Noise canceling method, information processing device, and program

Abstract

To provide improved noise-canceling technique.SOLUTION: A noise-canceling method to be implemented by an information processing device 10 is provided, the method comprising estimating pattern noise on the basis of tire characteristics information and vehicle speed information, generating a canceling sound of the estimated pattern noise, and outputting the canceling sound from a speaker.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present disclosure relates to a noise canceling method, an information processing apparatus, and a program.

Background Art

[0002] Conventionally, noise canceling technology has been known to reduce noise generated as a vehicle travels. For example, Patent Document 1 discloses a vehicle soundproofing device that adjusts a canceling sound for canceling road noise that enters the vehicle cabin according to the road surface conditions in front of the vehicle.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The conventional technology selects an appropriate canceling sound based on image data obtained by photographing road surface irregularities, but has not considered canceling pattern noise, which is a particular type of noise that enters the vehicle cabin. Thus, there has been room for improvement in noise canceling technology.

[0005] In view of such circumstances, an object of the present disclosure is to improve noise canceling technology that cancels pattern noise in particular.

Means for Solving the Problems

[0006] (1) A noise canceling method according to an embodiment of the present disclosure is a noise canceling method executed by an information processing apparatus, estimating pattern noise based on tire characteristic information and vehicle speed information; The step of generating a cancellation sound for the pattern noise The step of emitting the cancellation sound from a speaker, and including. According to such a configuration, since the information processing apparatus emits a cancellation sound that cancels pattern noise based on the tire characteristic information and the vehicle speed information, noise can be efficiently reduced, and the noise cancellation technology is improved.

[0007] (2) The noise cancellation method according to an embodiment of the present disclosure is the noise cancellation method described in (1), wherein the tire characteristic information includes the tire circumference and the number of pitches per revolution. According to such a configuration, since the tire characteristic information includes the tire circumference and the number of pitches per revolution, pattern noise can be efficiently estimated, and the effect of noise cancellation can be enhanced.

[0008] (3) The noise cancellation method according to an embodiment of the present disclosure is the noise cancellation method described in (1) or (2), wherein it includes the step of correcting the cancellation sound based on the actually measured sound. According to such a configuration, the information processing apparatus can efficiently cancel pattern noise by extracting a portion corresponding to the frequency band of the generated cancellation sound from the actually measured sound and generating a cancellation sound that cancels this.

[0009] (4) The noise cancellation method according to an embodiment of the present disclosure is the noise cancellation method described in any one of (1) to (3), and further the actually measured sound is measured by a microphone or an acceleration sensor. According to such a configuration, the information processing apparatus can observe the actually measured sound and utilize it for the generation or correction of the cancellation sound, thereby enhancing the effect of noise cancellation.

[0010] (5) The noise canceling method according to an embodiment of the present disclosure is the noise canceling method according to any one of (1) to (4), and further, The pattern noise is a pattern excitation sound. According to such a configuration, since the pattern noise is a pattern excitation sound, the pattern noise can be effectively estimated using the vehicle speed information, and the effect of noise canceling can be enhanced.

[0011] (6) The noise canceling method according to an embodiment of the present disclosure is the noise canceling method according to any one of (1) to (5), and includes a step of correcting the canceling sound based on the observed sound of the evaluation microphone. According to such a configuration, the information processing device can verify whether the noise is effectively canceled by the canceling sound, correct the canceling sound, and further enhance the effect of noise canceling.

[0012] (7) The noise canceling method according to an embodiment of the present disclosure is a noise canceling method executed by an information processing device, and includes a step of training a learning model based on vehicle speed information and measured sound, a step of estimating pattern noise based on vehicle speed information, a step of generating a canceling sound for the pattern noise, a step of emitting the canceling sound from a speaker, and in the estimating step, includes inputting the vehicle speed information into the learning model, and estimating pattern noise based on the learning model. According to such a configuration, by training a learning model using vehicle speed information and measured sound as training data, the more the present technology is implemented, the more the accuracy of the cancellation sound emitted by the information processing device can be improved, and the effect of noise cancellation can be enhanced. Further, according to the present technology, even when the characteristic information of the tire cannot be used, pattern noise can be estimated by the learning model without using the characteristic information of the tire, and an appropriate cancellation sound can be emitted.

[0013] (8) An information processing device according to an embodiment of the present disclosure is an information processing device including a processor, The processor executes the noise canceling method according to any one of (1) to (7). According to such a configuration, the noise canceling method according to an embodiment of the present disclosure can be generally obtained in the form of an information processing device that executes these, and the technology for canceling noise is improved.

[0014] (9) A program according to an embodiment of the present disclosure is A program executed by an information processing device, which causes a computer to execute the noise canceling method according to any one of (1) to (7). According to such a configuration, the noise canceling method according to an embodiment of the present disclosure can be generally obtained in the form of a program that causes an information processing device to execute these, and the technology for canceling noise is improved.

Advantages of the Invention

[0015] According to the noise canceling method, information processing device, and program according to an embodiment of the present disclosure, in particular, the technology for canceling pattern noise can be improved.

Brief Description of the Drawings

[0016]

Figure 1

Figure 2

Figure 3

Figure 4

Mode for Carrying Out the Invention

[0017] Hereinafter, the noise canceling technology according to the embodiment of the present disclosure will be described with reference to the drawings.

[0018] In each figure, the same or corresponding parts are denoted by the same reference numerals. In the description of the present embodiment, the description of the same or corresponding parts will be omitted or simplified as appropriate.

[0019] FIG. 1 is a diagram showing an example of a vehicle 50 equipped with an information processing apparatus 10 according to an embodiment. The information processing apparatus 10 outputs a canceling sound for canceling noise (noise) generated as the vehicle 50 travels, thereby reducing the noise in the vehicle interior.

[0020] First, the outline of the present embodiment will be described. The noise canceling technology according to the embodiment of the present disclosure is executed by the information processing apparatus 10. The noise canceling technology according to the embodiment of the present disclosure can be used, for example, when canceling pattern noise. Specifically, in the noise canceling technology according to the embodiment of the present disclosure, the information processing apparatus 10 estimates pattern noise based on tire characteristic information and vehicle speed information. The information processing apparatus 10 generates a canceling sound for the pattern noise and emits the canceling sound from a speaker. Thereby, pattern noise can be suppressed efficiently, and the noise canceling technology is improved.

[0021] Here, pattern noise is noise that is radiated when the discontinuous portions of the tread patterns of the tires 51 and 52 collide with the road surface G when they come into contact with the road surface, causing the tires 51 and 52 to vibrate due to the impact force. Also, tire characteristic information refers to any information regarding the characteristics of the tire. It is preferable that the tire characteristic information used by the information processing device 10 can affect the spectrum of the pattern noise observed inside the vehicle cabin.

[0022] The tire characteristic information may be, for example, the tire circumference, the number of pitches per revolution, etc. Also, the tire characteristic information may be the tire size, the type of tire, the presence or absence or type of inclusions, etc. The tire size may include the outer diameter of the tire, the inner diameter of the tire (rim diameter), etc. The outer diameter of the tire may be the diameter of the tire (the diameter in a donut-shaped cross-section) in a state where the tire is mounted on a rim, filled with air to an appropriate air pressure, and no load is applied. The outer diameter of the tire may also be calculated from the rim diameter, the tire width, the aspect ratio, etc. The types of tires may include passenger car tires, light truck tires, small truck tires, truck tires, bus tires, motorcycle tires, etc. The inclusions in the tire may include sound-absorbing sponges, films, etc.

[0023] In addition to this, the tire characteristic information may include various information regarding the characteristics of the tire. For example, the tire characteristic information may include information such as changes over time, deterioration, changes due to use, or wear of the tire.

[0024] According to the noise canceling technology according to the embodiment of the present disclosure, among the noises that enter the vehicle cabin, particularly pattern noise can be canceled. In other words, the information processing device 10 estimates the pattern noise based on the tire characteristic information and the vehicle speed information. The information processing device 10 generates a canceling sound for the estimated pattern noise and emits it from the speaker. Thereby, noise can be efficiently reduced based on known information such as tire characteristic information and easily obtainable vehicle speed information.

[0025] Thus, according to the noise cancellation technology according to this embodiment, based on the known information of the tire characteristics information and the vehicle speed information that can be easily obtained, by canceling the pattern noise, the noise can be efficiently reduced, and the noise cancellation technology is improved.

[0026] Hereinafter, the configuration according to an embodiment of the present disclosure will be described.

[0027] As shown in FIG. 1, the information processing device 10 includes a microphone 14, a speaker 15, an acceleration sensor 16, and a calculation unit 17.

[0028] The microphone 14 detects the voice information of the noise in the passenger compartment of the vehicle 50. The microphone 14 may be provided, for example, near the seat of the vehicle 50. Alternatively, for example, the microphone 14 may be provided on the floor surface inside the vehicle or near the floor surface. The information processing device 10 may invert the phase of the voice information of the noise detected by the microphone 14 to generate a cancellation sound for canceling the noise. In a state where the cancellation sound is output, the information processing device 10 may use the voice information in the passenger compartment acquired by the microphone 14 to evaluate the effect of noise cancellation.

[0029] The speaker 15 outputs the cancellation sound as a sound wave. The speaker 15 may be realized by the voice output device of the audio system originally provided in the vehicle 50. The speaker 15 can be installed at any location inside the vehicle. For example, the speaker 15 may be provided on the floor surface inside the vehicle or near the floor surface.

[0030] The acceleration sensor 16 measures the acceleration of the vehicle 50. The measured value of the acceleration sensor 16 is used for the evaluation of NVH (Noise, Vibration and Harshness). The acceleration sensor 16 may be realized by a sensor of any type such as a semiconductor gauge, a capacitance, or a differential transformer type. The acceleration sensor 16 may be attached, for example, to an axle that transmits the power generated by a driving unit such as an engine, a motor, or a combination thereof to the tires 51, 52.

[0031] The arithmetic unit 17 is connected to the microphone 14, the speaker 15, and the acceleration sensor 16, and controls the operations of these devices. As will be described later with reference to FIG. 2, the arithmetic unit 17 includes a control unit 11, a storage unit 12, and a communication unit 13.

[0032] The vehicle 50 is, for example, an automobile such as a passenger car, but is not limited thereto and may be any vehicle. The automobile is, for example, a gasoline vehicle, a hybrid vehicle, a plug-in hybrid vehicle, a fuel cell vehicle, or a battery electric vehicle, but is not limited thereto. The vehicle 50 includes tires 51, 52.

[0033] A communication device 53 is provided on the tire 51. A communication device 54 is provided on the tire 52.

[0034] Communication devices 53 and 54 perform wireless communication. The communication devices 53 and 54 are, for example, RF (Radio Frequency) tags. RF tags are also referred to as RFID (Radio Frequency Identification) tags. The communication devices 53 and 54 include an IC (Integrated Circuit) chip that constitutes a control unit and a storage unit, and one or more antennas connected to the IC chip. The IC chip may store any information related to the tires 51 and 52, such as identification information of the tires 51 and 52, the manufacturing date, the outer diameter of the tire, the rim diameter, the circumference, and the number of pitches per revolution. For example, the communication devices 53 and 54 may be provided such that two antennas extending linearly, wavily, or spirally extend from the IC chip in opposite directions to each other, and the device as a whole may have an elongated shape.

[0035] The IC chip may operate by dielectric electromotive force generated by electromagnetic waves received by one or more antennas. That is, the communication devices 53 and 54 may be passive communication devices. Alternatively, the communication devices 53 and 54 may further include a battery and be capable of communicating by generating electromagnetic waves with their own power. That is, the communication devices 53 and 54 may be active communication devices.

[0036] (Configuration of the information processing device) Next, each configuration of the information processing device 10 will be described in detail.

[0037] As shown in FIG. 2, the information processing device 10 includes a microphone 14, a speaker 15, an acceleration sensor 16, and an arithmetic unit 17. The arithmetic unit 17 is one or a plurality of computer devices capable of communicating with each other. The arithmetic unit 17 is not limited to these, and may be any general-purpose electronic device such as an FPGA (Field Programmable Gate Array), a single board computer (SBC), or a PC (Personal Computer), or other dedicated electronic devices.

[0038] The control unit 11 includes one or more processors. In one embodiment, the "processor" is a general-purpose processor or a dedicated processor specialized for specific processing, but is not limited thereto. The control unit 11 is communicably connected to each component constituting the information processing apparatus 10 and controls the operation of the entire information processing apparatus 10.

[0039] The storage unit 12 includes any storage module such as, for example, an HDD (Hard Disk Drive), an SSD (Solid State Drive), a ROM (Read-Only Memory), and a RAM (Random Access Memory). The storage unit 12 may function as, for example, a main storage device, an auxiliary storage device, or a cache memory. The storage unit 12 stores any information used for the operation of the information processing apparatus 10. For example, the storage unit 12 may store a system program, an application program, and various information received by the communication unit 13. The storage unit 12 is not limited to being built into the information processing apparatus 10 and may be an external database or an external storage module.

[0040] The communication unit 13 includes any communication module that can be communicably connected to other devices such as a scanner by any communication technology. The communication unit 13 may further include a communication control module for controlling communication with other devices and a storage module for storing communication data such as identification information necessary for communication with other devices.

[0041] The functions of the information processing apparatus 10 are realized by executing the program according to this embodiment on a processor corresponding to the information processing apparatus 10. That is, the functions of the information processing apparatus 10 are realized by software. The program causes a computer to execute the operation of the information processing apparatus 10, causing the computer to function as the information processing apparatus 10. That is, the computer functions as the information processing apparatus 10 by executing the operation of the information processing apparatus 10 according to the program.

[0042] In this embodiment, the program can be recorded on a computer-readable recording medium. The computer-readable recording medium includes non-transitory computer-readable media, such as a magnetic recording device, an optical disk, a magneto-optical recording medium, or a semiconductor memory. The distribution of the program can be performed, for example, by selling, transferring, or lending a portable recording medium such as a DVD (Digital Versatile Disc) or a CD-ROM (Compact Disc Read Only Memory) on which the program is recorded. Also, the distribution of the program may be performed by storing the program in the storage of an external server and transmitting the program from the external server to another computer. Further, the program may be provided as a program product.

[0043] Some or all of the functions of the information processing apparatus 10 may be realized by a dedicated circuit corresponding to the control unit 11. That is, some or all of the functions of the information processing apparatus 10 may be realized by hardware.

[0044] Referring to the flowchart of FIG. 3, a noise canceling method according to an embodiment of the present disclosure is shown.

[0045] Step S101: The control unit 11 of the information processing apparatus 10 estimates pattern noise based on the tire characteristic information and the vehicle speed information.

[0046] For example, the control unit 11 acquires the tire characteristic information by communicating with the communication devices 53, 54. The control unit 11 may store the tire characteristic information in the storage unit 12. In this case, the control unit 11 may estimate the pattern noise based on the tire characteristic information stored in the storage unit 12.

[0047] The speed information of the vehicle can be acquired by any method. For example, the control unit 11 may calculate the speed information of the vehicle 50 based on the acceleration of the vehicle 50 detected by the acceleration sensor 16.

[0048] Step S102: The control unit 11 generates a cancellation sound for pattern noise. For example, the control unit 11 generates a cancellation sound corresponding to the spectrum of the estimated pattern noise in order to cancel the spectrum of the pattern noise estimated in step S101. By making the cancellation sound correspond to the spectrum of the estimated pattern noise, the pattern noise can be appropriately canceled out.

[0049] Step S103: The control unit 11 emits the cancellation sound from the speaker 15. For example, the control unit 11 emits the cancellation sound generated in step S102 from the speaker 15.

[0050] Thus, according to the noise cancellation technology according to this embodiment, the information processing apparatus 10 estimates pattern noise based on the tire characteristic information and the vehicle speed information, and emits a cancellation sound corresponding thereto. Thereby, noise can be efficiently reduced, and the noise cancellation technology is improved.

[0051] Furthermore, the tire characteristic information may include the tire circumference and the number of pitches per revolution. That is, in step S101, the control unit 11 may estimate pattern noise based on the tire characteristic information including the tire circumference and the number of pitches per revolution and the vehicle speed information.

[0052] Pattern noise can be affected by the traveling speed, the tire circumference, and the number of pitches per revolution. Therefore, by including the tire circumference and the number of pitches per revolution in the tire characteristic information, pattern noise can be efficiently estimated, and the effect of noise cancellation can be enhanced.

[0053] The control unit 11 may correct the generated cancellation sound based on the actually measured sound. Here, the actually measured sound refers to the noise inside the vehicle actually observed. The control unit 11 may collect the actually measured sound by the microphone 14.

[0054] Further, the control unit 11 may observe the measured sound by, for example, the acceleration sensor 16 instead of the microphone 14. In this case, the control unit 11 may convert the data observed by the acceleration sensor 16 into a sound spectrum and use it as the measured sound. The acceleration sensor 16 can be installed at any location on the path where the pattern noise propagates into the passenger compartment. For example, the acceleration sensor 16 may be installed on the axle.

[0055] The measured sound may include various noises other than the pattern noise. Among the noises, the pattern noise can be predicted to some extent by using the circumference of the tire and the number of pitches per revolution. Therefore, in order to efficiently cancel the noise by focusing on the pattern noise, the control unit 11 may extract and narrow down the portion of the measured sound corresponding to the spectral bandwidth of the cancellation sound generated by the control unit 11 from the measured sound. The control unit 11 may generate a cancellation sound that cancels the measured sound in the narrowed spectral bandwidth. In other words, the control unit 11 may correct the generated cancellation sound based on the measured sound in the frequency band corresponding to the frequency band of the cancellation sound generated by the control unit 11.

[0056] Here, correcting the generated cancellation sound based on the measured sound includes the control unit 11 directly correcting the cancellation sound generated in step S102 based on the measured sound. Also, correcting the generated cancellation sound based on the measured sound includes the control unit 11 correcting the pattern noise estimated in step S101. In the latter case, when the control unit 11 corrects the pattern noise estimated in step S101, the cancellation sound generated correspondingly in step S102 is corrected. Thus, in either case, as a result, the control unit 11 corrects the generated cancellation sound based on the measured sound.

[0057] In this way, the control unit 11 can efficiently cancel pattern noise by correcting the cancellation sound based on the actually measured sound. In other words, the processing efficiency when generating the cancellation sound can be increased compared to the case of generating a cancellation sound that cancels the entire frequency band of the actually measured sound. Further, the information processing device 10 can observe the actually measured sound by using the microphone 14 or the acceleration sensor 16 and utilize it for the generation or correction of the cancellation sound, thereby enhancing the effect of noise cancellation.

[0058] Furthermore, the pattern noise may be pattern excitation sound. The pattern excitation sound refers to the sound generated by the vibration of the tire when the tire rotates and contacts the road surface G due to the impact. It is considered that the frequency of the pattern excitation sound increases in proportion to the traveling speed. Therefore, since the pattern noise is pattern excitation sound, the pattern noise can be effectively estimated using the vehicle speed information, and the effect of noise cancellation can be enhanced.

[0059] Also, the control unit 11 may correct the cancellation sound based on the observed sound of the evaluation microphone. The evaluation microphone may be configured to observe the in-vehicle noise in a state where it is canceled by the cancellation sound, at least when the speaker 15 emits the cancellation sound. The control unit 11 may evaluate whether the pattern noise is effectively canceled by the cancellation sound emitted by the speaker 15 based on the observed sound of the evaluation microphone. The control unit 11 may correct the cancellation sound based on the result of the evaluation. The evaluation microphone can be installed at any location inside the vehicle. The evaluation microphone may be provided separately from the microphone 14 or the acceleration sensor 16, or the microphone 14 may also serve as the evaluation microphone.

[0060] For example, the control unit 11 may observe the in-vehicle noise in a state before the cancellation sound is emitted by the evaluation microphone. When the control unit 11 emits a cancellation sound by the speaker 15, the control unit 11 may observe the in-vehicle noise in a state canceled by the cancellation sound by the evaluation microphone. The control unit 11 may determine whether the degree to which the in-vehicle noise is canceled by the cancellation sound is sufficient. When the degree to which the in-vehicle noise is canceled is insufficient, the control unit 11 may correct the cancellation sound. In this case, the control unit 11 may further determine whether the in-vehicle noise is sufficiently canceled by the corrected cancellation sound based on the observed sound of the evaluation microphone.

[0061] As described above, based on the observed sound of the evaluation microphone, the control unit 11 verifies whether the noise cancellation by the cancellation sound is effectively performed, and by correcting the cancellation sound, the effect of noise cancellation can be further enhanced.

[0062] Referring to the flowchart of FIG. 4, a specific example of a noise cancellation method when training a learning model by machine learning is shown. In the noise cancellation technology according to the present embodiment, the microphone 14 or the acceleration sensor 16 may be used to acquire training data for training the learning model.

[0063] Step S201: The control unit 11 of the information processing apparatus 10 trains a learning model based on the vehicle speed information and the measured sound. Here, the learning model uses the vehicle speed information as an explanatory variable. The learning model is a learning model for causing a computer to function so as to output pattern noise, which is a target variable, based on the explanatory variable. The learning model may be a neural network model generated based on a multi-layer perceptron including an input layer, a hidden layer, and an output layer. The learning model may also be a linear model, a non-linear model, a support vector machine, or the like. The learning model may be, for example, a machine learning model constructed based on a decision tree. Machine learning models constructed based on decision trees include, for example, Light GBM, XGBoost, etc., but are not limited thereto. Alternatively, the learning model may be a model generated based on a machine learning algorithm such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), or other deep learning.

[0064] The training data for training the learning model may be the vehicle speed information and the measured sound corresponding thereto. Specifically, the control unit 11 may train the learning model by using the vehicle speed information among the training data as an explanatory variable and the measured sound corresponding thereto as a target variable.

[0065] Step S202: The control unit 11 estimates pattern noise based on the vehicle speed information. Specifically, the control unit 11 inputs the vehicle speed information into the learning model to obtain the output of the pattern noise estimated by the learning model.

[0066] Step S203: The control unit 11 generates a cancellation sound for the pattern noise. For example, the control unit 11 generates a cancellation sound corresponding to the spectrum of the estimated pattern noise in order to cancel the spectrum of the pattern noise estimated in Step S202. By making the cancellation sound correspond to the spectrum of the estimated pattern noise, the pattern noise can be appropriately canceled.

[0067] Step S204: The control unit 11 emits a cancellation sound from the speaker 15. For example, the control unit 11 emits the cancellation sound generated in step S203 from the speaker 15.

[0068] In this way, by training the learning model using the actually measured sound as training data, the more the present technology is implemented, the more the accuracy of the cancellation sound emitted from the speaker 15 can be improved, and the noise cancellation effect can be enhanced. Also, even when the characteristic information of the tire cannot be used, the control unit 11 can estimate the pattern noise by the learning model and emit an appropriate cancellation sound from the speaker 15.

[0069] Although the present disclosure has been described based on the drawings and embodiments, it should be noted that those skilled in the art can easily make various modifications and corrections based on the present disclosure. Therefore, it should be noted that these modifications and corrections are included in the scope of the present disclosure. For example, the functions included in each means or each step, etc. can be rearranged so as not to be logically contradictory, and a plurality of means or steps, etc. can be combined into one or divided.

[0070] For example, the information processing apparatus 10 according to the present embodiment may not include the microphone 14 or the acceleration sensor 16. For example, the information processing apparatus 10 may not include the microphone 14 and the acceleration sensor 16, or may include at least one of them.

[0071] Also, for example, in the present embodiment, an example in which the tires 51 and 52 are respectively provided with the communication devices 53 and 54 is shown, but the present invention is not limited to this. The tires 51 and 52 may not include the communication devices 53 and 54. In this case, the control unit 11 may acquire the characteristic information of the tire by another arbitrary method. For example, the control unit 11 may receive an input regarding the characteristic information of the tire from the outside and use it for processing.

[0072] [Contribution to the United Nations Sustainable Development Goals (SDGs)] The SDGs have been proposed towards the realization of a sustainable society. One embodiment of the present invention is considered to be a technology that can contribute to "No. 9 - Build the foundation for industry and technological innovation", etc.

Explanation of reference numerals

[0073] 10 Information processing device 11 Control unit 12 Storage unit 13 Communication unit 14 Microphone 15 Speaker 16 Acceleration sensor 17 Arithmetic unit 50 Vehicle 51 Tire 52 Tire 53 Communication device 54 Communication device G Road surface

Claims

1. A noise cancellation method executed by an information processing apparatus, comprising: estimating pattern noise based on tire characteristic information and vehicle speed information; generating a cancellation sound for the pattern noise; emitting the cancellation sound from a speaker. A noise cancellation method including the above steps.

2. The noise cancellation method according to claim 1, further comprising: the tire characteristic information includes the tire circumference and the number of pitches per revolution.

3. The noise cancellation method according to claim 2, further comprising: correcting the cancellation sound based on the measured sound.

4. The noise cancellation method according to claim 3, wherein the measured sound is measured by a microphone or an acceleration sensor.

5. The noise cancellation method according to claim 4, wherein the pattern noise is pattern excitation noise.

6. The noise cancellation method according to claim 5, further comprising: correcting the cancellation sound based on the observed sound of an evaluation microphone.

7. A noise cancellation method executed by an information processing apparatus, comprising: training a learning model based on vehicle speed information and measured sound; estimating pattern noise based on vehicle speed information; generating a cancellation sound for the pattern noise; emitting the cancellation sound from a speaker, wherein in the estimating step, inputting the vehicle speed information into the learning model; estimating pattern noise based on the learning model.

8. An information processing apparatus comprising a processor that executes the noise cancellation method according to any one of claims 1 to 7.

9. A program for causing an information processing apparatus to execute the noise cancellation method according to any one of claims 1 to 7.

Images