CN110457646B - Low-resource head-related transfer function personalization method based on parameter migration learning - Google Patents
Low-resource head-related transfer function personalization method based on parameter migration learning Download PDFInfo
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Abstract
The invention relates to the field of signal processing, discloses a personalized self-adaption method of a low-resource head-related transfer function, and solves the technical problem of accurately obtaining a target object personalized HRTF. The feature generation module generates head-related transfer function related features of different spatial positions, the reference personalized head-related transfer function model training module generates nonlinear mapping between the features of different spatial positions of different objects and corresponding personalized related transfer functions based on a reference head-related function library, the parameter migration model training module performs parameter migration on the reference personalized related transfer function model based on a low-resource personalized related transfer function database of the target object to generate a head-related transfer function personalized model of the target object, and the personalized related function prediction module predicts a full-space personalized head-related transfer function of the target object.
Description
Technical Field
The invention relates to the field of signal processing, in particular to a low resource head related transfer function personalization method based on parameter migration learning.
Background
The explosion in the field of virtual reality has made virtual hearing more and more interesting. Virtual reality includes virtual vision, virtual hearing, virtual touch, virtual taste, etc., wherein the important issue of virtual hearing technology is to restore the same spatial location features as natural hearing. The human auditory process can be generally regarded as a sound source-channel-receiving model, wherein the channel includes the diffraction and interference of the sound source through different parts of the human body and finally reaches the tympanic membrane, and can be regarded as a spatial digital filter called Head-Related Transfer Function (HRTF) which includes all the spectral features caused by the interaction between the sound wave and the body part. Since the physiological structure of each person is different, HRTF spectral features are extremely personalized, and therefore, it is difficult to measure HRTFs in full space for each individual.
At present, there are many HRTF (head related transfer function) personalized methods, and theoretical or mathematical modeling is used for modeling and analyzing a human body, such as a spherical head model, a snowman model, a structural model, a boundary element method, a finite difference time domain method and the like. However, these methods require expensive hardware to perform complex calculations. Therefore, some low complexity methods are proposed. The method for gradually determining the linear modeling of the parameters through audiometric experiments based on the perception method needs a large-scale database for matching so as to obtain the HRTF most suitable for the target object, and therefore, the time consumption is long. In consideration of the dependency relationship between the HRTF and the human physiological parameters, the physiological parameter-based regression method is becoming more common in predicting personalized HRTFs, however, most of these methods have the assumption that the physiological parameter weight is equal to the HRTF weight. On the other hand, the HRTF full-space estimation method from a small data measurement set is also a method for HRTF personalization, however, most of the existing methods only obtain coefficients of a linear prediction model from the small data measurement set and then expand the coefficients to the full space, and the method has a large error in a high frequency band (> 10 kHz).
Therefore, a method with low complexity and capable of obtaining the personalized HRTF of the target object more accurately is needed.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide a method for obtaining a target object personalized HRTF with low complexity and accuracy, which has the advantage of obtaining a target object personalized HRTF with more accuracy.
The above object of the present invention is achieved by the following technical solutions:
a low resource head-related transfer function individuation method based on parameter migration learning is characterized by comprising a feature generation module, a reference individuation related transfer function model training module, a parameter migration model training module and an individuation head-related function prediction module;
the feature generation module generates head-related transfer function related features of different spatial positions, the reference personalized head-related transfer function model training module generates nonlinear mapping between the features of different spatial positions of different objects and corresponding personalized related transfer functions based on a reference head-related function library, the parameter migration model training module performs parameter migration on the reference personalized related transfer function model based on a low-resource personalized related transfer function database of the target object to generate a head-related transfer function personalized model of the target object, and the personalized related function prediction module predicts a full-space personalized head-related transfer function of the target object.
Further, the generation of the feature generation module comprises a direction feature module, a distance feature module, a feature combination module and a feature preprocessing module;
the feature merging module merges the direction features and the distance features and then performs feature preprocessing, wherein the feature preprocessing normalizes the merged features, the mean value is 0, and the variance is 1.
Further, based on a reference head related transfer function database, extracting a log-amplitude minimum phase head related transfer function, then preprocessing the minimum phase head related transfer function, and enabling preprocessed data to enter a reference personalized related transfer function model training module;
the reference personalized head-related transfer function model training module is connected with the feature generation module, the head-related transfer function preprocessing module and the loss function design module based on expert domain knowledge and used for model training based on a deep neural network to obtain a reference personalized head-related transfer function model;
the loss function design module based on expert domain knowledge obtains a loss function in a training process of a reference personalized related transfer function model.
Further, the parameter migration model training module comprises a feature generation module, a reference personalized head related transfer function model training module, a low resource database preparation module and a model training module based on parameter migration learning;
the parameter migration model training module carries out parameter migration on the reference personalized head-related transfer function model based on a low-resource personalized head-related transfer function database of the target object to generate a head-related transfer function personalized model of the target object;
the low resource data preparation module generates parameter migration model training data;
the model training module based on parameter migration learning is used for matching the reference personalized head-related transfer function model migration with the head-related transfer function data of the target object to obtain a personalized head-related transfer function generation model.
Further, the personalized head-related transfer function prediction module comprises a minimum phase head-related transfer function prediction module and a head-related transfer function reconstruction module based on spatial positions, and predicts a full-space personalized head-related transfer function of the target object;
the minimum phase head related transfer function prediction module based on the space position predicts a log-amplitude minimum phase head related transfer function of a target object at a target space position;
the head-related transfer function reconstruction module predicts a log-amplitude minimum phase head-related transfer function to reconstruct an individualized head-related transfer function.
Compared with the prior art, the invention has the beneficial effects that:
(1) The parameter migration learning method provided by the invention can migrate the reference personalized HRTF model to the target object, so that more accurate estimation can be obtained;
(2) The method obtains more accurate personalized HRTF of the target object through different parameter migration methods;
(3) The individualized HRTF modeling method obtains the individualized HRTF in the whole space by referring to the individualized HRTF model and utilizing the small data test sample of the target object, has high robustness and is convenient to apply in the actual environment.
Drawings
FIG. 1 is a schematic structural diagram of a parameter transfer learning-based low resource head related transfer function personalization method for parameter transfer learning-based according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a feature generation module 1 for implementing a parameter transfer learning-based low-resource head-related transfer function personalization method of parameter transfer learning-based according to the present invention;
FIG. 3 is a schematic structural diagram of a reference personalized head-related transfer function model training module 2 for implementing a parameter transfer learning-based low-resource head-related transfer function personalization method of parameter transfer learning-based according to the present invention;
fig. 4 is a schematic structural diagram of the parameter migration model training module 3 implementing the reference personalized head related transfer function model training module according to the present invention.
Reference numerals: 1. a feature generation module; 2. a reference individualized head related transfer function model training module; 3. A parameter migration model training module; 4. a personalized head-related transfer function prediction module; 11. a direction feature generation module; 12. a distance feature generation module; 13. a feature merging module; 14. a feature preprocessing module; 21. a log-amplitude minimum phase HRTF extraction module; 22. a preprocessing module of head-related transfer functions; 23. a loss function design module based on expert domain knowledge; 31. a low resource data preparation module; 32. a model training module based on parameter transfer learning; 41. a minimum phase head related transfer function prediction module based on the spatial position; 42. and a head related transfer function reconstruction module.
Detailed Description
The invention is described in detail below with reference to the figures and examples.
It should be noted that in the drawings or description, the same drawing reference numerals are used for similar or identical parts. And are indicated in the drawings for simplicity and convenience. Furthermore, implementations not shown or described in the drawings are in a form known to those of ordinary skill in the art. Additionally, while exemplifications of parameters including particular values may be provided herein, it is to be understood that the parameters need not be exactly equal to the respective values, but may be approximated to the respective values within acceptable error margins or design constraints.
The invention provides a parameter transfer learning-based low-resource head related transfer function personalization method, which is based on parameter transfer learning, and is characterized in that personalized models of other objects are transferred to a target object through parameter transfer learning by means of other dense HRTF databases, nonlinear mapping of the target object between any space and a personalized HRTF is established, more accurate personalized HRTF of the target object can be obtained by using smaller sample data, and a basis is provided for virtual auditory sense drawing with high immersion under a dynamic scene.
The parameter migration learning method can migrate the reference personalized HRTF model to the target object to obtain more accurate estimation, and obtains more accurate personalized HRTF of the target object through different parameter migration methods.
Example one
A low resource head related transfer function personalization method based on parameter migration learning is disclosed, as shown in figure 1, and comprises a feature generation module 1, a reference personalized related transfer function model training module, a parameter migration model training module 3 and a personalized head related function prediction module;
the feature generation module 1 generates head-related transfer function related features of different spatial positions, the reference personalized head-related transfer function model training module 2 generates nonlinear mapping between the features of different spatial positions of different objects and corresponding personalized related transfer functions based on a reference head-related transfer function library, the parameter migration model training module 3 performs parameter migration on the reference personalized related transfer function model based on a low-resource personalized related transfer function database of the target object to generate a head-related transfer function personalized model of the target object, and the personalized related function prediction module predicts a full-space personalized head-related transfer function of the target object.
As shown in fig. 2, further, the generation of the feature generation module 1 includes a direction feature module, a distance feature module, a feature merging module 13, and a feature preprocessing module 14. The feature merging module 13 merges the direction feature and the distance feature and performs feature preprocessing, where the feature preprocessing normalizes the merged features, and the mean value is 0 and the variance is 1.
As shown in fig. 3, further, based on the reference head-related transfer function database, extracting a log-amplitude minimum phase head-related transfer function, then preprocessing the minimum phase head-related transfer function, and entering the preprocessed data into a reference personalized related transfer function model training module;
the reference personalized head related transfer function model training module 2 is connected with the feature generation module 1, the head related transfer function preprocessing module 22 and the loss function design module 23 based on expert domain knowledge, and is used for model training based on a deep neural network to obtain a reference personalized head related transfer function model;
the loss function design module 23 based on expert domain knowledge obtains a loss function in the training process of the reference personalized correlation transfer function model.
As shown in fig. 4, further, the parameter migration model training module 3 includes a feature generation module 1, a reference personalized head-related transfer function model training module 2, a low resource database preparation module, and a model training module 32 based on parameter migration learning;
the parameter migration model training module 3 performs parameter migration on the reference personalized head-related transfer function model based on the low-resource personalized head-related transfer function database of the target object to generate a head-related transfer function personalized model of the target object;
the low-resource data preparation module 31 generates parameter migration model training data;
the model training module 32 based on parameter migration learning performs migration on the reference personalized head-related transfer function model and matches the head-related transfer function data of the target object to obtain a personalized head-related transfer function generation model.
Further, the personalized head-related transfer function prediction module 4 includes a minimum phase head-related transfer function prediction module 41 and a head-related transfer function reconstruction module 42 based on spatial positions, and predicts a full-space personalized head-related transfer function of the target object;
the minimum phase head related transfer function prediction module 41 based on spatial position predicts the log-amplitude minimum phase head related transfer function of the target object at the target spatial position;
head-related transfer function reconstruction module 42 predicts a log-amplitude minimum phase head-related transfer function reconstruction personalized head-related transfer function.
Example two
On the basis of the first embodiment, more specifically, the direction feature generation module 11 is a feature generation module 1, and is configured to generate a direction-related feature for a current location. The sound field transmission response from the sound source to both ears is a complex function of frequency, distance, azimuth, elevation, and the sound field can be represented in a specific set of orthogonal sequences. The direction-related features are generated by adopting spherical harmonics, wherein the spherical harmonics are functions of azimuth angles and elevation angles and are defined as formulas (1) and (2).
Wherein N is the degree of the legendre function, N = 0.. N; m is the order of Legendre function, | m | is less than or equal to n;
is a Legendre function with the degree of n and the order of m; theta and phi are the azimuth and elevation angles, respectively, of the measured position.
Distance feature generation module 12 the feature generation module 1 is arranged to generate distance-related features at the current location. The distance-related features are generated by adopting a standard spherical Bessel function and defined as
Wherein j is l (x) Is a spherical Bessel function with an order of l,
J l' (x) Is a Bessel function. N is a radical of hydrogen nl In order to normalize the factors, the method comprises the following steps of,
x ln is j is l (x) =0 nth ascending positive root. k is a radical of formula nl ＝x nl A is the wavenumber and a is the maximum radius. And r is the distance from the current sound source position to the center of the head.
Feature merging module 13, direction feature generation module 11, and featuresThe generating module 1 is connected to the distance feature generating module 12, and is configured to combine features related to direction and distance at any spatial position, and use the combined features as an input of the feature preprocessing module 14. For the position d = (r, theta, phi), after the direction and distance features are combined, the obtained input feature set is the
Assuming that the degree of the Legendre function is N and the order of the spherical Bessel function is L, the generated feature set contains N in total for each position d t ＝[(N+1) 2 +NL]A characteristic parameter.
And the feature preprocessing module 14 is connected with the feature merging module 13 and the reference personalized head related transfer function model training module 2, and is used for preprocessing the merged features, normalizing the input features within values of 0 in mean value and 1 in variance, and using the output of the module as the input of the reference personalized head related transfer function model training. For the ith item in the feature set at the s-th position, the preprocessing procedure is expressed as
Wherein the content of the first and second substances,
and
respectively, the mean and standard deviation of the ith feature at all positions. S is the number of spatial measurement positions of the data in the reference HRTF database.
And the log-amplitude minimum phase HRTF extracting module 21 is connected with the feature generating module 1 and is used for extracting the log-amplitude minimum phase head related transfer function and preparing data for the output of the reference individualized head related transfer function model training module 2. HRTF H for ith frequency bin at s-th position s (i) The calculation process of the logarithm amplitude minimum phase HRTF is as follows
The preprocessing module 22 of the head-related transfer function is connected to the log-amplitude minimum-phase HRTF extracting module 21, and is used for preprocessing the minimum-phase HRTF and outputting the preprocessed minimum-phase HRTF as an output of the reference personalized head-related transfer function model training module 2. The purpose of the HRTF preprocessing is to normalize the input features to a value with a mean of 0 and a variance of 1, reducing the floating range of the data. Log-amplitude minimum phase HRTF for ith bin at s position
The pretreatment process is expressed as
Wherein the content of the first and second substances,
and
respectively representing the mean value and the standard deviation of the ith frequency point of the HRTF at all positions. N is a radical of f The number of bins for model training.
And the loss function design module based on expert domain knowledge is used for obtaining a loss function used in the training process of the reference personalized head related transfer function model. The design basis is subjective perception domain knowledge. Since the log-amplitude spectrum retains all perceptually relevant information for the human ear, the loss function is defined based on the log-spectrum distortion criterion as
Wherein k is 1 And k 2 Frequency band representing contrastRanging from the kth 1 Band to kth 2 The frequency band is generally 20Hz to 20kHz according to the hearing range of human ears. N is a radical of f Is k 1 To k 2 The number of frequency bins in between. S is the number of measurement locations used for model training.
And the normalized logarithmic amplitude minimum phase HRTF represents the ith frequency point at the s position predicted by the reference personalized head related transfer function model. By minimizing the loss function, the objective performance of the model can be maximized.
And the reference personalized head-related transfer function model training module is connected with the feature generation module, the head-related transfer function preprocessing module and the loss function design module based on expert domain knowledge and is used for model training based on the deep neural network to obtain a reference personalized head-related transfer function model.
And the low-resource data preparation module is connected with the feature generation module and the reference personalized head related transfer function model training module and is used for generating data for parameter migration model training. The parameter migration model training adopts a low-resource personalized head related transfer function database of a target object, and the database comprises a total S measured on the target object t HRTF at each position, where S t < S. For measured S t And (4) generating the characteristics of the parameter migration model training module through the characteristic generation module, wherein the data is used as the input of the parameter migration model. Then, for the HRTF corresponding to the target function at the s-th position, obtaining the HRTF after the target object is preprocessed by adopting a log-amplitude minimum phase HRTF extraction module in a reference personalized head-related transfer function model training module and a preprocessing module of the head-related transfer function, and taking the data as the output of the parameter migration model.
And the model training module based on parameter migration learning is connected with the data preparation module and the reference personalized head related transfer function model training module and is used for carrying out model migration on the reference personalized head related transfer function model so as to enable the model migration to be matched with HRTF data of a target object. Because the HRTF measuring positions of the target object are less, three parameter migration methods are adopted, which respectively correspond to the following steps: the method comprises the following steps of first hidden layer parameter migration, middle hidden layer parameter migration and last hidden layer parameter migration. And in the model training process based on parameter migration learning, all node parameters except the first hidden layer of the fixed reference personalized HRTF model are unchanged, and the first hidden layer node parameters in the reference personalized HRTF model are updated by using input features and output data obtained by the low-resource data preparation module. Similarly, the intermediate hidden layer parameter migration and the last hidden layer parameter migration only update the intermediate hidden layer node parameter and the last hidden layer node parameter, respectively.
And the head-related transfer function reconstruction module is connected with the minimum phase head-related transfer function prediction module based on the space position and used for reconstructing the personalized head-related transfer function through the predicted log amplitude minimum phase head-related transfer function. For target position d s And performing log-amplitude minimum phase HRTF de-normalization on the output of the minimum phase head-related transfer function prediction module based on the spatial position, wherein the log-amplitude minimum phase HRTF de-normalization is calculated as follows:
then, after the logarithm is changed into linear and inverse Hilbert transform, the reconstructed HRTF is obtained.
The parameter migration learning-based low resource head related transfer function personalization method based on parameter migration learning can be written by Matlab and c languages or any other programming languages. In addition, the invention can be applied to computer terminals, handheld mobile equipment or other forms of mobile equipment.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to those skilled in the art without departing from the principles of the present invention should also be considered as within the scope of the present invention.
Claims (1)
1. A low-resource head-related transfer function personalization method based on parameter migration learning is characterized by comprising a feature generation module (1), a reference personalized related transfer function model training module, a parameter migration model training module (3) and a personalized head-related function prediction module; the characteristic generation module (1) generates head-related transmission function related characteristics of different spatial positions, the reference personalized head-related transmission function model training module (2) generates nonlinear mapping between the characteristics of different objects at different spatial positions and corresponding personalized related transmission functions based on a reference head-related function library, the parameter migration model training module (3) performs parameter migration on the reference personalized related transmission function model based on a low-resource personalized related transmission function database of a target object to generate a head-related transmission function personalized model of the target object, and the personalized related function prediction module predicts the full-space personalized head-related transmission function of the target object;
the feature generation module (1) comprises a direction feature module, a distance feature module, a feature merging module (13) and a feature preprocessing module (14);
the characteristic merging module (13) merges the direction characteristic and the distance characteristic and then carries out characteristic preprocessing, the characteristic preprocessing normalizes the merged characteristic, the mean value is 0, and the variance is 1;
based on a reference head related transfer function database, extracting a log-amplitude minimum phase head related transfer function, preprocessing the minimum phase head related transfer function, and enabling preprocessed data to enter a reference personalized related transfer function model training module;
a reference personalized head-related transfer function model training module (2) which is connected with the feature generation module (1), the head-related transfer function preprocessing module (22) and the loss function design module (23) based on expert domain knowledge and is used for model training based on a deep neural network to obtain a reference personalized head-related transfer function model;
the loss function design module (23) based on expert domain knowledge obtains a loss function in a training process of a reference personalized related transfer function model;
the parameter migration model training module (3) comprises a feature generation module (1), a reference personalized head-related transfer function model training module (2), a low-resource database preparation module and a model training module (32) based on parameter migration learning;
the parameter migration model training module (3) conducts parameter migration on the reference personalized head-related transfer function model based on the low-resource personalized head-related transfer function database of the target object to generate a head-related transfer function personalized model of the target object;
the low-resource data preparation module (31) generates parameter migration model training data;
the model training module (32) based on parameter migration learning is used for matching the reference personalized head-related transfer function model migration with the head-related transfer function data of the target object to obtain a personalized head-related transfer function generation model;
the individualized head-related transfer function prediction module (4) comprises a minimum phase head-related transfer function prediction module (41) and a head-related transfer function reconstruction module (42) based on spatial positions, and predicts a full-space individualized head-related transfer function of a target object;
the spatial position-based minimum phase head related transfer function prediction module (41) predicts a log-amplitude minimum phase head related transfer function of the target object at the target spatial position;
the head-related transfer function reconstruction module (42) predicts a log-amplitude minimum-phase head-related transfer function to reconstruct an individualized head-related transfer function description.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9648438B1 (en) * | 2015-12-16 | 2017-05-09 | Oculus Vr, Llc | Head-related transfer function recording using positional tracking |
| CN107480100A (en) * | 2017-07-04 | 2017-12-15 | 中国科学院自动化研究所 | Head-position difficult labor modeling based on deep-neural-network intermediate layer feature |
| CN108038291A (en) * | 2017-12-05 | 2018-05-15 | 武汉大学 | A kind of personalized head related transfer function generation system and method based on human parameters adaptation algorithm |
| US10028070B1 (en) * | 2017-03-06 | 2018-07-17 | Microsoft Technology Licensing, Llc | Systems and methods for HRTF personalization |
| CN108549907A (en) * | 2018-04-11 | 2018-09-18 | 武汉大学 | A kind of data verification method based on multi-source transfer learning |
| CN109145360A (en) * | 2018-06-29 | 2019-01-04 | 中国科学院自动化研究所 | Head-position difficult labor personalization modeling based on sparse constraint |
-
2019
- 2019-06-26 CN CN201910560696.6A patent/CN110457646B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9648438B1 (en) * | 2015-12-16 | 2017-05-09 | Oculus Vr, Llc | Head-related transfer function recording using positional tracking |
| US10028070B1 (en) * | 2017-03-06 | 2018-07-17 | Microsoft Technology Licensing, Llc | Systems and methods for HRTF personalization |
| CN107480100A (en) * | 2017-07-04 | 2017-12-15 | 中国科学院自动化研究所 | Head-position difficult labor modeling based on deep-neural-network intermediate layer feature |
| CN108038291A (en) * | 2017-12-05 | 2018-05-15 | 武汉大学 | A kind of personalized head related transfer function generation system and method based on human parameters adaptation algorithm |
| CN108549907A (en) * | 2018-04-11 | 2018-09-18 | 武汉大学 | A kind of data verification method based on multi-source transfer learning |
| CN109145360A (en) * | 2018-06-29 | 2019-01-04 | 中国科学院自动化研究所 | Head-position difficult labor personalization modeling based on sparse constraint |
Non-Patent Citations (1)
| Title |
|---|
| 基于多维生理参数的头相关传递函数个人化方法;黄婉秋等;《西北工业大学学报》;20180415(第02期);全文 * |
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