Database management device and database management method

Abstract

A database management device according to the present invention comprises: a feature amount extraction unit that extracts, from input data of a head-related transfer function, a spectral cue set of the head-related transfer function; and a database updating unit that generates a node α (the size of the node α is 1) which is labeled with the extracted spectral cue set and that updates an HRTF database on the basis of the distance between the node α and a node αi (i=1,..., K, where K represents the number of nodes included in the HRTF database) which is included in the HRTF database.

Description

Database management device and database management method

[0001] The present invention relates to a database technology, and more particularly to a database for managing head-related transfer functions.

[0002] The head-related transfer function (HRTF) plays a central role in perceiving the direction of a sound (see Non-Patent Document 1). For example, the following findings have been obtained regarding the relationship between HRTF spectral cues (peak frequencies, notch frequencies, peak magnitudes, and notch magnitudes) and stereophonic sound.

[0003] (1) When an HRTF of another individual having the same spectral cues as the HRTF of the individual is used for that individual, the individual is likely to perceive the effect of stereophonic sound.

[0004] (2) The HRTFs of individuals who are sensitive to the effects of 3D sound can be roughly classified into about 10 patterns, and individuals whose HRTFs do not fall into any of these patterns cannot perceive the effects of 3D sound. Also, individuals whose ear canals are asymmetrical are unlikely to fall into any of these patterns.

[0005] HRTFs can be applied to a variety of 3D sound systems, but if we were to record the HRTF data of each individual as is, the amount of data would be enormous. However, if we record only the HRTF data of a small number of individuals, such as those who represent the 10 patterns mentioned above, and record the spectral cues of each individual's HRTF in a tabular database, we can build a 3D sound system that can be applied to everyone.

[0006] Kazuhiro Iida, "Head-Related Transfer Function and Acoustic Virtual Reality," pp.76-82, Springer, 2019.

[0007] However, in a tabular database, as the number of records (the number of HRTF spectral cues recorded in the database) increases, the efficiency of searches using spectral cues as keys decreases.

[0008] Therefore, an object of the present invention is to provide a database for head-related transfer functions in which the efficiency of searches using spectral cues as keys does not decrease even if the number of spectral cues recorded in the database increases.

[0009] One aspect of the present invention is a database management device that manages an HRTF database, which is a database for managing head-related transfer functions, and the HRTF database is configured as a graph having nodes labeled with one or more sets of spectral cues and edges representing the proximity between the nodes. The database management device includes: a feature extraction unit that extracts sets of spectral cues for head-related transfer functions from input head-related transfer function data; and a database update unit that generates nodes α (where the size of node α is 1) labeled with the extracted sets of spectral cues and updates the HRTF database based on the distance between node α and node αi (i = 1, ..., K, K represents the number of nodes included in the HRTF database).

[0010] According to the present invention, it is possible to prevent a decrease in efficiency of searches using spectral cues as keys even if the number of spectral cues recorded in the database increases.

[0011] It is a block diagram showing the configuration of the database management device 100. It is a flowchart showing the operation of the database management device 100. It is a diagram showing an example of the functional configuration of a computer that realizes each device in the embodiment of the present invention.

[0012] Hereinafter, an embodiment of the present invention will be described in detail. Note that components having the same functions are given the same numbers and redundant explanations will be omitted.

[0013] <Technical Background> In an embodiment of the present invention, a graph is used as the structure of a database for head-related transfer functions. Hereinafter, this database will be referred to as an HRTF database. When constructing the HRTF database, a head-related transfer function is represented by a set of spectral cues (i.e., multiple spectral cues). The HRTF database is then constructed as a graph having nodes labeled with one or more sets of spectral cues and edges representing the proximity between the nodes. In this way, one node corresponds to one or more head-related transfer functions. Here, the number of head-related transfer functions associated with a node (i.e., the number of sets of spectral cues labeled with the node) is referred to as the size of the node. Therefore, the size of a node will be represented by a natural number greater than or equal to 1.

[0014] Consider the case where a new node α is added to the HRTF database. Here, node α corresponds to one head-related transfer function. In other words, node α is a node labeled with one set of spectral cues. In this case, an edge connecting node αi and node α contained in the HRTF database is generated according to the following rules.

[0015] (Rule 1) For each spectral cue included in the label assigned to a node, if the distance between node αi and node α calculated using that spectral cue is less than or equal to a predetermined value, an edge is generated connecting node αi and node α. Here, the distance between node αi and node α calculated using a certain spectral cue is, for example, the absolute value of the difference between the representative value of that spectral cue included in the label of node αi (for example, the value of that spectral cue included in the set of spectral cues recorded first in the HRTF database among the set of one or more spectral cues included in the label of node αi) and the value of that spectral cue included in the label of node α.

[0016] Therefore, if N is the number of spectral cues included in the label attached to the node, the number of edges connecting node αi and node α is 0 to N inclusive.

[0017] Furthermore, if the number of edges connecting node αi and node α is N, node α'i is generated by absorbing node α into node αi according to three rules: (Rule 2-1), (Rule 2-2), and (Rule 2-3).

[0018] (Rule 2-1) The size of node α'i is set to the size of node αi plus 1. By doing so, the size of node α'i represents the number of head-related transfer functions associated with node α'i.

[0019] (Rule 2-2) The label of node α'i is the union of a set whose elements are one or more pairs of spectral cues included in the label of node αi and a set whose element is one pair of spectral cues included in the label of node α. In other words, the label of node αi is the union of a set whose elements are one or more pairs of spectral cues included in the label of node α. M} (where S1(i=1, 2, …, M) is the set of spectral cues included in the label of node αi), and the label of node α is {S} (where S is the set of spectral cues included in the label of node α), then the label of node α'i is {S1, S2, …, S M , S}.

[0020] (Rule 2-3) The edge connecting node α'i to a node in the HRTF database other than node αi is the same as the edge connecting node αi to a node in the HRTF database other than node αi. In other words, the edge connecting node α'i to a node in the HRTF database other than node αi is a continuation of the edge connecting node αi to a node in the HRTF database other than node αi.

[0021] Therefore, the HRTF database has the following characteristics:

[0022] (1) Since larger nodes correspond to more individual HRTFs, larger nodes are considered to be more versatile.

[0023] (2) Although a graph may contain nodes that are not connected to other nodes (i.e., isolated nodes), it is expected that the number of isolated nodes will decrease as the number of nodes increases.

[0024] (3) It is possible to find the number of dominant nodes in the graph and the nodes that have edges between many nodes.

[0025] First Embodiment A database management device 100 manages a database for managing head-related transfer functions (hereinafter referred to as an HRTF database). The HRTF database is configured as a graph having nodes labeled with sets of one or more spectral cues and edges representing the proximity between the nodes.

[0026] The database management device 100 will be described below with reference to FIGS. 1 and 2. FIG. 1 is a block diagram showing the configuration of the database management device 100. FIG. 2 is a flowchart showing the operation of the database management device 100. As shown in FIG. 1, the database management device 100 includes a feature extraction unit 110, a database update unit 120, and a recording unit 190. The recording unit 190 is a component that appropriately records information necessary for processing by the database management device 100. The recording unit 190 records, for example, an HRTF database.

[0027] The operation of the database management device 100 will be described with reference to FIG.

[0028] In S110, the feature extraction unit 110 extracts and outputs a set of spectral cues of the head-related transfer function from the input data of the head-related transfer function. The feature extraction unit 110 extracts, for example, the frequency of the first peak P1, the frequency of the first notch N1, the frequency of the second peak P2, and the frequency of the second notch N2 as the spectral cues of the head-related transfer function. Furthermore, for example, the feature extraction unit 110 extracts, for example, the magnitude of the first peak P1, the magnitude of the first notch N1, the magnitude of the second peak P2, and the magnitude of the second notch N2 as the spectral cues of the head-related transfer function. Any peak detection algorithm can be used to extract the frequency / magnitude of the first peak P1 and the frequency / magnitude of the second peak P2. Furthermore, the frequency / magnitude of the first notch N1 and the frequency / magnitude of the second notch N2 can be extracted using the peak detection algorithm used to extract the frequency / magnitude of the first peak P1 and the frequency / magnitude of the second peak P2 by inverting the positive and negative signs of the input head-related transfer function data. Therefore, the set of spectral cues extracted by the feature extraction unit 110 is configured using one or more spectral cues from the frequency of the first peak P1, the frequency of the first notch N1, the frequency of the second peak P2, the frequency of the second notch N2, the magnitude of the first peak P1, the magnitude of the first notch N1, the magnitude of the second peak P2, and the magnitude of the second notch N2.

[0029] In S120, the database update unit 120 receives as input the set of spectral cues output in S110, generates a node α (where the size of node α is 1) labeled with the set of spectral cues, and for each node αi (i = 1, ..., K, where K represents the number of nodes included in the HRTF database) included in the HRTF database, generates an edge connecting node αi and node α if the distance between node αi and node α, calculated using the spectral cues for each spectral cues included in the label assigned to the node, is less than or equal to a predetermined value, and generates a single node in which node αi has been absorbed into node αi (where the label of the node is the union of a set whose elements are one or more sets of spectral cues included in the label of node αi and a set whose element is one set of spectral cues included in the label of node αi, and the size is the value obtained by adding 1 to the size of node αi), thereby updating the HRTF database. In other words, the database update unit 120 generates a node α (where the size of node α is 1) labeled with the set of spectral cues extracted in S110, and updates the HRTF database based on the distance between node α and node αi (i = 1, ..., K, where K represents the number of nodes included in the HRTF database).

[0030] The database management device 100 can be applied to applications other than managing head-related transfer functions. For example, the database management device 100 can create and manage a HeSTF database for managing HeSTFs using HeSTFs (Hearable Speaker Transfer Functions) behind the ears instead of head-related transfer functions. Managing an HRTF database and a HeSTF database enables efficient cross-referencing of two graph nodes corresponding to a given individual, since HeSTFs exhibit similar trends to HRTFs. Cross-referencing between an HRTF database and a HeSTF database makes it possible to interpolate unmeasured angular direction data (i.e., angular direction data for which no true data exists) in angular direction data that is only roughly measured from angular direction data that is precisely measured between HRTFs and HeSTFs with different elevation angles or azimuth angles.

[0031] According to an embodiment of the present invention, it is possible to prevent a decrease in efficiency of searches using spectral cues as keys even if the number of spectral cues recorded in the database increases.

[0032] <Additional Notes> The functions performed by the components described herein may be implemented in circuitry or processing circuitry, including general-purpose processors, application-specific processors, integrated circuits, ASICs (Application Specific Integrated Circuits), CPUs (Central Processing Units), conventional circuits, and / or combinations thereof, programmed to perform the described functions. A processor includes transistors and other circuits and is considered to be circuitry or processing circuitry. A processor may also be a programmed processor that executes a program stored in a memory.

[0033] In this specification, a circuitry, unit, or means is hardware that is programmed to realize or performs the described functions, which may be any hardware disclosed herein or any hardware known to be programmed to realize or perform the described functions.

[0034] If the hardware is a processor considered to be a type of circuitry, the circuitry, means, or unit is a combination of the hardware and software used to configure the hardware and / or processor.

[0035] The various processes described above can be implemented by loading a program that executes each step of the above method into the recording unit 2020 of the computer 2000 shown in Figure 3, and operating the control unit 2010, input unit 2030, output unit 2040, display unit 2050, etc.

[0036] The program describing the processing contents can be recorded on a computer-readable recording medium, which may be, for example, a magnetic recording device, an optical disk, a magneto-optical recording medium, a semiconductor memory, or any other suitable recording medium.

[0037] 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.

[0038] 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 in its storage device 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 the program each time a program is transferred from a server computer to the 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. Furthermore, the server computer may execute the process at the terminal using a so-called SaaS (Software as a Service) service, which allows users to use part of the server computer along with the program. In this embodiment, the program includes information used for processing by an electronic computer that is equivalent to a program (such as data that is not a direct instruction to a computer but has properties that dictate computer processing).

[0039] Furthermore, in this embodiment, the device is configured by executing a predetermined program on a computer, but at least a part of the processing contents may be realized by hardware.

[0040] The present invention is not limited to the above-described embodiments, and various modifications can be made without departing from the spirit of the present invention. 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 capacity of the device that executes the processes or as needed.

Claims

1. A database management device for managing an HRTF database, which is a database for managing head-related transfer functions, wherein the HRTF database is configured as a graph having nodes labeled with one or more sets of spectral cues and edges representing the proximity between the nodes, the database management device comprising: a feature extraction unit that extracts sets of spectral cues for the head-related transfer functions from input head-related transfer function data; and a database update unit that generates node α (where the size of node α is 1) labeled with the extracted sets of spectral cues and updates the HRTF database based on the distance between node α and node αi (i=1, ..., K, K represents the number of nodes included in the HRTF database).

2. A database management device according to claim 1, wherein the database update unit generates a node α (where the size of node α is 1) labeled with the extracted set of spectral cues, and for node αi (i = 1, ..., K, K represents the number of nodes included in the HRTF database) included in the HRTF database, if the distance between node αi and node α, calculated using the spectral cues for each spectral cue included in the label assigned to the node, is less than or equal to a predetermined value, generates an edge connecting node αi and node α, and if the number of edges connecting node αi and node α matches the number of spectral cues included in the label assigned to the node, generates a single node in which node α is absorbed into node αi (where the label of the node is the union of a set whose elements are one or more sets of spectral cues included in the label of node αi and a set whose element is one set of spectral cues included in the label of node α, and the size is the size of node αi plus 1), thereby updating the HRTF database.

3. A database management device according to claim 1, wherein the set of spectral cues extracted by the feature extraction unit is constructed using one or more spectral cues from the frequency of the first peak P1, the frequency of the first notch N1, the frequency of the second peak P2, the frequency of the second notch N2, the magnitude of the first peak P1, the magnitude of the first notch N1, the magnitude of the second peak P2, and the magnitude of the second notch N2.

4. A database management method in which a database management device manages an HRTF database, which is a database for managing head-related transfer functions, wherein the HRTF database is configured as a graph having nodes labeled with one or more sets of spectral cues and edges representing the proximity between the nodes, the database management method comprising: a feature extraction step in which the database management device extracts sets of spectral cues for the head-related transfer functions from input head-related transfer function data; and a database update step in which the database management device generates node α (where the size of node α is 1) labeled with the extracted set of spectral cues, and updates the HRTF database based on the distance between node αi (i=1, ..., K, K represents the number of nodes included in the HRTF database) included in the HRTF database and node α.

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