# US 10,313,818 B2 · HRTF personalization based on anthropometric features > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,313,818 B2 - Document ID: US10313818B2 - Status: Granted - Kind: B2 - Application number: 15/876,644 - Filed: 2018-01-22 - Published: 2018-05-24 - Granted: 2019-06-04 - Earliest claimed: 2014-04-29 - Assignees: Microsoft Technology Licensing, LLC - Applicants: Microsoft Technology Licensing, LLC - Inventors: Piotr Tadeusz Bilinski; Jens Ahrens; Mark R. P. Thomas; Ivan J. Tashev; John C. Platt; David E. Johnston - Web page: https://patentgrep.com/patent/US10313818B2 ## CPC - H04S 7/302: {Electronic adaptation of stereophonic sound system to listener position or orientation (H04S7/301 takes precedence)} - H04S 7/301: {Automatic calibration of stereophonic sound system, e.g. with test microphone} - H04S 2400/11: Positioning of individual sound objects, e.g. moving airplane, within a sound field (H04S2420/13 takes precedence) - H04S 2420/01: 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] - H04S: STEREOPHONIC SYSTEMS ## Abstract The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject. ## Claims ### Claim 1 One or more computer storage media storing computer-executable instructions that are executable to cause one or more processors to perform acts comprising: obtaining one or more training anthropometric feature parameters and corresponding Head-related Transfer Functions (HRTFs) of a plurality of training subjects; obtaining a test anthropometric feature parameter of a test subject; determining a representation of a statistical relationship between the test anthropometric feature parameter of the test subject and a subset of training anthropometric feature parameters belonging to the plurality of training subjects; applying the representation of the statistical relationship to the HRTFs of the plurality of training subjects thereby modifying the HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the test subject; and modifying at least one audio-signal based on the set of personalized HRTFs. ### Claim 2 (depends on 1) The one or more computer storage media of claim 1, further comprising generating 3-dimensional sound for the test subject using at least a pair of speakers based at least on the set of personalized HRTFs for the test subject. ### Claim 3 (depends on 1) The one or more computer storage media of claim 1, wherein the test anthropometric feature parameter and the subset of training anthropometric feature parameters correspond to inter-pupillary distance. ### Claim 4 (depends on 1) The one or more computer storage media of claim 1, wherein the test anthropometric feature parameter and the subset of training anthropometric feature parameters correspond to a distance between eyes. ### Claim 5 (depends on 1) The one or more computer storage media of claim 1, wherein applying the statistical relationship to obtain the set of personalized HRTFs includes obtaining personalized HRTFs for at least one of a left ear or a right ear of the test subject. ### Claim 6 (depends on 1) The one or more computer storage media of claim 1, wherein applying the representation of the statistical relationship includes: determining a HRTF magnitude for the test subject by the applying the representation of the statistical relationship to the HRTFs of the plurality of training subjects; determining a corresponding HRTF phase scaling factor for the HRTF magnitude by applying the representation of the statistical relationship to interaural time delay (ITD) data of the plurality of training subjects; and combining the HRTF magnitude and the corresponding HRTF phase scaling factor to generate a personalized HRTF for the test subject. ### Claim 7 (depends on 1) The one or more computer storage media of claim 1, wherein the obtaining includes: obtaining a sample anthropometric feature parameter of a training subject from the plurality of training subjects via at least one of user input or an input from an automated measurement tool; storing the sample anthropometric feature parameter of the training subject; obtaining a set of HRTFs for the training subject via measurement of sounds transmitted to ears of the training subject from a plurality of positions in a spherical arrangement that excludes a spherical wedge; interpolating an additional set of HRTFs for the training subject with respect to virtual positions in the spherical wedge based on the set of HRTFs; and storing the set of HRTFs and the additional set of HRTFs of the training subject. ### Claim 8 (depends on 1) The one or more computer storage media of claim 1, further comprising: obtaining the test anthropometric feature parameter of the test subject via an automated measurement tool. ### Claim 9 A computer-implemented method, comprising: obtaining one or more training anthropometric feature parameters and corresponding Head-related Transfer Functions (HRTFs) of a plurality of training subjects; obtaining a test anthropometric feature parameter of a test subject; determining a sparse representation of the test anthropometric feature parameter of the test subject, the sparse representation representing the test anthropometric feature parameter of the test subject based at least on a subset of the one or more training anthropometric feature parameters belonging to the plurality of training subjects; applying the sparse representation to the HRTFs of the plurality of training subjects thereby modifying the HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the test subject: and modifying at least one audio-signal based on the set of personalized HRTFs. ### Claim 10 (depends on 9) The computer-implemented method of claim 9, wherein obtaining the test anthropometric feature parameter of the test subject includes obtaining the test anthropometric feature parameter of the test subject via at least one of user input or an input from an automated measurement tool. ### Claim 11 (depends on 9) The computer-implemented method of claim 9, wherein the sparse representation represents the test anthropometric feature parameter of the test subject as a linear superposition of the subset of the one or more training anthropometric feature parameters belonging to the plurality of training subjects. ### Claim 12 (depends on 9) The computer-implemented method of claim 9, wherein determining the sparse representation includes using a non-negative sparse representation term in a minimization problem for learning the sparse representation to ensure that weight values of the sparse representation are positive. ### Claim 13 (depends on 9) The computer-implemented method of claim 9, wherein applying the sparse representation includes: determining a HRTF magnitude for the test subject by applying the sparse representation to the HRTFs of the plurality of training subjects; determining a corresponding HRTF phase scaling factor for the HRTF magnitude by applying the sparse representation to interaural time delay (ITD) data of the plurality of training subjects; and combining the HRTF magnitude and the corresponding HRTF phase scaling factor to generate a personalized HRTF for the test subject. ### Claim 14 (depends on 9) The computer-implemented method of claim 9, wherein the test anthropometric feature parameter and the subset of the one or more training anthropometric feature parameters correspond to inter-pupillary distance. ### Claim 15 (depends on 9) The computer-implemented method of claim 9, wherein determining the sparse representation includes solving a minimization problem for a non-negative shrinking parameter that is tuned using a leave-one-person-out cross-validation approach. ### Claim 16 A system, comprising: processors; a memory that includes a computer-executable components that are executable by the processors to perform a plurality of actions, the plurality of actions comprising: obtaining one or more training anthropometric feature parameters and corresponding Head-related Transfer Functions (HRTFs) of a plurality of training subjects; obtaining a test anthropometric feature parameter of a test subject; determining a ridge regression representation of the test anthropometric feature parameter of the test subject, the ridge regression representation representing the test anthropometric feature of the test subject based at least on a subset of the one or more training anthropometric feature parameters belonging to the plurality of training subjects; applying the ridge regression representation to the HRTFs of the plurality of training subjects thereby modifying the HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the test subject and modifying at least one audio-signal based on the set of personalized HRTFs. ### Claim 17 (depends on 16) The system of claim 16, wherein obtaining the test anthropometric feature parameter of the test subject includes obtaining the test anthropometric feature parameter of the test subject via at least one of user input or an input from an automated measurement tool. ### Claim 18 (depends on 16) The system of claim 16, wherein the ridge regression representation represents the test anthropometric feature parameter of the test subject as a linear superposition of the subset of the one or more training anthropometric feature parameters belonging to the plurality of training subjects. ### Claim 19 (depends on 16) The system of claim 16, wherein the applying the ridge regression representation includes: determining a HRTF magnitude for the test subject by applying the ridge regression representation to the HRTFs of the plurality of training subjects; determining a corresponding HRTF phase scaling factor for the HRTF magnitude by applying the ridge regression representation to interaural time delay (ITD) data of the plurality of training subjects; and combining the HRTF magnitude and the corresponding HRTF phase scaling factor to generate a personalized IIRTF for the test subject. ### Claim 20 (depends on 16) The system of claim 16, wherein the obtaining includes: obtaining a set of HRTFs for a training subject from the plurality of training subjects via measurement of sounds transmitted to ears of the training subject from a plurality of positions in a spherical arrangement that excludes a spherical wedge; interpolating a complementary set of HRTFs for the training subject with respect to virtual positions in the spherical wedge based on the set of the HRTFs; and storing the set of HRTFs and the complementary set of HRTFs of the training subject.