CAB.
  • This embodiment makes it possible to maintain the individual IND (with respect to the other axes x and z) and to position it correctly with respect to the reference mark REP1 and, consequently, with respect to the sound sources S1, S2,. CAB cabin.
  • REP2 mark such as a visual cue on a mirror
  • another REP2 mark allows the individual to be positioned in height (along the y axis).
  • the individual can sit on a height adjustable seat and adjust the height until his ears coincide with the mark REP2 on the mirror.
  • one of the advantages of the implementation of the invention is to avoid the clustering technique and to leave a free choice at the location of the sound sources S1-Sn.
  • these sources may be available elsewhere than at the mirror bearing the mark REP2, or else at the level of the base of the rod REP1.
  • the source S2 is slightly shifted relative to the reference mark REP1.
  • the number of sources S1-Sn to predict depends, in principle, the number of HRTFs that one wishes to calculate from the model. Typically, to calculate HRTFs throughout the 3D space, between 25 and 30 prerequisite directions in CAB Cabin are recommended. Nevertheless, for a satisfactory comfort of listening, about fifteen measures is sufficient.
  • the sources S1 to Sn are not necessarily arranged on the same area of the sphere portion.
  • the purpose of the measurement protocol of the figure 5 is not to obtain HRTFs in the strict sense of the term, but more precisely transfer functions of an individual, these transfer functions being partially representative of its HRTFs. These transfer functions are intended to be used as input parameters of the model 44.
  • the inventors have indeed found that the model was able to extract and use the individual information contained in these transfer functions, even if this information is partial or scrambled. What matters is not the quality of the HRTFs measured according to this protocol, but their reproducibility. It is essentially on this reproducibility that the model of HRTFs is based.
  • One advantage of this measurement protocol is to relax the constraints of the measurement procedure, without affecting the proper functioning of the model.
  • the sound sources S1-Sn provided in the cab CAB may be in respective positions belonging to different sphere surfaces.
  • the signals measured by the microphone PCM are collected by an interface 51 of a central unit UC (for example an audio acquisition board), which converts them into digital data.
  • a central unit UC for example an audio acquisition board
  • These data, if necessary enriched by a measurement of the morphological parameter (s) of the individual, are then processed by the model 44 in the sense of the invention.
  • the model 44 may be stored as a computer program product in a memory of the CPU.
  • the HRTFs calculated for all the directions of the space that the model gives can then be stored in memory 52 or recorded on a removable medium (on diskette or engraved on CD-ROM) or communicated via a network such as the Internet or equivalent .
  • the input layer of the neural network comprises a selection of HRTFs of the individual corresponding to any directions, but fixed a priori, and obtained under non-ideal conditions.
  • HRTFs are certainly obtained by direct measurement on the individual IND, but under non-ideal conditions, especially in an environment that is not necessarily anechoic.
  • the measurement protocol must be defined beforehand (typically in learning step b)) and must be rigorously followed in step c) of applying the model to any individual.
  • the resulting neural network is able to compute the HRTFs of any individual in any direction, since it has the steps in the directions ⁇ i and ⁇ i my my selected and obtained under these conditions predefined.
  • a single source can be provided which moves between positions S1 to Sn.

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  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Acoustics & Sound (AREA)
  • Signal Processing (AREA)
  • Stereophonic System (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Feedback Control In General (AREA)

Claims (10)

  1. Verfahren zur Modellierung von einer Person eigenen HRTFs-Übertragungsfunktionen, wobei:
    a) eine Datenbank erstellt wird, die eine Vielzahl von HRTFs gemäß einer Vielfalt von Richtungen des Raums und für eine Vielzahl von Personen umfasst,
    b) durch Lernen in der Datenbank ein Modell konstruiert wird, das HRTFs für die Vielfalt von Richtungen angeben kann, ausgehend von einem Satz von Messwerten, die für HRTFs in aus der Vielfalt von Richtungen ausgewählten Richtungen repräsentativ sind, und
    c) für eine beliebige Person:
    c1) ein Satz von Funktionen gemessen wird, die für die HRTFs der Person nur in den ausgewählten Richtungen repräsentativ sind,
    c2) das Modell an die Messwerte in den ausgewählten Richtungen angewendet wird, und
    c3) die HRTFs der Person in der ganzen Vielfalt von Richtungen erhalten werden,
    und wobei:
    - die Messbedingungen und -richtungen zum Erhalt des Satzes von Messwerten willkürlich während des Lernschritts b) festgelegt werden, und
    - im Schritt c1) Messbedingungen angewendet werden, die im Wesentlichen mit den Messbedingungen des Schritts b) reproduzierbar sind.
  2. Verfahren nach Anspruch 1, wobei im Schritt a) parallel zur Erstellung der Datenbank für die Vielzahl von Personen außerdem an der Vielzahl von Personen jeweilige Sätze von Funktionen gemessen werden, die für die HRTFs unter den willkürlich festgelegten Messbedingungen und -richtungen repräsentativ sind, und für die Konstruktion des Modells im Schritt b):
    - am Eingang des Modells die jeweiligen Sätze angewendet werden, und
    - am Ausgang des Modells die Datenbank angewendet wird.
  3. Verfahren nach einem der Ansprüche 1 und 2, wobei das Modell unter Verwendung eines Netzwerks aus künstlichen Neuronen aufgebaut wird.
  4. Verfahren nach Anspruch 3, wobei der Schritt b) aufweist:
    - eine Lernphase,
    - eine Validierungsphase, die parallel zur Lernphase ausgeführt wird, und
    - eine Testphase,
    und wobei während der Validierungsphase eine optimale Anzahl (Nopt) von Messungen bestimmt wird, die am Eingang des Modells zur Durchführung des Schritts c) zu liefern sind, um eine Wirkung des Überlernens des Modells zu begrenzen.
  5. Verfahren nach Anspruch 4, wobei die optimale Anzahl (Nopt) in der Größenordnung von zwanzig liegt.
  6. Verfahren nach einem der vorhergehenden Ansprüche, wobei das Modell außerdem mindestens einen eine Person kennzeichnenden morphologischen Parameter verwendet, und wobei im Schritt c2) außerdem ein Messwert des morphologischen Parameters an das Modell geliefert wird.
  7. Verfahren nach einem der vorhergehenden Ansprüche, wobei im Schritt c2) am Eingang des Modells geliefert wird:
    - der Satz von Messwerten in den ausgewählten Richtungen, und
    - mindestens eine Richtung (ϕj cal, θj cal) aus der Vielfalt von Richtungen, in der eine Schätzung von HRTFs gewünscht wird.
  8. Anlage zur Schätzung von einer Person eigenen HRTFs-Übertragungsfunktionen, die aufweist:
    - eine Messkabine für repräsentative Übertragungsfunktionen von HRTFs in einem Satz von ausgewählten Richtungen, und
    - eine Verarbeitungseinheit (UC), um einen Satz von Messwerten an einer Person in den ausgewählten Richtungen wiederzugewinnen und die HRTFs der Person in einer Vielfalt von Richtungen des Raums zu bewerten, die die ausgewählten Richtungen umfassen, ausgehend von einem Modell, das fähig ist, HRTFs für eine Vielfalt von Richtungen ausgehend von einem Satz von Messwerten anzugeben, die für HRTFs nur in einigen Richtungen repräsentativ sind, die willkürlich unter der Vielfalt von Richtungen festgelegt werden,
    wobei die Messrichtungen in der Kabine den willkürlich festgelegten Richtungen entsprechen, dadurch gekennzeichnet, dass, da die Kabine Bezugspunkte (REP1, REP2) auf Achsen (x, y) aufweist, deren Schnittstelle eine Stellung der Ohren der Person (IND) in der Kabine (CAB) definiert, Schallquellen (S1-Sn), die in der Kabine (CAB) zur Durchführung der Messungen vorgesehen sind, in unterschiedlichen Abständen von der Schnittstelle platziert sind.
  9. Computerprogrammprodukt, das dazu bestimmt ist, in einem Speicher einer Verarbeitungseinheit oder auf einem entfernbaren Träger gespeichert zu werden, der mit einem Lesegerät der Verarbeitungseinheit zusammenwirken kann, oder dazu bestimmt ist, von einem Server zur Verarbeitungseinheit übertragen zu werden, das Anweisungen in Form eines Computercodes aufweist, um ein Modell zu konstruieren, das auf einem Netzwerk aus künstlichen Neuronen basiert und fähig ist, HRTFs-Übertragungsfunktionen einer Person für eine Vielfalt von Richtungen ausgehend von einem Satz von an dieser Person ausgeführten Messungen anzugeben, die für HRTFs nur in einigen Richtungen repräsentativ sind und willkürlich unter der Vielfalt von Richtungen festgelegt werden, wobei das Programm ausgehend von einer Datenbank, die eine Vielzahl HRTFs gemäß einer Vielfalt von Richtungen des Raums und für eine Vielzahl von Personen umfasst, mindestens eine Lernphase durchführt.
  10. Computerprogrammprodukt, das dazu bestimmt ist, in einem Speicher einer Verarbeitungseinheit oder auf einem entfernbaren Träger gespeichert zu werden, der mit einem Lesegerät der Verarbeitungseinheit zusammenwirken kann, oder das dazu bestimmt ist, von einem Server zur Verarbeitungseinheit übertragen zu werden, das Anweisungen in Form eines Computercodes aufweist, um ein Modell anzuwenden, das auf einem Netzwerk aus künstlichen Neuronen basiert und fähig ist, HRTFs-Übertragungsfunktionen einer Person für eine Vielfalt von Richtungen ausgehend von einem Satz von an dieser Person ausgeführten Messungen anzugeben, die für HRTFs nur in einigen Richtungen repräsentativ sind und willkürlich unter der Vielfalt von Richtungen festgelegt werden, wobei die an der Person ausgeführten Messungen in einer Kabine durchgeführt werden, in der:
    - die Messrichtungen den willkürlich festgelegten Richtungen entsprechen, und
    - die Kabine Bezugspunkte (REP1, REP2) auf jeweiligen Achsen (x, y) aufweist, deren Schnittstelle eine Stellung der Ohren der Person (IND) in der Kabine (CAB) definiert, um die Messungen durchzuführen, wobei in der Kabine (CAB) vorgesehene Schallquellen (S1-Sn) in unterschiedlichen Abständen zur Schnittstelle platziert sind.
EP06709051.4A 2005-01-10 2006-01-09 Verfahren und vorrichtung zur individualisierung von hrtfs durch modellierung Active EP1836876B1 (de)

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FR0500218A FR2880755A1 (fr) 2005-01-10 2005-01-10 Procede et dispositif d'individualisation de hrtfs par modelisation
PCT/FR2006/000037 WO2006075077A2 (fr) 2005-01-10 2006-01-09 Procede et dispositif d’individualisation de hrtfs par modelisation

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JP2008527821A (ja) 2008-07-24
WO2006075077A3 (fr) 2006-10-05
US20080137870A1 (en) 2008-06-12

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