WO2023012920A1

WO2023012920A1 - Estimation device, estimation method, and program

Info

Publication number: WO2023012920A1
Application number: PCT/JP2021/028897
Authority: WO
Inventors: 達也加古; 賢一野口
Original assignee: 日本電信電話株式会社
Priority date: 2021-08-04
Filing date: 2021-08-04
Publication date: 2023-02-09
Also published as: JPWO2023012920A1

Abstract

Provided is a technology that uses a simulation space and a real space and makes it possible to estimate reflectance in a short amount of time. According to the present invention, an estimation device estimates the reflectance of a wall surface in a prescribed space. The estimation device includes a simulation acquisition unit that acquires a value based on the reverberation of an acoustic signal emitted from a prescribed location in a simulation space that simulates the prescribed space, a real space acquisition unit that acquires a value based on the reverberation of an acoustic signal emitted from a location that corresponds to the prescribed location in a real space that is the prescribed space, and a parameter estimation unit that estimates the reflectance of the wall surface using an evaluation value obtained using the difference between the value based on the reverberation in the simulation space and the value based on the reverberation in the real space.

Description

Estimation device, estimation method, and program

The present invention relates to an estimation technique for estimating the reflectance of walls and the like.

There are many devices that use point cloud sensors, represented by LiDAR (Light Detection and Ranging or Laser Imaging Detection and Ranging). For example, Non-Patent Document 1 describes a device capable of acquiring point cloud data inside and outside a building. Here, the point cloud data refers to data representing the presence or absence of a point for each coordinate, with a predetermined real space as a three-dimensional coordinate space. It is possible to add additional information to the point cloud data in addition to the presence or absence of a point for each coordinate, but the additional information is generally information relating to the color of the point.

When trying to simulate acoustic signals in the space using point cloud data, it is necessary to consider not only the presence or absence of points but also the reflectance of points. Note that the reflectance represents the attenuation value of the wave of the waveform when the sound is reflected. For example, if a sound hits a wall with a reflectance of 0.5, a wave with half the amplitude of the wave before reflection is reflected from the wall. When trying to measure such reflectance in the real space, impulse response is sometimes used, but it needs to be done by a skilled person taking a long time.

The purpose of the present invention is to provide a technology that enables estimation of reflectance in a short period of time using simulation space and real space.

In order to solve the above problems, according to one aspect of the present invention, an estimating device estimates the reflectance of a wall surface within a predetermined space. The estimating device includes a simulation acquisition unit that acquires a value based on the reverberation of an acoustic signal emitted from a predetermined position in a simulation space that simulates a predetermined space, and a simulation acquisition unit that corresponds to the predetermined position in the predetermined space that is the real space. A real space acquisition unit that acquires a value based on the reverberation of an acoustic signal emitted from a position where the object is located, and an evaluation value that uses the difference between the value based on the reverberation in the simulation space and the value based on the reverberation in the real space. and a parameter estimator for estimating the reflectance of the wall surface.

According to the present invention, it is possible to estimate the reflectance in a short time.

The functional block diagram of the estimation system which concerns on 1st embodiment. The figure which shows the example of the processing flow of the estimation system which concerns on 1st embodiment. FIG. 3 is a functional block diagram of an acquisition unit; The figure which shows the example of the processing flow of an acquisition part. The figure showing the example of the point cloud data after noise removal. The figure showing the example of the point cloud data after noise removal. The functional block diagram of the room shape creation part. The figure which shows the example of the processing flow of a room shape preparation part. FIG. 4 is a diagram for explaining processing of a loss complementing unit; FIG. 4 is a diagram for explaining processing of a loss complementing unit; FIG. 4 is a diagram for explaining parameter estimation by a Gaussian process; The figure which shows the table|surface of update by a Gaussian process, an evaluation function, and the estimated parameter value. The figure which shows the structural example of the computer which applies this method.

Embodiments of the present invention will be described below. It should be noted that in the drawings used for the following description, the same reference numerals are given to components having the same functions and steps that perform the same processing, and redundant description will be omitted. In the following description, processing performed for each element of a vector or matrix applies to all elements of the vector or matrix unless otherwise specified.

<Points of the first embodiment>
(1) A value based on the reverberation of an acoustic signal emitted from a predetermined position in a simulation space that simulates a predetermined space, and a value based on the reverberation of an acoustic signal emitted from a position corresponding to the predetermined position in the simulation space in a predetermined space that is the real space. The reflectivity of the wall surface is estimated so that the difference from the value based on the reverberation of the received acoustic signal is minimized.

(2) By using RT60 as a value based on the reverberation of the acoustic signal, it is possible to reduce the amount of calculation by reducing the influence of the position of the sound source and the position of the microphone compared to the impulse response.

<Estimation system according to the first embodiment>
FIG. 1 is a functional block diagram of an estimation system according to the first embodiment, and FIG. 2 shows its processing flow.

The estimation system includes an acquisition unit 110, an estimation device 100, and an impulse response output unit 190.

The estimation system acquires point cloud data of the space in which sound is to be collected via the acquisition unit 110, estimates the reflectance of a predetermined space using the point cloud data, and uses the estimated value i of the reflectance to obtain a predetermined It estimates and outputs the impulse response of an arbitrary sound source position and an arbitrary microphone position in the space of .

The estimating device 100 is, for example, a special computer configured by reading a special program into a known or dedicated computer having a central processing unit (CPU: Central Processing Unit), a main memory (RAM: Random Access Memory), etc. It is a device. The estimating apparatus 100 executes each process under the control of a central processing unit, for example. Data input to the estimating device 100 and data obtained in each process are stored, for example, in a main memory device, and the data stored in the main memory device are read out to the central processing unit as necessary and used for other purposes. used to process At least a part of each processing unit of the estimation device 100 may be configured by hardware such as an integrated circuit. Each storage unit included in the estimation device 100 can be configured by, for example, a main storage device such as RAM (Random Access Memory), or middleware such as a relational database or a key-value store. However, each storage unit does not necessarily have to be provided inside the estimating apparatus 100, and is configured by an auxiliary storage device configured by a semiconductor memory device such as a hard disk, an optical disk, or a flash memory, and the estimating apparatus 100 may be provided outside.

Each part will be explained below.

<Acquisition unit 110>
The acquiring unit 110 acquires the point cloud data R of the space in which sound is to be collected (S110) and outputs it. FIG. 3 is a functional block diagram of the acquisition unit 110, and FIG. 4 shows an example of its processing flow. For example, the acquisition unit 110 includes a spatial sensing unit 111 , a noise removing unit 113 and a spatial model combining unit 115 .

<Spatial Sensing Unit 111>
The space sensing unit 111 acquires point cloud data of the space in which sound is to be collected (S111) and outputs it. For example, the space sensing unit 111 consists of a LiDAR installed in a space where sound is collected. and find the direction of the object from the launch direction. This distance and direction are represented by point cloud data. Various conventional techniques can be used as spatial sensing techniques. For example, Reference 1 is known as an existing space sensing technology.

(Reference 1) Toshio Ito, "Principles and Applications of LiDAR Technology for Autonomous Driving," Science Information Publishing Co., Ltd., 2020 <Noise Removal Unit 113>
The noise removal unit 113 receives the point cloud data, removes noise included in the point cloud data (S113), and outputs the removed point cloud data. Various conventional techniques can be used as the noise removal technique. For example, Reference 2 is known as an existing noise removal technique.

(Reference 2) Rusu, R. B., Z. C. Marton, N. Blodow, M. Dolha, and M. Beetz, "Towards 3D Point Cloud Based Object Maps for Household Environments", Robotics and Autonomous Systems Journal, 2008.
5 and 6 show examples of point cloud data after noise removal. FIG. 5 shows point cloud data acquired with the spatial sensing unit 111 (for example, LiDAR) at an elevation angle of 0 degrees, and FIG. 6 shows point cloud data obtained with an elevation angle of −90 degrees.

<Spatial model combining unit 115>
The space model combining unit 115 receives a plurality of point cloud data, combines the plurality of point cloud data, restores the shape of the space (S115), and outputs point cloud data R representing the shape of the space. For example, while rotating the LiDAR, multiple point cloud data with different elevation angles are obtained, and the multiple point cloud data are combined using the Iterative Closest Point (ICP) algorithm to reconstruct a 3D scene. Various conventional techniques can be used as the spatial model combination technique. For example, Reference 3 is known as an existing spatial model combining technique.

(Reference 3) Szymon.R and Marc.L, "Efficient Variants of the ICP Algorithm", Proceedings Third International Conference on 3-D Digital Imaging and Modeling, 2001, pp. 145-152.
<Estimation device 100>
The estimating apparatus 100 receives point cloud data R indicating the shape of a space, estimates the reflectance of a given space using the point cloud data R, and outputs an estimated value i of the reflectance.

For example, the estimation device 100 includes a room shape generator 120, a parameter input unit 130, an impulse response estimator 140, a reverberation time calculator 160, a real environment impulse response acquirer 150, an evaluation function calculator 170, and a parameter estimator 180. (See Figure 1).

The estimating device 100 may or may not include the obtaining unit 110 in the computer that constitutes the estimating device 100 . When the acquisition unit 110 is not included, the estimation device 100 includes an input interface for inputting point cloud data (not shown). When the acquisition unit 110 is included, the processing S113 and the processing S115, or the processing S115 may be performed within the computer that constitutes the estimation device 100 . In this case, an input unit (not shown) is provided before the part corresponding to the processing performed in the computer. , or noise-removed point cloud data output from the noise removal unit 113).

<Room shape creation unit 120>
The room shape generator 120 receives the point cloud data R indicating the shape of the space, complements the shape of the space that does not appear in the point cloud data R (S120), and outputs the shape U of the complemented space.

FIG. 7 is a functional block diagram of the room shape creation unit 120, and FIG. 8 shows an example of its processing flow.

For example, the room shape creation unit 120 includes a wall surface extraction unit 121 and a loss complement unit 123.

<Wall surface extraction unit 121>
The wall surface extracting unit 121 receives the point cloud data R indicating the shape of the space, uses the shape of the space to extract the wall surfaces forming the space (S121), and outputs them.

For example, the wall surface extraction unit 121 extracts the wall surface by acquiring the parameters of the wall surface forming the space using an algorithm that is an improvement of RANSAC (Random Sample Consensus) for wall surface extraction. Examples are shown below.

(1) Divide the point cloud data R, which indicates the shape of the space, based on the observation angle. For example, in the horizontal plane, it is divided into M directions.

(2) Specify the m-th direction (ROI (region of interest)) from among the M-divided directions. However, m=1,2,...,M.

(3) Randomly acquire 3 points a, b, and c from the point cloud belonging to the ROI.

(4) Calculate the coefficients of the plane equation ax+by+cz+d=0 from 3 points a, b, c to 3 points a, b, c. For example, coefficients are calculated from the outer product of vectors ab and ac.

(5) Calculate the distance between each point of the point cloud belonging to the ROI and the plane, and obtain the number n _q of points whose distance is equal to or less than a predetermined threshold. However, q=1,2,...,Q. For example, calculate the distance d by d=n(pa). n is the normal vector of the plane containing the three points a, b, c, and p is each point of the point cloud belonging to the ROI.

(6) The above (3) to (5) are repeated Q times, and the maximum number n _m of points out of the Q numbers n _q and the coefficient corresponding to that number are taken as parameters for the direction m.

(7) The above (2) to (6) are repeated M times, and the direction with the maximum number of points is selected from the M numbers _nm .

(8) Collect points whose distance from the plane indicated by the coefficient corresponding to the direction selected in (7) above is equal to or less than the threshold, and obtain the normal vector from the collected point group. For example, the normal vector can be obtained by SVD (Singular Value Decomposition) or PCA (Principal Component Analysis) of the covariance matrix.

(9) Record the normal vector as the largest wall surface in the point cloud data.

(10) Remove the point cloud data belonging to the largest wall surface from the data and return to (1). Note that the point cloud data belonging to the largest wall surface represents the extracted wall surface.

In addition, when the ratio of the point cloud data belonging to the largest wall surface to the entire point cloud data R is less than or equal to a predetermined value, it is assumed that there is no wall surface there. That is, if the ratio of the number _nq of points below the threshold in (5) above in a certain direction is below a predetermined value, it is assumed that there is no wall there. Furthermore, in (10) above, since the point cloud data belonging to the largest wall surface is removed from the data, the number of wall surfaces that can be extracted gradually decreases and converges automatically. With such a configuration, not only the walls and ceiling but also the plane shape of the point cloud that is likely to reflect are extracted to some extent, and as a result, furniture and equipment are simulated as a space. For example, the process may be controlled to be repeated until the plane formed by the point cloud data belonging to the largest wall surface becomes equal to or less than a predetermined threshold value (for example, 20 cm square). The predetermined threshold may be estimated according to the amount of processing.

<Deficit Compensation Unit 123>
The loss complementing unit 123 receives the wall surface forming the space, and if there is an object other than the wall surface in the space and the shape of the part of the wall surface cannot be estimated, the shape of the part of the wall surface can be estimated from the estimated shape of the other part of the wall surface. The shape is estimated, the shape of the space is complemented using the estimated part of the wall surface (S123), and the shape U of the space after complementation is output.

As described above, since LiDAR uses laser reflection to measure distance, data loss occurs in areas where the laser cannot reach due to obstructions. Therefore, the missing data is supplemented from the information of the estimated wall surface, and the vertex (corner of the room) is obtained.

　In the example of Fig. 9, the display D becomes a shield, and the walls that exist in the part surrounded by the dashed line in the figure cannot be observed. Note that FIG. 9 shows point cloud data on a horizontal plane with an elevation angle of 0 degrees, and point O is the installation position of the LiDAR.

For example, the parameters of the wall surface (a, b, c, d) are extracted by plane approximation, and the vertex coordinates of the wall are obtained from the straight line. That is, as shown in FIG. 10, the point of intersection P of the straight lines is determined as the vertex coordinate of the wall, and the broken line portion in FIG. 10 is assumed to be the wall surface.

<Parameter input unit 130>
The parameter input unit 130 receives the sound source position S and the microphone position M as inputs (S 130 ) and outputs them to the impulse response estimation unit 140 . A speaker and a microphone are arranged at positions in the real space corresponding to the sound source position S and the microphone position M in the simulation space simulating the inside of a predetermined space. Therefore, the sound source position S and the microphone position M are positions where the speaker and the microphone can be arranged in the real space. One or more positions can be envisaged as the sound source position S and the microphone position M respectively. A parameter input unit 130 is an input device such as a keyboard or a mouse, and the user inputs the sound source position via the parameter input unit 130 . For example, the parameter input unit 130 receives the shape of the space after complementing, which is the output of the loss complementing unit 123 (indicated by the dashed line in FIG. 1), and uses the shape of the space after complementing via a display device such as a display. A configuration may also be adopted in which the sound source is presented to the user, and the user uses a mouse or the like to specify where in the space after complementation the sound source is to be placed. Alternatively, it may be configured to automatically input using object recognition such as Reference 4 from point cloud data R acquired from LiDAR, or to manually select from the position of the recognized object.

(Reference 4) D. Maturana and S. Scherer, "VoxNet: A 3D Convolutional Neural Network for real-time object recognition", 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, 2015, pp. 922-928, doi: 10.1109/IROS.2015.7353481.
The parameter input unit 130 is a storage medium reading device, and the estimation apparatus 100 reads the storage medium storing the sound source positions and the microphone positions via the parameter input unit 130, thereby accepting the sound source positions and the microphone positions as inputs. good too.

Alternatively, the sound source position and the microphone position may be manually input to the estimation device 100 in advance.

<Impulse response estimator 140>
The impulse response estimation unit 140 receives the sound source position S, the space shape U, the listening position M, and the estimated value R or the initial value R _ini of the reflectance, and uses these values to calculate the sound source position in the space shape U. The impulse response of S and the listening position M is estimated (S140), and the estimated value i _e of the impulse response is output. Assume that the initial value R _ini is manually input in advance. For example, R _ini =0.5. The reflectance estimate value R is the output of the parameter estimator 180, which will be described later.

For example, the impulse response estimating unit 140 uses the sound source position S, the space shape U, the estimated value R or initial value R _ini of the reflectance, and the listening position M to calculate the space of the listening position M in the space at the sound source position S. Calculate the transfer function. Various conventional techniques can be used as the spatial transfer function calculation technique. For example, the FDTD method (finite-difference time-domain method) is used to simulate the sound wave propagation from the space shape U and the estimated value R or initial value R _ini of the reflectance, and to predict the incoming sound at the listening position M. to calculate the spatial transfer function. Furthermore, the impulse response estimator 140 estimates the impulse response using the spatial transfer function.

<Real Environment Impulse Response Acquisition Unit 150>
The real environment impulse response acquisition unit 150 picks up and acquires an acoustic signal emitted from a position corresponding to the sound source position S at a position corresponding to the listening position M in a predetermined space that is the real space, and obtains the sound source position S and the position corresponding to the listening position M are measured (S150), and the measured value _im of the impulse response is output. For example, a TSP (Time Stretched Pulse) signal is reproduced from a speaker placed at a position corresponding to the sound source position S in a given space, which is the real space, and placed at a position corresponding to the listening position in the given space, which is the real space. Pick up sound with a microphone and measure the impulse response. The microphone and speaker may be separate devices, or may be an integrated device such as a smart speaker.

<Reverberation time calculator 160>
The reverberation time calculator 160 receives the impulse response estimated value i _e and the impulse response measured value _im as inputs, and calculates the reverberation time r _e of the impulse response estimated value i _e and the reverberation time of the impulse response measured value _im Calculate time r _i (S160) and output. For example, RT60 is used as the reverberation time. RT60 is the time it takes for the reverberation in the room to drop by 60dB. By using the RT60, unlike the impulse response, it does not change sensitively depending on the location or sound source position.

A combination of the impulse response estimator 140 and the reverberation time calculator 160 is used to obtain a value (RT60) based on reverberation of an acoustic signal emitted from a predetermined position (sound source position) in a simulation space simulating a predetermined space. , is also called a simulation acquisition unit.

Also, the combination of the real environment impulse response acquisition unit 150 and the reverberation time calculation unit 160 is used to obtain a value (RT60) based on the reverberation of an acoustic signal emitted from a position corresponding to a given position in a given space that is the real space. Since it acquires, it is also called a real space acquisition unit.

<Evaluation function calculator 170>
The evaluation function calculator 170 receives the reverberation time r _e and the reverberation time r _m as inputs, calculates the evaluation value E using the difference between the reverberation time r _e and the reverberation time r _m (S170), and outputs it. As an evaluation value, for example, the square error between RT60 of the estimated value i _e of the impulse response and _RT60 of the measured value im of the impulse response can be considered, and is represented by the following equation.

E=(r _e -r _m ) ²
<Parameter estimation unit 180>
The parameter estimator 180 receives the evaluation value E, estimates the reflectance of the wall surface using the evaluation value (S180), and outputs the estimated value R.

In this embodiment, the parameter estimation unit 180 performs parameter estimation using a Gaussian process.

The evaluation value E and the parameter to be manipulated are used as items necessary for parameter estimation by Gaussian process. For example, the manipulated parameter is the reflectance of each wall.

Use the observation points to narrow down the regression curve that follows the Gaussian distribution to the range of true values.

Efficiently search for which points to obtain to obtain the maximum value according to the Gaussian process.

FIG. 11 is a diagram for explaining parameter estimation by a Gaussian process. The vertical axis of the figure represents the evaluation value, and the horizontal axis represents the reflectance index.

The parameter estimating unit 180 uniquely obtains an evaluation function from each parameter, predicts a regression curve using a Gaussian distribution, sequentially updates the predicted parameter (reflectance) that minimizes the evaluation value E, and uses a high-speed evaluation function. It is possible to search for the lowest value.

When the number of iterations is equal to or less than the predetermined number (NO in S185), the parameter estimation section 180 outputs the updated parameters to the impulse response estimation section 140, performs processes S140 to S180, and repeats parameter estimation.

When the number of iterations exceeds a predetermined number (YES in S185), the parameter estimator 180 outputs the parameter with the minimum evaluation function as the final reflectance estimated value _Rf .

<Impulse Response Output Unit 190>
The impulse response output unit 190 receives the sound source position S′, the spatial shape U, the listening position M′, and the final reflectance estimate value R _f as inputs, and uses these values to calculate the response at the sound source position S′. The impulse response of the listening position M' in space is estimated (S190), and the estimated value i of the impulse response is output. For example, the impulse response is estimated by the same method as the impulse response estimator 140 . Impulse responses at various positions can be estimated while changing the sound source position S' and the listening position M'.

The estimating device 100 may or may not include the impulse response output unit 190 in the computer configuring the estimating device 100 .

<effect>
With the configuration described above, the reflectance can be estimated in a short time. In addition, by using RT60, it is possible to reduce the amount of calculation by reducing the influence of the position of the sound source and the position of the microphone compared to the impulse response.

By applying this embodiment using microphones and speakers included in smart speakers and equipment with positioning functions such as LiDAR, accurate parameters such as the surrounding structures and the reflectance of structures can be estimated. be able to. Furthermore, it is possible to estimate the place of use and apply the accurate simulation information to acoustic processing (noise suppression, reverberation suppression).

<Modification>
In this embodiment, the spatial shape U, which is the output value of the acquisition unit 110, is used as the input of the impulse response estimation unit 140. may be used as an input for In this case, acquisition unit 110 and room shape creation unit 120 may not be included.

Although RT60 is used as the value based on the reverberation of the acoustic signal in this embodiment, the impulse response may be used as the value based on the reverberation of the acoustic signal. In this case, estimation device 100 does not need to include reverberation time calculation section 160 . However, as mentioned above, by using RT60, the influence of the position of the sound source and the position of the microphone can be reduced compared to the impulse response, and the amount of calculation can be reduced.

Although E=(r _e −r _m ) ² is used as the evaluation value in this embodiment, other evaluation values may be used. For example, E=-(r _e -r _m ) ² may be used. In this case, the parameter estimator 180 outputs the parameter with the maximum evaluation function as the final reflectance estimated value R _f .

In short, the difference between the value based on the reverberation in the simulation space and the value based on the reverberation in the real space is used. , _the final estimated reflectance value R _f And it is sufficient.

<Simulation result>
Estimation by the FDTD method takes a very long time, so the mirror image method is used in this simulation. Pyroomacoustics is used for the mirror image method. The true value of the reflectance is set to 0.15, and the RT60 norm is used as the evaluation function instead of the L2 norm of the impulse response.

As a result of estimating one variable, it was confirmed that using the RT60 difference approached the true value, although the accuracy was lower than directly calculating the impulse response difference. It was found that RT60 has less error than the impulse response when filling the difference with the actual measurement value.

FIG. 12 is a table of updates by Gaussian processes, evaluation functions, and estimated parameter values. In FIG. 12, iter represents the number of iterations, target represents the evaluation function, and var_abs represents the reflectance. A true value of 0.15 maximizes the evaluation function at 13 iterations. In this simulation, the evaluation function (squared error) is multiplied by a negative value to search for a variable that maximizes the evaluation function.

<Other Modifications>
The present invention is not limited to the above embodiments and modifications. For example, the various types of processing described above may not only be executed in chronological order according to the description, but may also be executed in parallel or individually according to the processing capacity of the device that executes the processing or as necessary. In addition, appropriate modifications are possible without departing from the gist of the present invention.

<Program and recording medium>
The various processes described above can be performed by loading a program for executing each step of the above method into the storage unit 2020 of the computer shown in FIG. .

A program that describes this process can be recorded on a computer-readable recording medium. Any computer-readable recording medium may be used, for example, a magnetic recording device, an optical disk, a magneto-optical recording medium, a semiconductor memory, or the like.

In addition, the distribution of this program is carried out, for example, by selling, assigning, lending, etc. portable recording media such as DVDs and CD-ROMs on which the program is recorded. Further, the program may be distributed by storing the program in the storage device of the server computer and transferring the program from the server computer to other computers via the network.

A computer that executes such a program, for example, first stores the program recorded on a portable recording medium or the program transferred from the server computer once in its own storage device. Then, when executing the process, this computer reads the program stored in its own recording medium and executes the process according to the read program. Also, as another execution form of this program, the computer may read the program directly from a portable recording medium and execute processing according to the program, and the program is transferred from the server computer to this computer. Each time, the processing according to the received program may be executed sequentially. In addition, the above-mentioned processing is executed by a so-called ASP (Application Service Provider) type service, which does not transfer the program from the server computer to this computer, and realizes the processing function only by its execution instruction and result acquisition. may be It should be noted that the program in this embodiment includes information that is used for processing by a computer and that conforms to the program (data that is not a direct instruction to the computer but has the property of prescribing the processing of the computer, etc.).

In addition, in this embodiment, the device is configured by executing a predetermined program on a computer, but at least part of these processing contents may be implemented by hardware.

Claims

An estimating device for estimating the reflectance of a wall surface in a predetermined space,
a simulation acquisition unit that acquires a value based on reverberation of an acoustic signal emitted from a predetermined position in the simulation space that simulates the predetermined space;
a real space acquisition unit that acquires a value based on reverberation of an acoustic signal emitted from a position corresponding to the prescribed position in the prescribed space that is the physical space;
a parameter estimation unit that estimates the reflectance of a wall surface using an evaluation value using a difference between a value based on reverberation in the simulation space and a value based on reverberation in the real space;
estimator including
The estimating device of claim 1,
an evaluation function calculation unit that obtains, as the evaluation value, a value based on a squared error between a value based on the reverberation in the simulation space and a value based on the reverberation in the real space;
estimation device.
The estimating device according to claim 1 or claim 2,
The parameter estimation unit predicts a regression curve using a Gaussian distribution, and estimates the reflectance at which the evaluation value is the minimum or maximum.
estimation device.
The estimation device according to any one of claims 1 to 3,
The simulation acquisition unit
an impulse response estimator for estimating impulse responses of a sound source position and a listening position in a simulation space simulating the predetermined space;
a reverberation time calculator that calculates the reverberation time of the impulse response estimate;
The real space acquisition unit
Acoustic signals emitted from a position corresponding to the sound source position are picked up at a position corresponding to the listening position in the predetermined space that is the physical space, and the position corresponding to the sound source position and the listening position are acquired. a real-environment impulse response acquisition unit that measures an impulse response at a position corresponding to
a reverberation time calculator that calculates the reverberation time of the impulse response measurement;
The reverberation-based value in the simulation space is the reverberation time of the impulse response estimate,
the value based on reverberation in the real space is the reverberation time of the impulse response measurement;
estimation device.
An estimation method for estimating the reflectance of a wall surface in a predetermined space using an estimation device,
a simulation acquisition step in which the estimation device acquires a value based on reverberation of an acoustic signal emitted from a predetermined position in a simulation space that simulates the predetermined space;
a real space obtaining step in which the estimation device obtains a value based on reverberation of an acoustic signal emitted from a position corresponding to the predetermined position in the predetermined space that is the physical space;
a parameter estimation step in which the estimation device estimates the reflectance of the wall surface using an evaluation value using a difference between a value based on the reverberation in the simulation space and a value based on the reverberation in the real space;
Estimation method including .
A program for causing a computer to function as the estimation device according to any one of claims 1 to 4.