Information processing device and information processing method

Abstract

To provide an information processing device that presents information that supports a tuning operation of a stringed instrument to a user.SOLUTION: An information processing device includes: an acceptance unit that accepts playing sound of a stringed instrument having strings and pegs; an estimation unit that estimates position information of the pegs based on the accepted playing sound; and an output unit that outputs guidance information for changing positions of the pegs based on estimation results of the estimation unit.SELECTED DRAWING: Figure 4

Description

Technical Field

[0001] One embodiment of this invention relates to an information processing apparatus and an information processing method for presenting information regarding the tuning of stringed instruments.

Background Art

[0002] The tuning device of Patent Document 1 is a tuning device for strings of a stringed instrument such as a guitar. The tuning device of Patent Document 1 includes tuning pegs or machine heads that provide equal or substantially equal tuning sensitivity to a plurality of strings provided on the same instrument. The tuning device of Patent Document 1 causes equal or substantially equal sound transitions of the strings associated with one rotation unit of the tuning peg or machine head.

[0003] The adjustment device of Patent Document 2 includes a sound source that outputs a sound source signal for adjusting the timbre of an instrument 400 having a wooden soundboard, a first equalizer that changes the frequency characteristics of the sound source signal according to the characteristics of the exciter, a second equalizer that changes the frequency characteristics of the sound source signal from the first equalizer, a spectrum analyzer that analyzes the spectrum of the adjustment sound generated by the instrument due to the excitation from the exciter, and a controller that controls the second equalizer according to the analysis result of the spectrum.

[0004] The virtual tuning method of the stringed instrument of Patent Document 3 is such that, in a state where the pitch is not adjusted, the strings of the stringed instrument are excited, and a standard adjustment coefficient is determined for each string. For example, when a pitch is generated as a result of a string being plucked by a fingernail during normal playing of the instrument, the pitch generated by the string is adjusted by the standard adjustment coefficient and an intonation adjustment coefficient corresponding to the intonation error.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

[0006] None of the prior art described above supports the tuning of stringed instruments.

[0007] One aspect of this disclosure aims to provide an information processing device that presents information to a user to support the tuning of a stringed instrument. [Means for solving the problem]

[0008] An information processing device according to one embodiment of the present invention includes a receiving unit that receives the sound of a stringed instrument having strings and pegs being played, an estimation unit that estimates the position information of the pegs based on the received sound of the instrument being played, and an output unit that outputs guidance information for changing the position of the pegs based on the estimation result of the estimation unit. [Effects of the Invention]

[0009] According to one embodiment of the present invention, information to support the tuning of a stringed instrument can be presented to the user. [Brief explanation of the drawing]

[0010] [Figure 1] This is a diagram showing the configuration of the acoustic system 1. [Figure 2] This is a block diagram showing the configuration of guitar amplifier 11. [Figure 3] This is a block diagram showing the main configuration of user terminal 12. [Figure 4] This is a functional block diagram of the application program read by CPU204. [Figure 5] This is a flowchart showing how the application program works. [Figure 6] This is an external view of user terminal 12, showing an example of a display screen related to an application program. [Figure 7] This is a flowchart showing the operation of the trained model generation method performed by the trained model generation device. [Figure 8] This is an external view showing an example of guidance information displayed on the display unit 201 of the user terminal 12. [Modes for carrying out the invention]

[0011] Figure 1 is an external view showing an example of the sound system 1. The sound system 1 includes an electric guitar 10, a guitar amplifier 11, and a user terminal 12.

[0012] The electric guitar 10 is an example of a stringed instrument having strings and pegs. In this embodiment, the electric guitar 10 is shown as an example of a stringed instrument, but the stringed instruments of the present invention also include other instruments such as electric basses and acoustic instruments such as violins.

[0013] The guitar amplifier 11 is connected to the electric guitar 10 via an audio cable. The guitar amplifier 11 is also connected to the user terminal 12 via wireless communication such as Bluetooth® or Wi-Fi. The electric guitar 10 outputs analog sound signals related to the sound being played to the guitar amplifier 11. If the stringed instrument is an acoustic instrument, the sound signal is input to the guitar amplifier 11 using a microphone or pickup.

[0014] Figure 2 is a block diagram showing the configuration of the guitar amplifier 11. The guitar amplifier 11 includes a display 101, a user interface (I / F) 102, flash memory 103, a CPU 104, RAM 105, a DSP 106, a communication I / F 107, an audio I / F 108, an A / D converter 109, a D / A converter 110, an amplifier 111, and a speaker 112.

[0015] The display 101 is composed of, for example, an LED, an LCD (Liquid Crystal Display), or an OLED (Organic Light-Emitting Diode), etc., and displays the state of the guitar amplifier 11, etc.

[0016] The user I / F 102 is composed of knobs, switches, or buttons, etc., and accepts user operations. Also, the user I / F 102 may be a touch panel laminated on the LCD of the display 101.

[0017] The CPU 104 reads out various programs stored in the flash memory 103, which is a storage medium, to the RAM 105 and controls the guitar amplifier 11. For example, the CPU 104 accepts parameters related to signal processing via the user I / F 102 and controls the DSP 106 and the amplifier 111.

[0018] The communication I / F 107 connects to other devices such as the user terminal 12 via Bluetooth (registered trademark) or wireless LAN, etc.

[0019] The audio I / F 108 has an analog audio terminal. The audio I / F 108 accepts an analog audio signal from the electric guitar 10 via an audio cable.

[0020] The A / D converter 109 converts the analog audio signal received by the audio I / F 108 into a digital audio signal.

[0021] The DSP 106 performs various signal processes such as effects on the digital audio signal. The parameters related to the signal processing are accepted via the user I / F 102. The DSP 106 outputs the digital audio signal after the signal processing to the D / A converter 110.

[0022] The CPU 104 transmits the digital audio signal after the signal processing by the DSP 106 or the digital audio signal before the signal processing via the communication I / F 107 to the user terminal 12.

[0023] The D / A converter 110 converts the digital audio signal received from the DSP 106 into an analog audio signal. The amplifier 111 amplifies the analog audio signal. Amplification parameters are received via the user interface 102.

[0024] The speaker 112 outputs the sound of the electric guitar 10 being played, based on the analog sound signal amplified by the amplifier 111.

[0025] Figure 3 is a block diagram showing the configuration of the user terminal 12. The user terminal 12 is an information processing device such as a personal computer or a smartphone. The user terminal 12 is equipped with a display 201, a user interface 202, flash memory 203, a CPU 204, RAM 205, and a communication interface 206.

[0026] The display unit 201 consists of, for example, an LED, LCD, or OLED, and displays various information. The user interface 202 is a touch panel stacked on the LCD or OLED of the display unit 201. Alternatively, the user interface 202 may be a keyboard or mouse. If the user interface 202 is a touch panel, it, together with the display unit 201, constitutes a GUI (Graphical User Interface).

[0027] The CPU 204 is a control unit that controls the operation of the user terminal 12. The CPU 204 performs various operations by reading predetermined programs, such as application programs, stored in the flash memory 203 (a storage medium) into the RAM 205 and executing them. The programs may also be stored on a server (not shown). The CPU 204 may also download and execute programs from the server via a network.

[0028] Figure 4 is a functional block diagram of the application program read by the CPU 204. The CPU 204 configures a reception unit 51, an estimation unit 52, and an output unit 53 based on the read application program. Figure 5 is a flowchart showing the operation of the information processing method by the application program. Figure 6 is an external view of the user terminal 12 showing an example of a display screen related to the application program.

[0029] The reception unit 51 receives digital sound signals related to the sound played by the electric guitar 10 from the guitar amplifier 11 via the communication interface 206 (S11). In this example, the reception unit 51 also receives information about the target pitch for tuning. For example, the CPU 204 displays a screen for selecting the string to be tuned on the display unit 201.

[0030] As shown in Figure 6, the reception unit 51 displays an image of the guitar's tuning pegs and a selection box for selecting the string to be tuned on the display unit 201. The selection box is a GUI for selecting, for example, the 6th string (pitch: E2), 5th string (pitch: A2), 4th string (pitch: D3), 3rd string (pitch: G3), 2nd string (pitch: B3), or 1st string (E4) of the guitar.

[0031] The user selects the string to be tuned or the target pitch via the touch panel's user interface 202. The receiving unit 51 then receives information about the target pitch. The user then plays the selected string from among the multiple strings of the electric guitar 10 as an open string. The receiving unit 51 then receives a digital sound signal from the guitar amplifier 11 related to the sound produced by the electric guitar 10.

[0032] Next, the estimation unit 52 estimates the position information of the pegs based on the sound of the performance received by the reception unit 51 (S12). The position information of the pegs includes, for example, information on the direction and amount of rotation of the pegs.

[0033] For example, the estimation unit 52 estimates the position information of the tuning pegs based on a trained model that was trained using a Deep Neural Network (DNN) to learn the relationship between the sound played by the electric guitar 10 and the position information of the tuning pegs of the electric guitar 10.

[0034] Figure 7 is a flowchart illustrating the operation of the pre-trained model generation method performed by the pre-trained model generation device. The pre-trained model generation device is implemented, for example, by a program running on a computer (server) used by a musical instrument manufacturer. In the training phase, the pre-trained model generation device acquires a large number of datasets (training data) that include the positions of the pegs of the electric guitar 10 and the sound produced by the electric guitar 10 at each peg position (S21). The pre-trained model generation device trains a predetermined training model to understand the relationship between the peg positions and the sound produced using a predetermined algorithm (S22). The peg positions may be estimated, for example, from images captured by a camera, or detected by attaching sensors such as rotary encoders to the pegs.

[0035] The algorithm used to train the learning model is not limited, and any machine learning algorithm such as CNN (Convolutional Neural Network) or RNN (Recurrent Neural Network) can be used. The machine learning algorithm may be supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, inverse reinforcement learning, active learning, or transfer learning. Furthermore, the estimation unit 52 may train the learning model using machine learning models such as HMM (Hidden Markov Model) or SVM (Support Vector Machine).

[0036] The pitch of the open strings of the electric guitar 10 is determined by the position of the tuning pegs. In other words, there is a correlation between the position of the tuning pegs and the sound produced. Therefore, the pre-trained model generation device trains a predetermined pre-trained model to learn the relationship between the position of the tuning pegs and the sound produced, and generates a pre-trained model (S23).

[0037] The estimation unit 52 obtains a trained model, which is the result of learning the relationship between the position of the pegs and the sound played, from a trained model generation device (for example, a server of a musical instrument manufacturer) via the network. In the execution stage, the estimation unit 52 uses the trained model to determine the rotation direction and amount of the pegs of the electric guitar 10 based on the pitch difference between the pitch of the currently played sound received and the target pitch. More specifically, the estimation unit 52 uses the trained model to determine the rotation direction and amount of the pegs of the electric guitar 10 in order to make the pitch difference between the pitch of the received sound received and the target pitch zero.

[0038] The output unit 53 outputs guidance information for changing the position of the peg based on the estimation result of the estimation unit 52 (S13). For example, the output unit 53 outputs an image to the display unit 201 showing the direction and amount of rotation of the peg.

[0039] Figure 8 is an external view showing an example of guidance information displayed on the display unit 201 of the user terminal 12. As shown in Figure 8, the output unit 53 displays on the display unit 201 an image of the guitar's tuning peg, text indicating the string to be tuned (the 6th string: E2 in the example of Figure 8), and text indicating the direction and amount of rotation of the peg. The user refers to this guidance information and rotates the tuning peg of the electric guitar 10 to tune it. In the example of Figure 8, the display unit 201 displays the text "Rotate 90 degrees to the right" and an image prompting rotation on the peg corresponding to the 6th string, so the user rotates the peg corresponding to the 6th string 90 degrees to the right to tune it. The guidance information may also be text information such as "loosen," "tighten," or "rotate half a turn to the right," or it may be voice guidance. Alternatively, the guidance information may be indicated by turning multiple LEDs on or off. For example, the user terminal 12 may indicate that the peg should be rotated to the right by turning on the LED labeled "right." Furthermore, the user terminal 12 may indicate the amount of rotation by the number of LEDs that are lit. For example, if a large amount of rotation is required, a large number of LEDs may be lit.

[0040] Furthermore, the user terminal 12 may detect changes in the sound played when the user rotates the peg. If the pitch difference between the pitch of the sound played and the target pitch has widened, the user terminal 12 may output warning information such as "The rotation direction is reversed" as guidance information.

[0041] In this way, the user terminal 12 of this embodiment can present the user with information to support the tuning of a stringed instrument. As a result, the user can easily determine in which direction and by how much to rotate the tuning peg of the string to be tuned on the electric guitar 10 simply by plucking the string to be tuned.

[0042] (Variation 1) In the above embodiment, the receiving unit 51 received a target note (for example, the string to be tuned). However, the receiving unit 51 may automatically determine the target note. In this case, the configuration for receiving the target note is not essential. For example, the receiving unit 51 automatically determines the target note to be the pitch closest to the pitch of the received playing note. In this case, the user relies to some extent on their own ears to tune. After performing some tuning, the user can easily determine in which direction and by how much to rotate the peg of the target string to complete the tuning.

[0043] Alternatively, even if the user completes tuning using a tuner before playing, the tuning may go out of whack during the performance. In the modified example 1, the user terminal 12 receives the sound produced by the out-of-tuned instrument during the performance, automatically determines the target pitch to be the pitch closest to the received sound, and presents information on the rotation direction and amount of the electric guitar's tuning pegs 10. Therefore, the user can perceive that the tuning has gone out of whack during the performance without using a tuner, and can also correct the out-of-tuned instrument.

[0044] (Modification 2) In the above embodiment, the estimation unit 52 estimated the position information of the pegs based on a trained model that learned the relationship between the sound of a stringed instrument being played and the position information of the stringed instrument's pegs. However, the estimation unit 52 may also estimate the position information of the pegs by referring to a table that defines the relationship between the sound of a stringed instrument being played and the position information of the stringed instrument's pegs. This table is pre-registered in the flash memory 203 of the user terminal 12 or in a database on a server (not shown).

[0045] This allows the user terminal 12 to present the user with information to support the tuning of stringed instruments without using artificial intelligence algorithms.

[0046] (Variation 3) In the above embodiment, tuning of the 6th string was shown as an example. However, stringed instruments such as the electric guitar 10 include multiple strings and multiple pegs corresponding to each of the multiple strings. Therefore, it is preferable to train the model to learn the relationship between the sound played and the position information of the pegs for each of the multiple strings. The estimation unit 52 acquires the sound played for each string and estimates the position information of the corresponding peg.

[0047] This allows the user terminal 12 to provide the user with information that supports tuning for each string with high precision.

[0048] (Modification 4) The reception unit 51 in Modification 4 further receives environmental information. Environmental information includes, for example, information about the performance venue, humidity, temperature, or atmospheric pressure. Environmental information may be received from the user via the user I / F 202, or it may be received by a sensor (not shown).

[0049] Environmental information is one of the factors that affect tuning. Tuning often shifts due to changes in these environmental factors. The estimation unit 52 in Modification 4 estimates the position information of the pegs using a trained model that has learned the relationship between the performance sound, environmental information, and the position information of the pegs.

[0050] This allows users to be aware of tuning deviations caused by changes in environmental factors such as humidity.

[0051] (Variation 5) The reception unit 51 in Modification 5 further receives information on the number of people. This information may be received from the user via the user interface 202, or it may be received by, for example, taking a picture of the area around the user terminal 12 with a camera (not shown) and recognizing people from the captured image.

[0052] The number of people is also one of the factors that affect tuning. Tuning often shifts due to changes in the number of people. The estimation unit 52 in Modification 5 estimates the position information of the pegs using a trained model that has learned the relationship between the performance sound, the number of people information, and the position information of the pegs.

[0053] Furthermore, the estimation unit 52 in the modified example 5 may estimate the position information of the pegs using a trained model that has learned the relationship between the performance sound, environmental information, and the number of people and the position information of the pegs.

[0054] This allows users to be aware of tuning discrepancies caused by changes in the number of users.

[0055] (Experimental variation 6) The receiving unit 51 in the modified example 6 further receives string information. String information includes, for example, the material of the string, the thickness of the string, whether or not it is coated, the type of coating material, or the usage time of the string. Alternatively, the string information may include information about the string manufacturer or the string product name.

[0056] String information may be received from the user via the user interface 202, or it may be received by, for example, taking a picture of the string packaging with a camera (not shown) and recognizing the string manufacturer, product name, manufacturing date, etc. from the captured image.

[0057] String characteristics are also a factor that affects tuning. For example, even when the tuning peg is turned in the same direction by the same amount, the amount of pitch change will differ between strings that have been used for a long time and those that have been used for a short time. Alternatively, even when the tuning peg is turned in the same direction by the same amount, the amount of pitch change may differ if the material or thickness of the strings is different.

[0058] The estimation unit 52 in the modified version 6 estimates the position information of the pegs using a trained model that has learned the relationship between the sound of the performance, the string information, and the position information of the pegs.

[0059] Furthermore, even if the same temperature and humidity changes occur, the amount of tuning deviation will differ if the string material and thickness are different. Therefore, the estimation unit 52 in Modification 6 may estimate the position information of the pegs using a trained model that has learned the relationship between the performance sound, environmental information, string information, and peg position information.

[0060] Furthermore, the estimation unit 52 in the modified example 6 may estimate the position information of the pegs using a trained model that has learned the relationship between the performance sound, string information, and the number of people and the position information of the pegs.

[0061] This allows the user terminal 12 to present the user with information that supports differences in tuning due to differences in string material, string thickness, etc.

[0062] (Example 7) The receiving unit 51 in the modified example 7 receives frequency information regarding the target tuning frequency. The user sets one of the following as the target tuning frequency: for example, 440Hz, 441Hz, or 442Hz. The frequency information is received from the user via the user I / F 202.

[0063] The estimation unit 52 estimates the position information of the pegs based on the received performance sound and frequency information.

[0064] This allows the user terminal 12 to present the user with information that supports tuning their instrument to match the tuning frequencies of other musicians playing with them.

[0065] (Variation 8) In the above embodiment, an example was shown in which the user terminal 12 receives the sound of an open string being played. However, the receiving unit 51 may also receive sounds of the same string but at different pitches, such as the sound of a fret being pressed down, rather than an open string. In this case, the estimation unit 52 estimates the position information of the peg based on multiple sounds of different pitches. In this case, the receiving unit 51 automatically determines the target pitch to be the pitch closest to the pitch of the received sound.

[0066] The estimation unit 52 pre-learns the rotation direction and amount of the tuning pegs of the electric guitar 10 to make the pitch difference between the pitch of the sound produced when each fret is pressed and the target pitch zero for each string. The estimation unit 52 then determines the rotation direction and amount of the tuning pegs of the electric guitar 10 using the learned model.

[0067] The reception unit 51 may also receive song data for the song to be played. The reception unit 51 receives song data for the song to be played via the user interface 202. In this case, the reception unit 51 receives the song data as a target pitch. The estimation unit 52 pre-learns the rotation direction and amount of the tuning pegs of the electric guitar 10 in order to make the pitch difference between the received performance sound and the target pitch in the song data zero. The estimation unit 52 uses this learned model to determine the rotation direction and amount of the tuning pegs of the electric guitar 10.

[0068] As a result, the user terminal 12 receives the sound produced by the out-of-tune playing during performance and presents information on the rotation direction and amount of the electric guitar 10's tuning pegs based on the pitch difference from the target pitch determined from the song data. Therefore, the user can perceive when the tuning has gone out of whack during performance without using a tuner, and can also correct the out-of-tune tuning.

[0069] This allows the user terminal 12 to provide the user with information to support the tuning process, even while the user is playing.

[0070] (Extreme variation 9) The reception unit 51 in the modified example 9 further receives information about the instrument. This information includes, for example, the manufacturer's name, product name, usage time, or the names of the parts used in the instrument.

[0071] Information about the instrument may be received from the user via the user interface 202, or it may be received by, for example, taking a picture of the instrument with a camera (not shown) and recognizing the instrument's manufacturer, product name, the names of the parts used in the instrument, the manufacturing date, etc., from the captured image.

[0072] Information about the instrument is also one of the factors that affects tuning. For example, even if the tuning pegs are turned in the same direction by the same amount, the amount of pitch change will differ between instruments that have been used for a long time and those that have been used for a short time. Alternatively, if parts such as the bridge are different, the amount of pitch change may also differ even if the tuning pegs are turned in the same direction by the same amount.

[0073] The estimation unit 52 in Modification 9 estimates the position information of the pegs using a trained model that has learned the relationship between the sound of the performance, the information of the instrument, and the position information of the pegs. The configuration of Modification 9 can also be combined with any of the above-described Modifications 1 to 8.

[0074] (Variation 10) In the modified example 10, the user terminal 12 performs the generation of a trained model, which is the learning stage shown in Figure 7, and the output of peg guidance information, which is the execution stage shown in Figure 5. In other words, the operation of the learning stage of the learning model and the operation of the execution stage of the trained model may be performed by a single device. Alternatively, the server may be the information processing device of the present invention. In other words, the server may perform the operation of the learning stage of the learning model and the operation of the execution stage of the trained model. In this case, the user terminal 12 transmits information indicating the sound of the electric guitar 10 being played and the target pitch to the server via the network, and receives information on the rotation direction and amount of rotation of the peg from the server.

[0075] The description of this embodiment should be considered in all respects to be illustrative and not restrictive. The scope of the invention is indicated by the claims, rather than by the embodiments described above. Furthermore, the scope of the invention includes the scope equivalent to the claims.

[0076] For example, in the above-described embodiment, a user terminal 12 was shown as an example of the information processing device of the present invention. However, a guitar amplifier 11 may correspond to the information processing device of the present invention. In this case, the guitar amplifier 11 receives the sound of a stringed instrument being played, estimates the position information of the tuning pegs based on the received sound, and outputs guidance information for changing the position of the tuning pegs. Alternatively, an electric guitar 10 may correspond to the information processing device of the present invention. In this case, the electric guitar 10 may be equipped with a display and display guidance information for changing the position of the tuning pegs on the display. Alternatively, the electric guitar 10 may be equipped with a speaker and output guidance information by voice. Furthermore, in the above-described embodiment, an electric guitar 10 was shown as an example of a stringed instrument of the present invention, but acoustic stringed instruments are also included in the stringed instruments of the present invention.

[0077] Furthermore, in the above-described embodiment, the user terminal 12 received the sound of the electric guitar 10 via the guitar amplifier 11, but the user terminal 12 may also receive the sound of the instrument through its microphone (not shown). [Explanation of Symbols]

[0078] 1: Sound System 10: Electric guitar 11: Guitar Amplifier 12: User terminal 51: Reception Department 52: Estimation part 53: Output section 101:Display unit 102: User Interface 103: Flash memory 104:CPU 105: RAM 106: DSP 107: Communication I / F 108: Audio Interface 109: A / D converter 110: D / A converter 111: Amplifier 112: Speaker 201:Display unit 202: User Interface 203: Flash memory 204:CPU 205: RAM 206: Communication I / F

Claims

1. A reception unit that receives the sound of a stringed instrument having strings and pegs, An estimation unit that estimates the rotation direction and amount of rotation of the peg based on the received performance sound, An output unit outputs guidance information for changing the position of the peg based on the estimation result of the estimation unit, Equipped with an information processing device.

2. The reception unit receives the target sound, The estimation unit estimates information regarding the rotation direction and amount of the peg in order to reduce the pitch difference between the pitch of the received target sound and the received performance sound. The information processing apparatus according to claim 1.

3. The estimation unit estimates the rotation direction and amount of the pegs based on a trained model that has learned the relationship between the sound produced by the stringed instrument and the rotation direction and amount of the pegs of the stringed instrument. The information processing apparatus according to claim 1 or claim 2.

4. The string includes multiple strings, The peg includes a plurality of pegs corresponding to each of the plurality of strings, The trained model has learned the relationship between the sound played and the information on the direction and amount of rotation for each of the multiple strings. The information processing apparatus according to claim 3.

5. The aforementioned trained model has further learned the relationship between environmental information and the information on the direction and amount of rotation. The aforementioned reception unit further receives environmental information, The estimation unit further estimates the rotation direction and rotation amount information based on the environmental information. The information processing apparatus according to claim 3 or claim 4.

6. The aforementioned trained model has further learned the relationship between the number of people and the information on the direction and amount of rotation. The aforementioned reception desk further receives information on the number of people, The estimation unit further estimates the rotation direction and amount information based on the number information of the person. The information processing apparatus according to any one of claims 3 to 5.

7. The aforementioned trained model has further learned the relationship between the string information and the rotation direction and rotation amount information. The aforementioned reception unit further receives string information, The estimation unit further estimates the rotation direction and rotation amount based on the information of the string. The information processing apparatus according to any one of claims 3 to 6.

8. The reception unit receives frequency information regarding the target tuning frequency, The estimation unit estimates the rotation direction and rotation amount information based on the received performance sound and frequency information. The information processing apparatus according to any one of claims 1 to 7.

9. The aforementioned performance sound includes multiple performance sounds of different pitches. The estimation unit estimates information about the rotation direction and amount of rotation of the peg based on the multiple different pitches of the played sounds. The information processing apparatus according to any one of claims 1 to 8.

10. An information processing method performed by an information processing device, The information processing device receives the sound of a stringed instrument having strings and pegs, The information processing device estimates the rotation direction and amount of rotation of the peg based on the received sound of the performance, The information processing device outputs guidance information for changing the position of the peg based on the estimation result. Information processing methods.

11. The information processing device receives a target sound, The information processing device estimates information regarding the rotation direction and amount of the peg in order to reduce the pitch difference between the pitch of the received target sound and the received performance sound. The information processing method according to claim 10.

12. The information processing device estimates the rotation direction and amount of rotation of the pegs based on a trained model that has learned the relationship between the sound of the stringed instrument being played and the rotation direction and amount of rotation of the pegs of the stringed instrument. The information processing method according to claim 10 or claim 11.

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