Selection device, selection method, and program

Abstract

Provided is a selection device in which two or more clusters obtained by classifying measurement data relating to the auricle shapes and wearing positions of open-ear earphones of a plurality of listeners or values based on the measurement data on the basis of the degrees of approximation thereof are associated with transfer characteristics corresponding to the respective clusters and each being the transfer characteristic from an earphone sound hole to an external auditory pore or with correction filters based on the transfer characteristics corresponding to the respective clusters, the selection device including a selection unit that acquires measurement data of a user and selects a transfer characteristic corresponding to the measurement data of the user or a value based on the measurement data of the user, or a correction filter.

Description

Selection device, selection method, and program

[0001] The present disclosure relates to a selection device, a selection method, and a program that selects the transfer characteristics or correction filters of an open-ear type earphone according to the pinna shape of each user and the wearing position of the open-ear type earphone.

[0002] Recently, there has been a lot of talk about constructing cross-reality (XR) technology for sound by presenting 3D sound with open-ear earphones. However, the transfer characteristics of open-ear earphones depend on the shape of the ear and the position where the earphones are worn, and the relationship between the shape of the ear and the transfer characteristics has been unclear.

[0003] For example, Non-Patent Document 1 evaluated the accuracy of a general-purpose filter by comparing it with the accuracy of an individual filter, and showed that the two were close to each other and that the general-purpose filter was effective.

[0004] Engel, Isaac, et al. "The effect of generic headphone compensation on binaural renderings.",Audio Engineering Society Conference: 2019 AES International Conference on Immersive and Interactive Audio.Audio Engineering Society, 2019.

[0005] However, using the general-purpose filter described in Non-Patent Document 1 alone may not be sufficient, and it may be necessary to optimize the filter for each individual. For example, the graph in Figure 1 shows that the waveform of the dashed line graph, which is a general-purpose HpTF (Headphone to Ear Transfer Function) obtained using HATS (Head and Torso Simulators), is significantly different from the waveform of the solid line graph, which is the average HpTF measured from 29 listeners. Note that (A) in the same figure shows the result for the left ear, and (B) in the same figure shows the result for the right ear.

[0006] Therefore, an object of the present disclosure is to provide a selection device that can select the transfer characteristics or correction filters of open-ear earphones depending on the auricle shape of each user and the position where the open-ear earphones are worn.

[0007] The selection device of the present disclosure includes a selection unit. Two or more clusters obtained by classifying measurement data relating to the pinna shapes and the wearing positions of open-ear earphones of multiple listeners, or values ​​based on the measurement data, based on the degree of similarity, are associated with transfer characteristics corresponding to each cluster, which are transfer characteristics from the earphone sound hole to the outer ear canal, or correction filters based on the transfer characteristics corresponding to each cluster. The selection unit acquires the user's measurement data and selects the transfer characteristics or correction filters corresponding to the cluster corresponding to the user's measurement data or the values ​​based on the user's measurement data.

[0008] According to the selection device of the present disclosure, it is possible to select the transfer characteristics or correction filters of the open-ear earphones according to the pinna shape of each user and the position where the open-ear earphones are worn.

[0009] 1A and 1B are diagrams comparing the general-purpose HpTF measured by HATS with the average HpTF measured from listeners, where FIG. 1A shows the result for the left ear and FIG. 1B shows the result for the right ear. A block diagram showing the functional configuration of a selection device according to a first embodiment. A flowchart showing the operation of generating transfer characteristics or correction filters by the selection device according to the first embodiment. A flowchart showing the operation of selecting transfer characteristics or correction filters by the selection device according to the first embodiment. A block diagram showing the functional configuration of a selection device according to a second embodiment. A flowchart showing the operation of generating transfer characteristics or correction filters by the selection device according to the second embodiment. A flowchart showing the operation of selecting transfer characteristics or correction filters by the selection device according to the second embodiment. A diagram explaining the names of each part of the pinna. A diagram explaining 11 types of measurement data used in clustering, where FIG. 9A explains the first to fifth measurement data and FIG. 9B explains the sixth to eleventh measurement data. A diagram showing the eigenvectors of each principal component obtained by principal component analysis of the measurement data. 11(A) shows the results of clustering a listener's HpTF in the range of 188 Hz to 24 kHz, where Fig. 11(A) shows the average HpTF for each cluster, Fig. 11(B) shows the distribution of eight-dimensional pinna shape features corresponding to each cluster, and Fig. 11(C) shows the distribution of 11-dimensional measurement data corresponding to each cluster. 12(A) shows the average HpTF for each cluster, Fig. 12(B) shows the distribution of eight-dimensional pinna shape features corresponding to each cluster, and Fig. 12(C) shows the distribution of 11-dimensional measurement data corresponding to each cluster. 13(A) shows the results of clustering a listener's HpTF by the first principal component, where Fig. 13(B) shows the average HpTF for each cluster, and Fig. 13(B) shows the distribution of eight-dimensional pinna shape features corresponding to each cluster. 14A and 14B are diagrams showing the results of clustering a listener's HpTF by the sixth principal component, with Fig. 14A showing the average HpTF for each cluster and Fig. 14B showing the distribution of eight-dimensional pinna shape features corresponding to each cluster. 15A and 15B are diagrams showing the results of clustering a listener's HpTF by the fourth measurement data (m4), with Fig. 15A showing the average HpTF for each cluster and Fig. 15B showing the distribution of eleven-dimensional measurement data corresponding to each cluster.16(A) and 16(B) are diagrams showing the results of clustering a listener's HpTF using the fifth measurement data (m5), where Fig. 16(A) shows the average HpTF for each cluster and Fig. 16(B) shows the distribution of 11-dimensional measurement data corresponding to each cluster. 17(A) and 17(B) are diagrams showing the results of clustering a listener's HpTF using the seventh measurement data (m7), where Fig. 17(A) shows the average HpTF for each cluster and Fig. 17(B) shows the distribution of 11-dimensional measurement data corresponding to each cluster. 18(A) and 18(B) are diagrams showing the results of clustering a listener's HpTF using the ninth measurement data (m9), where Fig. 18(A) shows the average HpTF for each cluster and Fig. 18(B) shows the distribution of 11-dimensional measurement data corresponding to each cluster. 19A and 19B are diagrams showing the results of clustering the HpTF of listeners using the tenth measurement data (m10), in which Fig. 19A shows the average HpTF of each cluster, and Fig. 19B shows the distribution of 11-dimensional measurement data corresponding to each cluster.

[0010] Hereinafter, embodiments of the present disclosure will be described in detail. Note that components having the same functions are assigned the same numbers, and redundant explanations will be omitted.

[0011] The functional configuration of the selection device of the first embodiment will be described below with reference to Fig. 2. As shown in Fig. 2, the selection device 1 of this embodiment includes a generation unit 11, a storage unit 12, and a selection unit 13. Although a filter processing unit 94 and an earphone speaker 95 are assumed to be external to the device, these components may be located inside the selection device 1.

[0012] The transfer characteristics and correction filter generation operation of the selector 1 of this embodiment will be described below with reference to FIG.

[0013] The generation unit 11 classifies measurement data related to the pinna shapes and the wearing positions of open-ear earphones of multiple listeners, or values ​​based on the measurement data (e.g., principal component values ​​obtained by aggregating the measurement data through principal component analysis), into two or more clusters based on their similarity (S11). The generation unit 11 generates transfer characteristics corresponding to these clusters, i.e., transfer characteristics from the earphone sound hole to the outer ear canal, for example, by taking the average value of the transfer characteristics of listeners belonging to each cluster, and stores the generated transfer characteristics in the storage unit 12 in association with each cluster (S11). Alternatively, the generation unit 11 generates correction filters (typically inverse filters of the transfer characteristics) based on the transfer characteristics generated corresponding to each cluster, and stores the generated correction filters in the storage unit 12 in association with each cluster (S11).

[0014] The transfer characteristic corresponding to each cluster may typically be the average value within the cluster, but other methods may also be used such that the transfer characteristic of the listener closest to the center of the cluster is simply selected.In addition to this, for example, instead of averaging the transfer characteristics, the correction filters may be averaged, or the correction filter of the listener closest to the center of the cluster may be simply selected.

[0015] The storage unit 12 stores and holds the clusters generated in step S11, the transfer characteristics corresponding to each cluster, and the correction filters corresponding to each cluster.

[0016] The transfer characteristics and correction filter selection operation of the selector 1 of this embodiment will be described below with reference to FIG.

[0017] The selection unit 13 acquires measurement data of a user (which refers to a person other than the listener described above and whose transfer characteristics are unknown), and selects a transfer characteristic or a correction filter corresponding to the cluster corresponding to the user's measurement data or a value based on the user's measurement data (for example, a principal component value obtained by aggregating the measurement data through principal component analysis) (S13).

[0018] The filter processing unit 94 generates a correction filter based on the selected transfer characteristics and performs filtering based on the generated correction filter, or performs filtering based on the selected correction filter. The earphone speaker 95 reproduces the filtered earphone-reproduced sound. Filtering using the correction filter cancels out the influence of the transfer characteristics from the earphone sound hole to the outer ear canal, correcting the localization.

[0019] Although details will be described later, the generation unit 11 may associate two or more clusters classified based on the degree of approximation of any of the values ​​of the principal components with corresponding transfer characteristics or correction filters and store them in the storage unit 12, and the selection unit 13 may acquire user measurement data, perform principal component analysis on the user measurement data, perform clustering based on the degree of approximation of any of the principal component values, and select transfer characteristics or correction filters corresponding to the corresponding clusters. In this case, the selection unit 13 may select transfer characteristics or correction filters corresponding to the clusters for each frequency region corresponding to any of the principal components.

[0020] Furthermore, although details will be described later, the generation unit 11 may associate two or more clusters classified based on the degree of approximation of any value in the measurement data with corresponding transfer characteristics or correction filters and store them in the storage unit 12, and the selection unit 13 may acquire the user's measurement data, perform clustering based on the degree of approximation of any value in the user's measurement data, and select a transfer characteristic or correction filter corresponding to the corresponding cluster. In this case, the selection unit 13 may select a transfer characteristic or correction filter corresponding to a cluster for each frequency range corresponding to any value in the measurement data.

[0021] In the first embodiment, clustering is performed based on measurement data or values ​​based on the measurement data (for example, values ​​of principal components). However, clustering may be performed based on the degree of approximation of transfer characteristics, for example.

[0022] The functional configuration of the selection device of the second embodiment will be described below with reference to Fig. 5. As shown in Fig. 5, the selection device 2 of this embodiment includes a generation unit 21, a storage unit 22, and a selection unit 23. The external configuration of the device is the same as that of the first embodiment, and these components may be located inside the selection device 2.

[0023] The transfer characteristics and correction filter generation operation of the selector 2 of this embodiment will be described below with reference to FIG.

[0024] The generation unit 21 classifies the transfer characteristics (from the earphone sound hole to the outer ear canal) of multiple listeners into two or more clusters based on the degree of similarity (S21).

[0025] The generation unit 21 generates transfer characteristics corresponding to these clusters, for example, by taking the average value of the transfer characteristics of the listeners belonging to each cluster, and stores the generated transfer characteristics in association with each cluster in the storage unit 12 (S21). As in the first embodiment, the transfer characteristic corresponding to each cluster may typically be the average value within the cluster, but may also be the transfer characteristic of the listener closest to the center of the cluster, or may be an average of correction filters instead of transfer characteristics, or may be the correction filter of the listener closest to the center of the cluster.

[0026] Furthermore, the generation unit 21 stores the measurement data corresponding to these clusters (the listener's pinna shape, the position where the earphones are worn) or values ​​based on the corresponding measurement data (e.g., the values ​​of the principal components) in the storage unit 22 in association with each cluster (S21).

[0027] The storage unit 22 stores and holds the clusters generated in step S21, the transfer characteristics corresponding to each cluster, the measurement data (or values ​​based on the measurement data) corresponding to each cluster, and the correction filters corresponding to each cluster.

[0028] The transfer characteristics and correction filter selection operation of the selector 2 of this embodiment will be described below with reference to FIG.

[0029] The selection unit 23 acquires the user's measurement data and selects transfer characteristics or correction filters corresponding to the user's measurement data or a cluster corresponding to a value based on the user's measurement data (e.g., a principal component value) (S23). Measurement data relating to the listener's (user's) pinna shape and the wearing position of the open-ear earphones will be described below with reference to FIGS. 8 and 9. As shown in FIG. 8, the human pinna is formed by the concavity and convexity of the saddle bone, and each part of this concavity and convexity shape has a name. For example, in FIG. 8, 91 is called the Helix, 91a is the Helicalcrus, 91b is the Cauda helicis, 92 is the Scarpa, 93 is the Antihelix, 93b is the Crus inferius antihelices, 94 is the Concha, 94b is the Incisura intertragica, 95 is the Tragus, and 96 is the Lobule.

[0030] Of course, there are other names for the parts indicated by numbers in the figure, and there are also names for parts not indicated by numbers in the figure. Furthermore, the names that should be used to refer to the corresponding regions (areas) of the auricle vary slightly depending on the reference material. In this specification, the measurement data will be explained according to the definitions of the names in Figure 8. Furthermore, the x direction in the figure will be referred to as the short-side direction of the auricle, and the y direction will be referred to as the long-side direction of the auricle.

[0031] As shown in Figure 9 (A), the first measurement data (also referred to as m1, or simply 1 in the graph described later) is the distance in the longitudinal direction of the auricle between the lower end of the helical crus 91a and the intertragic notch 94b.

[0032] The second measurement data (also referred to as m2, and simply written as 2 in the graph described later) is the distance in the transverse direction of the auricle between the tragus 95 and the end of the cavity of the concha 94 on the occipital side in the transverse direction of the auricle.

[0033] The third measurement data (also referred to as m3, and simply referred to as 3 in the graph described below) is the longitudinal distance of the auricle between the lower antihelix crus 93b and the upper end of the auricle longitudinally of the helix 91 (but on the inner surface of the helix 91).

[0034] The fourth measurement data (also referred to as m4, and simply written as 4 in the graph described below) is the longitudinal distance between the upper end of the auricle longitudinal direction of the helix 91 and the lower end of the auricle longitudinal direction of the earlobe 96.

[0035] The fifth measurement data (also referred to as m5, and simply written as 5 in the graph described below) is the distance along the width of the auricle between the nose-side end of the auricle 91 in the short direction (but on the inner surface of the auricle 91) and the occipital end of the auricle 91 in the short direction.

[0036] As shown in FIG. 9B, the sixth measurement data (also referred to as m6, and simply referred to as 6 in the graphs described later) is the distance in the longitudinal direction of the pinna between the earphone sound hole 81 and the intertragic notch 94b.

[0037] The seventh measurement data (also referred to as m7, and simply referred to as 7 in the graph described later) is the distance along the width of the auricle between the earphone sound hole 81 and the end of the cavity of the concha 94 on the occipital side along the width of the auricle.

[0038] The eighth measurement data (also referred to as m8, and simply referred to as 8 in the graphs described later) is the longitudinal distance of the earphone sound hole 81 and the upper end of the helix 91 in the longitudinal direction of the auricle.

[0039] The ninth measurement data (also referred to as m9, and simply referred to as 9 in the graphs described later) is the distance in the longitudinal direction of the pinna between the earphone sound hole 81 and the lower end of the earlobe 96 in the longitudinal direction of the pinna.

[0040] The tenth measurement data (also referred to as m10, and simply referred to as 10 in the graph described below) is the distance in the short direction of the pinna between the earphone sound hole 81 and the end of the helix 91 on the occipital side in the short direction of the pinna.

[0041] The eleventh measurement data (also referred to as m11, and simply referred to as 11 in the graphs described later) is the earphone attachment angle relative to a predetermined reference attachment angle.

[0042] <Analysis: Relationship between Measured Data and Headphone-to-Ear Transfer Function (HpTF)> The following analysis of the relationship between measured data and HpTF is presented with reference to Figures 10-19. In this analysis, HpTF measurements were performed using steps 1) to 3). 1) A microphone was placed at the entrance of each listener's ear canal. 2) A TSP signal was played back through open-ear earphones. 3) The impulse response was measured. Five measurements of HpTF for each listener and each ear were averaged on the logarithmic power spectrum of the amplitude response. A total of 54 HpTFs were generated for 27 listeners and two ears. Measurement data from the 11 locations (m1-m11) described above was used. As shown in Figure 10, in addition to the measured data, eight principal components obtained by principal component analysis of the measured data were also used. Hereinafter, these eight principal components (1-8) are referred to as pinna shape features. The nth principal component (n = 1, 2, ..., 8) is also referred to as the nth pinna shape feature.

[0043] Figure 11 shows the results of clustering using kmeans to obtain three clusters using principal components obtained by dimensionally compressing the listener's HpTF (used frequency band: 188 Hz-24 kHz, covering almost the entire audible range) to a contribution rate of 95%. Figure 11(A) shows the average HpTF for each cluster, with the HpTF for cluster A indicated by a solid line, the HpTF for cluster B indicated by a dashed line, and the HpTF for cluster C indicated by a dashed-dotted line. Figure 11(B) shows the distribution of eight-dimensional pinna shape features corresponding to each cluster obtained by the clustering, and Figure 11(C) shows the distribution of 11-dimensional measurement data corresponding to each cluster obtained by the clustering. Cluster A (also referred to as group A) is represented by a circle, cluster B (also referred to as group B) by a square, and cluster C (also referred to as group C) by a triangle.

[0044] From the figure, it can be confirmed that in the frequency band of 188 Hz-24 kHz, significant differences occur in the distribution between clusters for the first pinna shape feature (1), the fourth measurement data (m4), the seventh measurement data (m7), the ninth measurement data (m9), and the tenth measurement data (m10), suggesting that these data are important factors in determining the shape of the HpTF in the frequency band of 188 Hz-24 kHz (almost the entire audible range).

[0045] Figure 12 shows the results of clustering using kmeans to obtain three clusters using principal components obtained by dimensionally compressing the HpTFs of listeners in a different frequency band from that in Figure 11 (188 Hz-10 kHz, including most of the audible range and cutting the high-frequency band) to a contribution rate of 95%. Figure 12(A) shows the average HpTFs for each cluster. Figure 12(B) shows the distribution of eight-dimensional pinna shape features corresponding to each cluster obtained by the clustering, and Figure 12(C) shows the distribution of 11-dimensional measurement data corresponding to each cluster obtained by the clustering.

[0046] From the figure, it can be confirmed that there are significant differences in the distribution between clusters for the sixth pinna shape feature (6) and the fifth measurement data (m5) in the frequency band of 188 Hz-10 kHz, suggesting that these data are important factors in determining the shape of the HpTF in the frequency band of 188 Hz-10 kHz.

[0047] Based on the findings obtained from FIGS. 11 and 12 , the average value of HpTF for each cluster when clustering is performed based on the first pinna shape feature (1), the sixth pinna shape feature (6), the fourth measurement data (m4), the fifth measurement data (m5), the seventh measurement data (m7), the ninth measurement data (m9), and the tenth measurement data (m10) will be considered below.

[0048] Fig. 13 shows the results of clustering using the first pinna shape feature (1) with kmeans so that the number of clusters is 3. Fig. 13(A) shows the average HpTF of each cluster. Fig. 13(B) shows the distribution of eight-dimensional pinna shape features corresponding to each cluster obtained by the above clustering. As shown in the figure, when clustering is performed using the first pinna shape feature (1), the HpTF differs significantly in the low frequency range and in the frequency band above 10 kHz.

[0049] Fig. 14 shows the results of clustering using kmeans with the sixth pinna shape feature (6) so that the number of clusters is 3. Fig. 14(A) shows the average HpTF for each cluster. Fig. 14(B) shows the distribution of eight-dimensional pinna shape features corresponding to each cluster obtained by the above clustering. As shown in the figure, when clustering is performed using the sixth pinna shape feature (6), significant differences occur in the HpTF in the frequency band higher than 5 kHz.

[0050] FIG. 15 shows the results of clustering using the fourth measurement data (m4) with kmeans to set the number of clusters to three. FIG. 15(A) shows the average HpTF for each cluster. FIG. 15(B) shows the distribution of 11-dimensional measurement data corresponding to each cluster obtained by the above clustering. As shown in the figure, when clustering is performed using the fourth measurement data (m4), significant differences occur in all frequency bands. As mentioned above, the fourth measurement data (m4) is the longitudinal distance between the upper end of the helix and the lower end of the earlobe, which is close to the entire longitudinal length of the pinna.

[0051] FIG. 16 shows the results of clustering using the fifth measurement data (m5) with kmeans to set the number of clusters to three. FIG. 16(A) shows the average HpTF for each cluster. FIG. 16(B) shows the distribution of 11-dimensional measurement data corresponding to each cluster obtained by the above clustering. As shown in the figure, when clustering is performed using the fifth measurement data (m5), significant differences in HpTF occur in the frequency band higher than 7 kHz. As mentioned above, the fifth measurement data (m5) refers to the distance in the transverse direction of the pinna between the nasal end of the helix in the transverse direction of the pinna (but on the medial surface of the helix) and the occipital end of the helix in the transverse direction of the pinna, which is close to the entire length of the pinna in the transverse direction.

[0052] Figure 17 shows the results of clustering using kmeans to obtain three clusters using the seventh measurement data (m7). Figure 17(A) shows the average HpTF for each cluster. Figure 17(B) shows the distribution of 11-dimensional measurement data corresponding to each cluster obtained by the above clustering. As shown in the figure, when clustering is performed using the seventh measurement data (m7), significant differences in HpTF occur in the frequency bands excluding 5-7 kHz. As mentioned above, the seventh measurement data (m7) refers to the distance in the transverse direction of the pinna between the earphone sound hole and the end of the cavity of the concha on the occipital side in the transverse direction of the pinna.

[0053] FIG. 18 shows the results of clustering using kmeans to obtain three clusters using the ninth measurement data (m9). FIG. 18(A) shows the average HpTF for each cluster. FIG. 18(B) shows the distribution of 11-dimensional measurement data corresponding to each cluster obtained by the above clustering. As shown in the figure, when clustering is performed using the ninth measurement data (m9), significant differences in the HpTF occur in frequency bands other than 5-7 kHz. As mentioned above, the ninth measurement data (m9) refers to the distance in the longitudinal direction of the pinna between the earphone sound hole and the lower end of the earlobe in the longitudinal direction of the pinna.

[0054] FIG. 19 shows the results of clustering using kmeans with the tenth measurement data (m10) so that the number of clusters is three. FIG. 19(A) shows the average HpTF for each cluster. FIG. 19(B) shows the distribution of 11-dimensional measurement data corresponding to each cluster obtained by the above clustering. As shown in the figure, when clustering is performed using the tenth measurement data (m10), significant differences in HpTF occur in the frequency bands lower than 5 kHz and higher than 10 kHz. As mentioned above, the tenth measurement data (m10) refers to the distance in the short-side direction of the pinna between the earphone sound hole and the end of the helix on the occipital side in the short-side direction of the pinna.

[0055] In the present disclosure, by configuring the selection devices 1 and 2 as described above, it is possible to provide each user with a highly accurate correction filter based on data that is easier to measure than HpTF, such as pinna shape and earphone wearing position, while avoiding the increased costs that would be incurred if HpTF were measured for each user and a custom-made correction filter were generated. This makes it possible to provide users with a more accurate stereophonic effect than the general-purpose filters of the prior art HATS, at low cost.

[0056] <Additional Notes> The device disclosed herein may, for example, be a single hardware entity that includes an input unit to which a keyboard or the like can be connected, an output unit to which an LCD display or the like can be connected, a communication unit to which a communication device (e.g., a communication cable) capable of communicating with an external device of the hardware entity can be connected, a CPU (which may also include a central processing unit, cache memory, registers, etc.), memories such as RAM and ROM, an external storage device such as a hard disk, and buses connecting these input unit, output unit, communication unit, CPU, RAM, ROM, and external storage device so as to enable data exchange. If necessary, the hardware entity may also be provided with a device (drive) capable of reading and writing recording media such as a CD-ROM. An example of a physical entity equipped with such hardware resources is a general-purpose computer.

[0057] The external storage device of the hardware entity stores the programs required to realize the above-mentioned functions and the data required for processing these programs (the programs may be stored in a ROM, which is a read-only storage device, for example, instead of an external storage device). Data obtained by processing these programs is stored in RAM, the external storage device, etc. as appropriate.

[0058] In a hardware entity, each program stored in an external storage device (or ROM, etc.) and the data required to process each program are loaded into memory as needed, and interpreted, executed, and processed by the CPU as appropriate, resulting in the CPU realizing a predetermined function (each component represented as a unit, means, etc.).

[0059] The present disclosure is not limited to the above-described embodiments, and modifications can be made as appropriate without departing from the spirit of the present disclosure. Furthermore, the processes described in the above embodiments may not only be executed in chronological order according to the order described, but may also be executed in parallel or individually depending on the processing capabilities of the devices that execute the processes or as needed.

[0060] As described above, when the processing functions of the hardware entities (apparatuses of the present disclosure) described in the above embodiments are realized by a computer, the processing contents of the functions that the hardware entities should have are described by a program, and the processing functions of the hardware entities are realized on the computer by executing this program.

[0061] The various processes described above can be implemented by loading a program that executes each step of the above method into the recording unit 10020 of the computer 10000 shown in Figure 20, and operating the control unit 10010, input unit 10030, output unit 10040, etc.

[0062] The program describing the processing contents can be recorded on a computer-readable recording medium. Examples of computer-readable recording media include magnetic recording devices, optical disks, magneto-optical recording media, and semiconductor memories. Specifically, examples of magnetic recording devices include hard disk drives, flexible disks, and magnetic tapes; optical disks include DVDs (Digital Versatile Discs), DVD-RAMs (Random Access Memory), CD-ROMs (Compact Disc Read Only Memory), and CD-Rs (Recordable / Rewritable) / RWs; magneto-optical recording media include MOs (Magneto-Optical Discs), and semiconductor memories include EEP-ROMs (Electrically Erasable and Programmable-Read Only Memory).

[0063] The program may be distributed by, for example, selling, transferring, lending, etc. portable recording media such as DVDs and CD-ROMs on which the program is recorded. Furthermore, the program may be stored in a storage device of a server computer, and then transferred from the server computer to other computers via a network, thereby distributing the program.

[0064] A computer that executes such a program may first temporarily store the program recorded on a portable recording medium or transferred from a server computer in its own storage device. Then, when executing a process, the computer reads the program stored on its own recording medium and executes the process in accordance with the read program. Alternatively, the computer may read the program directly from a portable recording medium and execute the process in accordance with the program. Furthermore, the computer may execute the process in accordance with each program transferred from the server computer. Alternatively, the server computer may not transfer the program to the computer, but may instead execute the process through a so-called ASP (Application Service Provider) service, which realizes the processing function by issuing an execution instruction and obtaining the results. In this embodiment, the program includes information used for processing by a computer that is equivalent to a program (e.g., data that is not a direct instruction to the computer but has properties that define computer processing).

[0065] In addition, in this embodiment, a hardware entity is configured by executing a predetermined program on a computer, but at least a part of the processing contents may be realized by hardware.

Claims

1. A selection device in which two or more clusters obtained by classifying measurement data relating to the pinna shapes and the wearing positions of open-ear type earphones of a plurality of listeners, or values ​​based on the measurement data, based on the degree of similarity, are associated with transfer characteristics corresponding to each cluster, which are transfer characteristics from the earphone sound hole to the outer ear canal, or correction filters based on the transfer characteristics corresponding to each cluster, and the selection device includes a selection unit that acquires the measurement data of a user and selects the transfer characteristic or the correction filter corresponding to the cluster corresponding to the measurement data of the user or the value based on the measurement data of the user.

2. A selection device in which two or more clusters classified based on the degree of similarity of transfer characteristics from the sound hole of an open-ear type earphone to the outer ear canal of a plurality of listeners are associated with measurement data corresponding to each cluster, the measurement data relating to the auricle shape of the listener and the position where the open-ear type earphone is worn, or a value based on the measurement data, and further associated with a transfer characteristic corresponding to each cluster, or a correction filter based on the transfer characteristic corresponding to each cluster, and the selection device includes a selection unit that acquires the measurement data of a user and selects the transfer characteristic or the correction filter corresponding to the cluster corresponding to the measurement data of the user or the value based on the measurement data of the user.

3. A selection device according to claim 1, wherein two or more clusters classified based on the degree of approximation of any of the values ​​of any of the principal components obtained by aggregating the measurement data through principal component analysis are associated with the corresponding transfer characteristic or correction filter.

4. A selection device according to claim 1, wherein two or more clusters classified based on the degree of approximation of any value in the measurement data are associated with the corresponding transfer characteristic or correction filter.

5. A selection device according to claim 3, wherein the selection unit selects the transfer characteristic or the correction filter corresponding to the cluster for each frequency domain corresponding to any one of the principal components.

6. A selection device according to claim 4, wherein the selection unit selects the transfer characteristic or the correction filter corresponding to the cluster for each frequency domain corresponding to any value in the measurement data.

7. A selection method executed by a selection device, wherein two or more clusters obtained by classifying measurement data relating to the pinna shapes and the wearing positions of open-ear type earphones of a plurality of listeners, or values ​​based on the measurement data, based on the degree of similarity, are associated with transfer characteristics corresponding to each cluster, which are transfer characteristics from the earphone sound hole to the outer ear canal, or correction filters based on the transfer characteristics corresponding to each cluster, and the selection method includes a step of acquiring the measurement data of a user and selecting the transfer characteristic or the correction filter corresponding to the cluster corresponding to the measurement data of the user or the value based on the measurement data of the user.

8. A program for causing a computer to function as the selection device according to any one of claims 1 to 6.

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