JP2015131069A - Impression estimation device, method thereof, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a technology capable of estimating preference of music in real time.SOLUTION: Impression estimation devices 10 and 20 include: feature amount extraction parts 13 and 23 for extracting a first feature amount corresponding to a feature of a micro saccade that appears in a movement of a person's eyeball in a first time section, and a second feature amount corresponding to a feature of a micro saccade that appears in a movement of a person's eyeball in a second time section, the first time section being a time section in which first music is presented to the person so as to be listened to, and the second time section being a time section in which second music different from the first music is presented to the person so as to be listened to; and preference estimation parts 14 and 24 for estimating the impression of the first music and the second music based on the extent of differences between the first feature amount and the second feature amount.

Description

  The present invention relates to a technique for estimating an impression (like / dislike) of music.

  In Non-Patent Document 1, it was confirmed that dopamine tends to be released in the striatum when listening to favorite music and further expecting to listen before listening.

V. N. Salimpoor, M. Benovoy, K. Larcher, A. Dagher & R. J. Zatorre, "Anatomically distinct dopamine release during anticipation and experience of peak emotion to music", Nature Neuroscience 14 (2), 2011, pp.257-262.

  According to Non-Patent Document 1, using the fact that the amount of dopamine released tends to change depending on whether or not you like a certain music and the expectation for a certain music, Or expectation). However, since a device (PET: positron emission tomography) used for measuring the amount of released dopamine has a low temporal resolution and takes a long time to analyze, Non-Patent Document 1 cannot estimate music preferences in real time.

  An object of this invention is to provide the technique which can estimate a music preference in real time.

  In order to solve the above-described problem, according to the first aspect of the present invention, the impression estimation device sets the first time interval as a time interval to be presented to a human so that the first music can be received, and the second time interval. The time interval is a time interval that is presented to a human so that a second music different from the first music can be received, and the first feature corresponds to a microsaccade feature that appears in the movement of the human eyeball in the first time interval. Difference between the first feature amount and the second feature amount, and a feature amount extraction unit that extracts the second feature amount corresponding to the feature of the microsaccade appearing in the movement of the human eyeball in the second time interval A preference estimation unit that estimates impressions of the first music and the second music based on the degree of the music.

  In order to solve the above problem, according to a second aspect of the present invention, an impression estimation method uses a first time interval as a time interval to be presented to a human so that the first music can be received, The time interval is a time interval that is presented to a human so that a second music different from the first music can be received, and the first feature corresponds to a microsaccade feature that appears in the movement of the human eyeball in the first time interval. The feature amount extraction step of extracting the amount and the second feature amount corresponding to the feature of the microsaccade appearing in the movement of the human eyeball in the second time interval, and the difference between the first feature amount and the second feature amount A preference estimation step of estimating an impression of the first music and the second music based on the degree of the first music.

  In order to solve the above problem, according to a third aspect of the present invention, an impression estimation method includes a feature amount and music corresponding to a feature of a microsaccade appearing in a movement of a human eyeball in a model storage unit. As a model that stores the preference estimation model that describes the correlation with the impression of a person's impression, it appears in the movement of the human eyeball during the time interval that is presented to the person so that the music of the impression estimation target can be heard The feature amount extraction step for extracting the feature amount corresponding to the feature of the microsaccade and the preference estimation step input the feature amount extracted in the feature amount extraction step and refer to the preference estimation model, thereby Estimate the impression of music.

  According to the present invention, there is an effect that music preference for each individual can be estimated in real time.

The figure showing a microsaccade. The functional block diagram of an impression estimation apparatus. The figure which shows the example of the processing flow of an impression estimation apparatus. Diagram for explaining a reference amplitude A, of the overshoot amplitude A _o and the rise time T _p, the maximum velocity V _max and velocity V _o of the overshoot. FIG. 5A is a diagram illustrating a waveform of music to be listened to in the experiment, and FIG. 5B is a diagram illustrating an evaluation result evaluated by the subject with respect to the music of FIG. 5A. The figure which shows an experimental result. Diagram for explaining the velocity V _d of the drift.

  Hereinafter, embodiments of the present invention will be described. In the drawings used for the following description, constituent parts having the same function and steps for performing the same process are denoted by the same reference numerals, and redundant description is omitted.

<First embodiment>
In the present embodiment, it is estimated whether or not the person likes the music based on minute jumping eye movements (hereinafter also referred to as microsaccade) in which the movement of the person's eyeball appears. First, microsaccade will be described. When a person is gazing at a certain point, the eyeball does not stop moving completely, but three types of eye movements (also known as drift and trend), trema, and micro Saccade (may be called flick)). Drift is a small smooth movement, trema is a very small high-frequency vibration, and microsaccade is a small jumping movement. FIG. 1 is a diagram showing a microsaccade, where the horizontal axis represents time (seconds) and the vertical axis represents the viewing angle. In detail, a microsaccade is a movement of an eyeball that appears (non-voluntarily) regardless of the intention of the subject about once in 1 to 2 seconds in a state of gazing at a certain point. It is a small jumping movement (the bold line portion in FIG. 1). Microsaccades can be obtained from either the horizontal or vertical component of motion. However, based on the property that the microsaccade is deflected in the horizontal direction, in this embodiment, only the component in the horizontal direction is used for simplicity. The “horizontal direction” does not mean that the direction is completely parallel to the ground, but the horizontal direction with respect to the face (the direction of eyeball arrangement, which may be referred to as the horizontal direction or the width direction) This is a concept including a direction defined as a horizontal direction in an eye movement acquisition unit 12 to be described later.

FIG. 2 is a functional block diagram of the impression estimation apparatus 10 according to the first embodiment, and FIG. 3 is a diagram showing an example of the processing flow.
The impression estimation device 10 includes a sound presentation unit 11, an eye movement acquisition unit 12, a feature amount extraction unit 13, and a preference estimation unit 14.

<Sound presentation unit 11>
The sound presentation unit 11 presents two different pieces of music to a person (hereinafter also referred to as a target person) so that they can be heard in different time intervals (s11). For example, each music is presented at a volume that can be heard through headphones or speakers. The two different music may be in different time intervals in the same music, or may be part or all of different music. A time interval that is presented to a person so that one music can be listened to is a first time interval, and a time period that is presented to a person so that the other music can be heard is a second time interval. Note that the lengths of the first time interval and the second time interval may be different.

<Eye Movement Acquisition Unit 12>
The eye movement acquisition unit 12 acquires the position information of the eyeball of the subject in each of the first time interval and the second time interval (s12), and outputs it to the feature amount extraction unit 13. For example, while the subject is presenting each piece of music (in the first time interval and the second time interval), the subject is asked to pay attention to a certain point, and the movement of the eyeball at that time is detected using an infrared camera. Take an image. And the time series of the position of the eyeball for every time (for example, 1000 Hz) is acquired as position information of the eyeball by performing image processing on the captured result. Note that the position information of both the left and right eyeballs may be acquired, or only the position information of one of the eyeballs may be acquired. In the present embodiment, only the position information of one eyeball is acquired.

<Feature Extraction Unit 13>
The feature quantity extraction unit 13 receives the position information of the eyeballs for each of the first time interval and the second time interval, and from the time series of the position information of the eyeballs, A feature amount corresponding to the saccade feature is extracted (s13) and output to the preference estimation unit 14.

  For example, a first-order difference series is calculated for a time series of eyeball position information, and a time section in which the absolute value of the difference series exceeds a predetermined threshold is detected as a section where microsaccade is occurring. For example, when it is determined that the acquired position information of the eyeball includes a lot of noise, a moving average value in an appropriate range may be used in calculating the first-order difference series. The threshold used for detection is preferably a value about 6 times the standard deviation of the difference series.

The feature amount can be said to be an index for estimating an impression of music. In other words, in the time series of the position information of the eyeball, the feature amount represents the eye movement in a section where the position of the eyeball changes greatly (section where microsaccade occurs). Specifically, the feature amount is an attenuation rate λ, an attenuation coefficient ζ, or a microsoccer when a time series of eyeball positions when microsaccade is occurring is modeled as a step response of the position control system. This is a feature amount including at least one of the occurrence frequency P. The step response of the position control system is the natural angular frequency ω _n

と表される。ここでG(s)は伝達係数,y(t)は位置, y'(t)は速度を表し、

It is expressed. Where G (s) is the transfer coefficient, y (t) is the position, y '(t) is the velocity,

It is expressed. Here, t is an index representing time, and s is a parameter (complex number) by Laplace transform. The natural angular frequency ω _n corresponds to an index representing the response speed of the microsaccade, and the damping coefficient ζ corresponds to an index corresponding to the accuracy of the microsaccade response. A, V _max , A _o , V _o , and T _p represent the following (see FIG. 4).

(1) Reference amplitude A: A movement amount when the movement of the eyeball by the microsaccade converges.
(2) Maximum speed V _max : Maximum speed until reaching the reference amplitude A + overshoot amplitude A _o .
(3) Overshoot amplitude A _o : The amount of the portion that exceeds (overshoots) the reference amplitude A by the microsaccade. The overshoot is a phenomenon in which the waveform protrudes beyond the reference amplitude A at the rising portion of the waveform, or the protruding waveform. In other words, the overshoot amplitude is the amount of the protruding portion.
(4) Overshoot speed V _o : This is the maximum speed when attempting to converge to reference amplitude A from reference amplitude A + overshoot amplitude A _o .
(5) Rise time T _p : Time required to reach (rise) the reference amplitude A + the overshoot amplitude A _o . The reference amplitude A + overshoot amplitude A _o time to reach of the same value as the time it takes from the maximum velocity V _max reach speeds V _o overshoot.

  Note that the attenuation rate λ and the attenuation coefficient ζ are modeled as step responses of the position control system for each microsaccade when multiple microsaccades are generated in the first time interval. A representative value (for example, an average value) of all the calculated attenuation rates λ or attenuation coefficients ζ within the first time interval is used as a feature amount in the first time interval. The same applies to the feature amount in the second time interval. As the representative value, in addition to the average value, a maximum value, a minimum value, a value corresponding to the first microsaccade in the time interval, and the like may be used.

<Preference estimation unit 14>
The preference estimation unit 14 is based on the difference between the feature amounts of the first time interval and the second time interval extracted by the feature amount extraction unit 13, and is selected from the two different music presented by the sound presentation unit. The impression about at least one is estimated (s14). Here, the impression is related to whether or not it likes, and means that “good” means that it is preferable, and “not good” means that it does not like it. For example, the preference estimation unit 14 estimates whether or not the target person likes one of two different music compared to the other music. Alternatively, the preference estimation unit 14 estimates which of the two different music the subject person prefers.

  Specifically, when the feature quantity is the attenuation rate λ or the occurrence frequency P, the music presented in the time section with the larger feature quantity has a better impression than the music presented in the other time section. (The degree of preference is large).

  Alternatively, when the feature amount is the attenuation coefficient ζ, the music presented in the time interval with the smaller feature amount has a better impression than the music presented in the other time interval (the degree of preference is greater) ).

This is based on the fact that the following correlation exists among the attenuation coefficient ζ, the attenuation rate λ, the occurrence frequency P, and the impression of music.
(1) When the impression is good (when it gets better), the damping coefficient ζ tends to decrease.
(2) When the impression is good (when it gets better), the attenuation factor λ tends to increase. Note that the attenuation coefficient and the attenuation rate have a negative correlation.
(3) When the impression is good (when it gets better), the microsaccade frequency P tends to increase.

  Note that any one of the attenuation coefficient ζ, the attenuation rate λ, and the occurrence frequency P may be used alone or in combination. For example, any two may be satisfied, all three may be satisfied, and the like may be set. That is, the impression of music may be estimated based on each of one or more feature amounts of the attenuation coefficient ζ, the attenuation rate λ, and the occurrence frequency P.

  The attenuation coefficient ζ is an index corresponding to the accuracy of the response when the microsaccade is viewed as a step response of the position control system (secondary delay system). When listening to your favorite music, being focused on the music can temporarily affect the brain's central or extraocular muscles involved in microsaccade control, resulting in accurate response (attenuation). It can be observed as a change in the coefficient.

  Hereinafter, an example of the experiment will be described with reference to FIG. The music represented by the waveform of FIG. 5A is presented to the subject. While listening to music, the subject turns eleven steps from 0 to 10 to evaluate the “rating” of the music from time to time. FIG. 5B shows the evaluation results. Note that rating indicates that 5 is neutral (a state that is neither favorite nor disliked), and a larger value (closer to 10) indicates that the user likes the music. In addition, it set so that evaluation might be set to 0 between music. The above feature amount was measured from the microsaccade of the subject at this time. The time zone judged as “Like” (in this experiment, the time zone where rating ≧ 7) and the time zone judged as “dislike” (in this experiment, the time zone where rating ≦ 4) FIG. 6A and FIG. 6B show the occurrence frequency and attenuation coefficient of microsaccades in FIG. The frequency of occurrence was determined by dividing the number of microsaccades occurring in the time zone where rating ≧ 7 or rating ≦ 4 by the time (seconds). As the attenuation coefficient, an average of attenuation coefficients of microsaccades generated in a time zone where rating ≧ 7 or rating ≦ 4 was used. In addition, the center line segment of each value represents the standard deviation of each value.

  As a result, the occurrence frequency and attenuation rate of microsaccades tend to increase and the attenuation coefficient tend to decrease during the time period determined as “like” compared to the time period determined as “dislike”. confirmed. That is, when the occurrence frequency and attenuation rate of microsaccades in the time zone determined as “like” and the occurrence frequency and attenuation rate of microsaccades in the time zone determined as “dislike” are respectively compared, The occurrence frequency and attenuation rate of the microsaccade in the time zone determined as “like” tend to be larger than the occurrence frequency and attenuation rate of the microsaccade in the time zone determined as “dislike”. In addition, when the attenuation coefficient of the microsaccade in the time zone determined as “like” is compared with the attenuation coefficient of the microsaccade in the time zone determined as “dislike”, it is determined as “like”. The attenuation coefficient of a microsaccade in a certain time zone tends to be smaller than the attenuation coefficient of a microsaccade in a time zone determined to be “dislike”.

  Based on this knowledge, the process of the preference estimation unit 14 described above estimates an impression on music presented in the first time interval and the second time interval.

<Effect>
Microsaccade is an index suitable for evaluating data for each individual as compared with the amount of released dopamine. Moreover, since the microsaccade can be analyzed at high speed from the acquired information on the eyeball position, the measurement result can be obtained in real time. Therefore, with such a configuration, an impression of music can be estimated in real time for each individual. By using the impression estimation apparatus of the present embodiment, it is possible to estimate an impression of music online. Microsaccades are not invasive due to light exposure like the measurement of the amount of released dopamine, can be measured non-invasively, and can be measured easily (without restraining the subject) with an inexpensive device. Therefore, music preference can be estimated at low cost.

  In addition, since the device used to measure the amount of released dopamine (PET) has low time resolution, it can only obtain feature values at coarse time intervals, and can accurately estimate impressions of music that changes from moment to moment. Can not. On the other hand, since microsaccades are feature quantities with high temporal resolution, there is an advantage that impressions of music that changes every moment can be estimated with high accuracy.

<Modification 1>
The impression estimation device 10 may not include the sound presenting unit 11 and the eye movement acquisition unit 12. In other words, at least one of the sound presentation unit 11 and the eye movement acquisition unit 12 may be configured as a separate device, and at least one of music for each time interval and position information of the eyeball may be received from the separate device.

<Second embodiment>
A description will be given centering on differences from the first embodiment.
The impression estimation device 10 according to the first embodiment has a relative comparison format in which which of two different music is preferred is estimated. The impression estimation apparatus 20 according to the second embodiment uses a certain piece of music to estimate an impression of the music. The impression estimation device 20 of the second embodiment includes a feature amount extraction unit 23 and a preference estimation unit 24 instead of the feature amount extraction unit 13 and the preference estimation unit 14 of the first embodiment, and further includes a model storage unit 25. Included (see FIG. 2). In the present embodiment, the impression of music for the target person is estimated by referring to the model storage unit 25. In the second embodiment, since only one piece of music (music for which an impression is to be estimated) needs to be presented, the sound presentation unit 11 and the eye movement acquisition unit 12 do not use the second time interval. Different from the embodiment.

<Feature Extraction Unit 23>
The feature amount extraction unit 23 receives the position information of the eyeball, extracts the feature amount corresponding to the feature of the microsaccade from the time series of the position information of the eyeball (s23), and outputs it to the preference estimation unit 24.

  At this time, as in the first embodiment, feature quantities including at least one of the attenuation coefficient ζ, the attenuation rate λ, and the occurrence frequency P of the microsaccade are extracted. In the present embodiment, a vector (hereinafter also referred to as “feature quantity vector”) including at least one of the above values as an element is extracted as a feature quantity. Alternatively, in addition to at least one of the attenuation coefficient ζ, attenuation rate λ, and occurrence frequency P of the microsaccade, a feature amount vector including the following values as elements may be extracted as the feature amount (see FIG. 4 and FIG. 4). 7).

(1) Reference amplitude A: A movement amount when the movement of the eyeball by the microsaccade converges.
(2) Maximum speed V _max : Maximum speed until the reference amplitude A + overshoot amplitude A _o is reached.
(3) Rise time T _p : time required to reach (rise) the reference amplitude A + the overshoot amplitude A _o . The value of the reference amplitude A + overshoot amplitude A _o is the same value as the time it takes from the maximum velocity V _max reach speeds V _o overshoot.
(4) Overshoot amplitude A _o : The amount of the portion that exceeds (overshoots) the reference amplitude A by the microsaccade. The overshoot is a phenomenon in which the waveform protrudes beyond the reference amplitude A at the rising portion of the waveform, or the protruding waveform. In other words, the overshoot amplitude is the amount of the protruding portion.
(5) Overshoot speed V _o : The maximum speed when attempting to converge to the reference amplitude A from the reference amplitude A + the overshoot amplitude A _o .
(6) Velocity of drift before and after microsaccade V _d : Drift is one of fixed eye movements as described above, and is the small smooth movement of the eyeball when a person is gazing at a certain point. (Refer to FIG. 7).

<Preference estimation unit 24>
The preference estimation unit 24 receives the feature amount, and estimates an impression of music based on the feature amount (s24). In the present embodiment, an impression is estimated and output from the extracted feature amount using a preference estimation model (by reference). In other words, the preference estimation unit 24 applies the feature amount extracted by the feature amount extraction unit 23 to a preference estimation model (a model for estimating an impression from the feature amount) stored in the model storage unit 25, thereby obtaining an impression. Is estimated.

<Model storage unit 25>
In the model storage unit 25, a preference estimation model that outputs an impression with a feature amount as an input is recorded in advance. The preference estimation model is created by learning the relationship between the feature amount and impression acquired for one or more people in advance by a machine learning method. That is, the preference estimation model is a model that describes the correlation between the feature amount and the impression.

  For example, the time series information of the position of the eyeball of the subject person is acquired while the music prepared for learning is presented to the subject person (see FIG. 6A), and the feature amount is obtained from the time series information of the obtained eyeball position. To extract. The feature amount extracted here is the same as the feature amount extracted by the feature amount extraction unit 23. Further, a degree of rating for the music is obtained from the target person, and a data set is prepared in which the extracted feature quantity and the degree of likes and dislikes are paired.

  Similar feature amount extraction is performed for a plurality of different music, and a data set including a combination of the degree of likes and dislikes for each music and the extracted feature amount is acquired as learning data.

  Using this learning data as input data, the machine learning method is used to learn the relationship between impressions and feature quantities.

  For example, there is a support vector machine (hereinafter also referred to as SVM) as a machine learning method. In this case, the degree of likes and dislikes given to music is set as a binary value of likes (1) or dislikes (0), and in the space corresponding to the dimension of the feature vector, when the user likes (good impression) It is possible to obtain a hyperplane that separates the point group corresponding to the feature amount vector and the point group corresponding to the feature amount vector when disliked (bad impression). As a result, when a feature amount whose likes and dislikes are unknown (a feature amount obtained by the feature amount extraction unit 23) is input to the obtained preference estimation model, the feature amount corresponds to “like” or corresponds to “dislike” You can estimate what to do.

  For example, learning is performed by SVM using a feature quantity vector that includes an attenuation coefficient (ζ), an attenuation rate (λ), and an occurrence frequency (P) alone or a combination thereof as elements.

Also, damping coefficient (ζ), damping rate (λ), frequency of occurrence (P), drift speed (V _d ), reference amplitude (A), overshoot amplitude (A _o ), maximum speed (V _max ), Learning is performed by SVM using a feature vector including the overshoot speed (V _o ) as an element. In addition, a rise time (T _p ) may be added as an element to this feature quantity vector.

  By increasing the number of elements, the accuracy of estimation can be increased.

  The SVM can be configured to be classified into a plurality of classes (classes corresponding to degrees of likes and dislikes), not limited to the binary classification of likes / dislikes impressions. Alternatively, as long as it is a machine learning method that identifies and classifies a plurality of classes (classes corresponding to the degree of likes and dislikes), other machine learning methods are not limited to SVM.

  It should be noted that the target person from whom the learning data is acquired need not be the same person as the person who actually estimates the impression. Further, when a preference estimation model is learned based on learning data obtained for a plurality of people, more accurate estimation can be performed. In addition, since the feature quantity that appears in response to the impression may differ for each target person, create a preference estimation model for each target person as the same person as the person who performs the impression estimation for the target person who acquires learning data By doing so, it is possible to perform estimation with higher accuracy in accordance with individual characteristics.

<Effect>
With such a configuration, the same effect as that of the first embodiment can be obtained.

<Other variations>
The present invention is not limited to the above-described embodiments and modifications. For example, the various processes described above are not only executed in time series according to the description, but may also be executed in parallel or individually as required by the processing capability of the apparatus that executes the processes. In addition, it can change suitably in the range which does not deviate from the meaning of this invention.

<Program and recording medium>
In addition, various processing functions in each device described in the above embodiments and modifications may be realized by a computer. In that case, the processing contents of the functions that each device should have are described by a program. Then, by executing this program on a computer, various processing functions in each of the above devices are realized on the computer.

  The program describing the processing contents can be recorded on a computer-readable recording medium. As the computer-readable recording medium, for example, any recording medium such as a magnetic recording device, an optical disk, a magneto-optical recording medium, and a semiconductor memory may be used.

  The program is distributed by selling, transferring, or lending a portable recording medium such as a DVD or CD-ROM in which the program is recorded. Further, the program may be distributed by storing the program in a storage device of the server computer and transferring the program from the server computer to another computer via a network.

  A computer that executes such a program first stores, for example, a program recorded on a portable recording medium or a program transferred from a server computer in its storage unit. When executing the process, this computer reads the program stored in its own storage unit and executes the process according to the read program. As another embodiment of this program, a computer may read a program directly from a portable recording medium and execute processing according to the program. Further, each time a program is transferred from the server computer to the computer, processing according to the received program may be executed sequentially. Also, the program is not transferred from the server computer to the computer, and the above-described processing is executed by a so-called ASP (Application Service Provider) type service that realizes the processing function only by the execution instruction and result acquisition. It is good. Note that the program includes information provided for processing by the electronic computer and equivalent to the program (data that is not a direct command to the computer but has a property that defines the processing of the computer).

  In addition, although each device is configured by executing a predetermined program on a computer, at least a part of these processing contents may be realized by hardware.

Claims (6)

The first time interval is a time interval that is presented to a person so that the first music can be heard, and the second time interval is a time that is presented to the human so that a second music different from the first music can be heard. A first feature amount corresponding to a feature of the microsaccade appearing in the movement of the human eyeball in the first time interval, and a feature of the microsaccade appearing in the motion of the human eyeball in the second time interval A feature quantity extraction unit that extracts a second feature quantity corresponding to
A preference estimation unit that estimates an impression of the first music and the second music based on a degree of difference between the first feature quantity and the second feature quantity;
Impression estimation device. The first time interval is a time interval that is presented to a person so that the first music can be heard, and the second time interval is a time that is presented to the human so that a second music different from the first music can be heard. A first feature amount corresponding to a feature of the microsaccade appearing in the movement of the human eyeball in the first time interval, and a feature of the microsaccade appearing in the motion of the human eyeball in the second time interval A feature amount extracting step for extracting a second feature amount corresponding to
A preference estimation step of estimating an impression of the first music and the second music based on a degree of difference between the first feature value and the second feature value;
Impression estimation method. An impression estimation method according to claim 2,
The feature of the microsaccade includes at least an attenuation rate when the microsaccade is modeled as a step response of a position control system,
The preference estimation step includes:
Estimating that the music presented in the time interval with a large attenuation rate included in the feature amount has a better impression than the music presented in the other time interval, and / or
Estimating that the music presented in the time interval with a small attenuation rate included in the feature amount has a worse impression than the music presented in the other time interval,
Impression estimation method. An impression estimation method according to claim 2,
The feature of the microsaccade includes at least an attenuation coefficient when the microsaccade is modeled as a step response of a position control system,
The preference estimation step includes:
Presuming that the music presented in the time interval with a small attenuation coefficient included in the feature amount has a better impression than the music presented in the other time interval, and / or
Estimating that the music presented in the time interval with a large attenuation coefficient included in the feature amount has a worse impression than the music presented in the other time interval,
Impression estimation method. The model storage unit stores a preference estimation model that is a model describing the correlation between the feature amount corresponding to the feature of the microsaccade appearing in the movement of the human eyeball and the impression of music,
A feature amount extraction step for extracting a feature amount corresponding to a feature of the microsaccade appearing in the movement of the human eyeball in a time interval presented to the human so that the music of the impression estimation target can be heard;
A preference estimation step of estimating an impression of the impression estimation target music by referring to the preference estimation model with the feature amount extracted in the feature amount extraction step as an input,
Impression estimation method.   A program for causing a computer to execute the impression estimation method according to claim 2.

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