Information processing device, control method therefor, and program

Abstract

This information processing device comprises a control unit that generates, on the basis of characteristic information indicating characteristics of a tire of a vehicle, a cancellation sound for cancelling noise generated due to the characteristics of the tire, and causes a speaker to output the generated cancellation sound.

Description

Information processing device, control method thereof, and program

[0001] The present disclosure relates to an information processing device, a control method thereof, and a program.

[0002] It is known to output a cancellation sound to cancel out noise in order to reduce noise generated when a vehicle is traveling. For example, Patent Document 1 describes adjusting a cancellation sound to cancel road noise entering the vehicle cabin in accordance with the road surface conditions ahead of the vehicle.

[0003] Japanese Patent Application Laid-Open No. 2018-169525

[0004] However, the conventional configuration leaves room for improvement in terms of reducing noise caused by tire characteristics.

[0005] An object of the present disclosure is to more effectively reduce noise caused by tire characteristics.

[0006] (1) An information processing device according to one embodiment of the present disclosure includes a control unit that generates a cancellation sound for canceling noise caused by characteristics of a vehicle tire based on characteristic information indicating the characteristics of the tire, and outputs the generated cancellation sound to a speaker.

[0007] (2) As one embodiment of the present disclosure, in (1), the control unit may generate, as the cancellation sound, a cancellation sound for canceling air column resonance sound generated due to air column resonance in the space between the groove portion of the tire of the vehicle and the road surface with which the tire is in contact, based on the contact shape of the tire in contact with the road surface, and output the generated cancellation sound to the speaker.

[0008] (3) As one embodiment of the present disclosure, in (2), the control unit may acquire audio information of noise generated while the vehicle is traveling, detected by a microphone, analyze the audio information, and acquire air column resonance sound generated due to air column resonance in the space between the groove portion of the tire and the road surface with which the tire is in contact, and generate, as the cancellation sound, audio for canceling the acquired air column resonance sound.

[0009] (4) As an embodiment of the present disclosure, in (2) or (3), the control unit may determine a frequency based on a contact length of the tire in contact with the road surface, and generate the cancellation sound including the determined frequency.

[0010] (5) As an embodiment of the present disclosure, in (4), the control unit may obtain the contact length of the tire in contact with the road surface based on at least one of the weight of the vehicle and the internal pressure of the tire, and determine the frequency based on the obtained contact length.

[0011] (6) As an embodiment of the present disclosure, in (4) or (5), the control unit may determine the frequency by inputting the acquired contact length into a trained model that has been trained using the contact length of the tire in contact with the road surface as an explanatory variable and the peak frequency of the air column resonance sound of the tire as a target variable.

[0012] (7) As an embodiment of the present disclosure, in any one of (4) to (6), the control unit may further determine the frequency based on a shape of a tread pattern of the tire.

[0013] (8) As an embodiment of the present disclosure, in any one of (2) to (7), the control unit may acquire audio information of an observed sound generated while the vehicle is traveling, detected by a microphone, and correct the cancellation sound based on the audio information.

[0014] (9) As one embodiment of the present disclosure, in (1), the control unit may estimate cavity resonance noise based on characteristic information of the tire, generate a cancellation sound for the cavity resonance noise, and emit the cancellation sound from the speaker.

[0015] (10) As an embodiment of the present disclosure, in (9), the control unit may correct the cavity resonance noise based on vehicle speed information, and generate a cancellation sound for the corrected cavity resonance noise.

[0016] (11) As an embodiment of the present disclosure, in (9) or (10), the control unit may correct the cancellation sound based on an actual measurement sound, and emit the corrected cancellation sound from the speaker.

[0017] (12) As an embodiment of the present disclosure, in any one of (9) to (11), the actual measured sound may be measured by a microphone or an acceleration sensor.

[0018] (13) As an embodiment of the present disclosure, in any one of (9) to (12), the tire characteristic information may include a tire outer diameter and a rim diameter.

[0019] (14) As an embodiment of the present disclosure, in any one of (9) to (13), the control unit may correct the cancellation sound based on an observation sound of an evaluation microphone, and emit the corrected cancellation sound from the speaker.

[0020] (15) As an embodiment of the present disclosure, in any one of (9) to (14), the control unit may train a learning model based on the tire characteristic information, the vehicle speed information, and the measured sound, and input the tire characteristic information and the vehicle speed information to the learning model to estimate the cavity resonance sound.

[0021] (16) As one embodiment of the present disclosure, in any one of (1), the control unit may estimate pattern noise based on the tire characteristic information and vehicle speed information, generate a cancellation sound for the pattern noise, and emit the cancellation sound from the speaker.

[0022] (17) As an embodiment of the present disclosure, in any one of (16), the tire characteristic information may include a circumference of the tire and a number of pitches per circumference.

[0023] (18) As an embodiment of the present disclosure, in (16) or (17), the control unit may correct the cancellation sound based on an actual measurement sound, and emit the corrected cancellation sound from the speaker.

[0024] (19) As an embodiment of the present disclosure, in any one of (16) to (18), the actual measured sound may be measured by a microphone or an acceleration sensor.

[0025] (20) As an embodiment of the present disclosure, in any one of (16) to (19), the pattern noise may be a pattern excitation sound.

[0026] (21) As an embodiment of the present disclosure, in any one of (16) to (20), the control unit may correct the cancellation sound based on an observation sound of an evaluation microphone, and emit the corrected cancellation sound from the speaker.

[0027] (22) As an embodiment of the present disclosure, in any one of (16) to (21), the control unit may train a learning model based on vehicle speed information and measured sound, input the vehicle speed information to the learning model to estimate pattern noise, generate a cancellation sound for the pattern noise, and emit the cancellation sound from the speaker.

[0028] (23) A control method for an information processing device according to one embodiment of the present disclosure is a control method for an information processing device including a control unit, wherein the control unit generates a cancellation sound for canceling noise caused by characteristics of a tire of a vehicle based on characteristic information indicating the characteristics of the tire, and outputs the generated cancellation sound to a speaker.

[0029] (24) A program according to an embodiment of the present disclosure causes a computer to operate as the information processing device according to any one of (1) to (23).

[0030] According to one embodiment of the present disclosure, noise caused by tire characteristics can be more effectively reduced.

[0031] 1 is a diagram illustrating an example of a vehicle equipped with an information processing device according to an embodiment; FIG. 2 is a block diagram illustrating an example of the configuration of the information processing device; FIG. 3 is a flowchart illustrating an example of an operation procedure of the information processing device; FIG. 4 is a schematic diagram illustrating an example of a frequency spectrum of air column resonance sound; FIG. 5 is a diagram illustrating an example of a contact portion between a tire and a road surface; FIG. 6 is a flowchart illustrating an example of an operation procedure of the information processing device; FIG. 7 is a flowchart illustrating an example of an operation procedure of the information processing device;

[0032] Hereinafter, an embodiment of the present disclosure will be described with reference to the drawings. In each drawing, parts having the same configuration or function are denoted by the same reference numerals. In the description of this embodiment, duplicated descriptions of the same parts may be omitted or simplified as appropriate.

[0033] 1 is a diagram illustrating an example of a vehicle 50 equipped with an information processing device 10 according to an embodiment. The information processing device 10 outputs a cancellation sound for canceling noise generated when the vehicle 50 is traveling, thereby reducing noise inside the vehicle cabin.

[0034] The information processing device 10 includes a microphone 15 , a speaker 16 , an acceleration sensor 17 , and a calculation unit 19 .

[0035] The microphone 15 detects audio information of noise in the cabin of the vehicle 50. The microphone 15 may be provided, for example, near a seat in the vehicle 50. Alternatively, for example, the microphone 15 may be provided on or near the floor of the vehicle interior. The information processing device 10 may invert the phase of the audio information of the noise detected by the microphone 15 to generate a cancellation sound to cancel the noise. While the cancellation sound is being output, the information processing device 10 may use the audio information in the cabin acquired by the microphone 15 to evaluate the effectiveness of noise canceling.

[0036] The speaker 16 outputs the cancellation sound as sound waves. The speaker 16 may be realized by an audio output device of an audio system originally provided in the vehicle 50. The speaker 16 may be installed at any location inside the vehicle. For example, the speaker 16 may be installed on or near the floor inside the vehicle.

[0037] The acceleration sensor 17 measures the acceleration of the vehicle 50. The measurement value of the acceleration sensor 17 is used to evaluate NVH (Noise, Vibration, and Harshness). The acceleration sensor 17 may be realized by any type of sensor, such as a semiconductor gauge, capacitance, or differential transformer type. The acceleration sensor 17 may be attached to an axle that transmits power generated by a drive unit, such as an engine, a motor, or a combination thereof, to the tires 51, 52.

[0038] The calculation unit 19 is connected to the microphone 15, the speaker 16, and the acceleration sensor 17, and controls the operations of these devices. As will be described later with reference to FIG. 2 , the calculation unit 19 includes a control unit 11, a storage unit 12, and a communication unit 13.

[0039] The vehicle 50 is, for example, a passenger car or the like, but is not limited to this and may be any vehicle. The vehicle may be, for example, a gasoline-powered vehicle, a hybrid vehicle, a plug-in hybrid vehicle, a fuel cell vehicle, or a battery-powered electric vehicle, but is not limited to these. The vehicle 50 has tires 51 and 52.

[0040] The tire 51 is provided with a communication device 53. The tire 52 is provided with a communication device 54.

[0041] The communication devices 53, 54 perform wireless communication. The communication devices 53, 54 are, for example, RF (Radio Frequency) tags. RF tags are also called RFID (Radio Frequency Identification) tags. The communication devices 53, 54 include an IC (Integrated Circuit) chip constituting the control unit 11 and the memory unit 12, and one or more antennas connected to the IC chip. The IC chip may store any information related to the tires 51, 52, such as identification information of the tires 51, 52, the date of manufacture, the circumference, the tire outer diameter, the rim diameter, the number of pitches per circumference, and the correspondence between the contact length between the tires 51, 52 and the road surface G and the peak frequency of the air column resonance sound. The pitches are widthwise grooves formed on the surfaces of the tires 51, 52. For example, the communication devices 53, 54 may have two antennas extending linearly, wavy, or spirally extending in opposite directions from the IC chip, resulting in a longitudinal shape as a whole.

[0042] The IC chip may be operated by an induced 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 using their own power. That is, the communication devices 53 and 54 may be active communication devices.

[0043] The information processing device 10 generates a cancellation sound for canceling noise caused by the characteristics of the tires 51, 52 of the vehicle 50, based on characteristic information indicating the characteristics of the tires 51, 52. The characteristic information of the tires 51, 52 is any information related to the features of the tires 51, 52. The information processing device 10 outputs the generated cancellation sound to the speaker 16. By performing such noise canceling, the information processing device 10 can reduce the noise caused by the characteristics of the tires 51, 52.

[0044] 2 is a block diagram showing an example configuration of the information processing device 10. As described above, the information processing device 10 includes a microphone 15, a speaker 16, an acceleration sensor 17, and a calculation unit 19. The calculation unit 19 includes a control unit 11, a storage unit 12, and a communication unit 13. The calculation unit 19 is one or more computer devices that can communicate with each other. The calculation unit 19 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 may be another dedicated electronic device.

[0045] The control unit 11 includes one or more processors. In one embodiment, the "processor" may be, but is not limited to, a general-purpose processor or a dedicated processor specialized for a specific process. The control unit 11 is communicably connected to each component of the information processing device 10 and controls the operation of the information processing device 10 as a whole.

[0046] The storage unit 12 includes any storage module, such as a hard disk drive (HDD), a solid state drive (SSD), a read-only memory (ROM), and a random access memory (RAM). 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 in the operation of the information processing device 10. For example, the storage unit 12 may store system programs, application programs, and various information received by the communication unit 13. The storage unit 12 is not limited to being built into the information processing device 10, but may also be an external database or an external storage module.

[0047] The communication unit 13 includes any communication module that can communicate with other devices using 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 required for communication with other devices.

[0048] The functions of the information processing device 10 can be realized by executing a computer program (program) according to this embodiment on a processor included in the control unit 11. That is, the functions of the information processing device 10 can be realized by software. The computer program causes a computer to execute steps included in the operation of the information processing device 10, thereby causing the computer to realize functions corresponding to the processing of each step. That is, the computer program is a program that causes a computer to function as the information processing device 10 according to this embodiment. The computer program may be recorded on a computer-readable recording medium. Computer-readable recording media include non-transitory computer-readable media, such as magnetic recording devices, optical discs, magneto-optical recording media, or semiconductor memories. The program can be distributed, for example, by selling, transferring, or lending portable recording media such as DVDs (digital versatile discs) or CD-ROMs (compact disc read-only memories) on which the program is recorded. The program can also be distributed by storing the program in the storage of an external server and transmitting the program from the external server to other computers. The program may also be provided as a program product. The program includes information used for processing by an electronic computer that is equivalent to a program. For example, data that is not a direct instruction to a computer but has the properties of specifying computer processing falls under the category of "something equivalent to a program."

[0049] Some or all of the functions of the information processing device 10 may be realized by a dedicated circuit included in the control unit 11. That is, some or all of the functions of the information processing device 10 may be realized by hardware. Furthermore, the information processing device 10 may be realized by a single computer or by multiple computers working together.

[0050] (Operation Example 1) As an example of noise caused by the characteristics of the tires 51, 52, a configuration for reducing noise caused by air column resonance in the space formed between the grooves in the tread patterns of the tires 51, 52 and the ground will be described with reference to FIGS. 3 to 5 . The information processing device 10 generates a cancellation sound for canceling air column resonance noise caused by air column resonance in the space between the grooves of the tires 51, 52 of the vehicle 50 and the road surface G, based on the contact shape of the tires 51, 52 in contact with the road surface G. The information processing device 10 outputs the generated cancellation sound to the speaker 16. By performing noise cancellation of the air column resonance noise in this manner, the information processing device 10 can reduce the harsh air column resonance noise.

[0051] Fig. 3 is a flowchart showing an example of an operation procedure of the information processing device 10. The operation of the information processing device 10 described with reference to Fig. 3 may correspond to one of the control methods of the information processing device 10. The operation of each step in Fig. 3 may be executed based on the control by the control unit 11 of the information processing device 10.

[0052] In step S1, the control unit 11 generates a cancellation sound to cancel air column resonance sound that occurs due to air column resonance in the space between the grooves of the tires 51, 52 of the vehicle 50 and the road surface G with which the tires 51, 52 are in contact. The air column resonance sound is sound that occurs when the air column formed between the grooves of the tires 51, 52 and the road surface G resonates. Therefore, the control unit 11 generates the cancellation sound based on the contact shape of the tires 51, 52 in contact with the road surface G.

[0053] Specifically, the control unit 11 may determine a frequency based on the contact shape of the tires 51, 52 in contact with the road surface G, and generate a cancellation sound including the determined frequency. Fig. 4 is a schematic diagram showing an example of the frequency spectrum of air column resonance sound. In Fig. 4, the horizontal axis represents frequency, and the vertical axis represents sound wave intensity. Graph 61 shows the frequency spectrum of air column resonance sound. As shown in graph 61, the frequency of air column resonance sound has a peak around 1 kHz. The peak frequency of air column resonance sound varies mainly depending on the circumferential tread length of the tires 51, 52 in contact with the road surface G.

[0054] FIG. 5 is a diagram showing an example of contact portions between tires 51, 52 and a road surface G. In the example of FIG. 5, tires 51, 52 are in contact with the road surface G over a length (contact patch length) L. Therefore, the peak frequency of the air column resonance sound of tires 51, 52 is determined based on L. Therefore, the control unit 11 may measure in advance the relationship between the length L and the peak frequency of the air column resonance sound and store it as correspondence information in the storage unit 12. In step S1, the control unit 11 may refer to this correspondence information to obtain the peak frequency of the air column resonance sound from the length L of the contact surface between tires 51, 52 and the road surface G, and generate audio including the peak frequency as the cancellation sound. The control unit 11 may refer to the correspondence relationship obtained in this way to obtain the peak frequency of the air column resonance sound from the length L of the contact surface between tires 51, 52 and the road surface G, and generate audio including the peak frequency as the cancellation sound. In this way, the information processing device 10 generates a cancellation sound that includes a frequency corresponding to the length L over which the tires 51, 52 are in contact with the road surface G, thereby making it possible to effectively reduce air column resonance sound even without a microphone 15.

[0055] Furthermore, the length L over which the tires 51, 52 are in contact with the road surface G is determined by various factors such as the weight of the vehicle 50 and the internal pressure of the tires 51, 52. Therefore, the control unit 11 may obtain the length L over which the tires 51, 52 are in contact with the road surface G based on at least one of the weight of the vehicle 50 and the internal pressure of the tires 51, 52, and determine the frequency of the cancellation sound based on the obtained length L. With this configuration, even if it is difficult to directly measure the length L, the information processing device 10 can generate an appropriate cancellation sound to cancel the air column resonance sound based on the weight of the vehicle 50 and the internal pressure of the tires 51, 52, etc. Therefore, the information processing device 10 can effectively reduce the air column resonance sound through the cancellation action of the cancellation sound.

[0056] The control unit 11 may also acquire and analyze audio information of noise generated while the vehicle 50 is traveling, detected by the microphone 15, to acquire air column resonance sound generated due to air column resonance in the space between the grooves of the tires 51, 52 and the road surface G with which the tires 51, 52 are in contact. The control unit 11 may also generate a cancellation sound to cancel the acquired air column resonance sound. In this way, the information processing device 10 outputs a cancellation sound from the speaker 16 to cancel the air column resonance sound detected based on the audio information, thereby effectively reducing the harsh air column resonance sound. The control unit 11 may also determine a frequency range in which a peak frequency may exist based on the length L, etc., and analyze the audio information by narrowing the frequency range to that range. By limiting the frequency range to be analyzed in this way, efficient processing can be performed.

[0057] The control unit 11 may determine the frequency by inputting the acquired length L into a trained model that has been trained using the length L over which the tires 51, 52 are in contact with the road surface G as an explanatory variable and the peak frequency of the air column resonance noise of the tires 51, 52 as a target variable. The information processing device 10 can effectively reduce the air column resonance noise by determining the frequencies included in the cancellation sound using such a trained model.

[0058] As described above, the peak frequency of the air column resonance noise varies mainly depending on the circumferential tread length L of the tires 51, 52 that are in contact with the road surface G, but may also be affected by the widthwise tread of the tires 51, 52. Therefore, the control unit 11 may determine the peak frequency of the cancellation noise based on the shape of the tread pattern of the tires 51, 52. In this way, the information processing device 10 can effectively reduce the air column resonance noise by determining the frequencies included in the cancellation noise based also on the shape of the tread pattern of the tires 51, 52.

[0059] In step S2, the control unit 11 causes the speaker 16 to output the cancellation sound generated in step S1.

[0060] In step S3, the control unit 11 acquires audio information of the observed sound in the vehicle cabin using the microphone 15.

[0061] In step S4, the control unit 11 corrects the cancellation sound based on the audio information of the observation sound acquired in step S3.

[0062] In step S5, the control unit 11 outputs the cancellation sound corrected in step S4 to the speaker 16. After completing the process of step S5, the control unit 11 ends the process of the flowchart.

[0063] As described above, the information processing device 10 generates a cancellation sound based on the contact shape of the tires 51, 52 with the road surface G and outputs the cancellation sound to the speaker 16. Here, the information processing device 10 acquires audio information of the observed sound generated while the vehicle 50 is traveling, detected by the microphone 15, and corrects the cancellation sound based on the audio information. In this way, the information processing device 10 can further effectively reduce the air column resonance sound by verifying whether the cancellation sound is effectively canceling out the air column resonance sound and correcting the cancellation sound.

[0064] (Operation Example 2) Next, a configuration for canceling cavity resonance, which is particularly likely to be heard as an unpleasant sound by the human ear, among noises entering the vehicle cabin as an example of noise caused by the characteristics of the tires 51, 52, will be described with reference to Figures 6 and 7. The information processing device 10 estimates cavity resonance based on characteristic information of the tires 51, 52. The information processing device 10 generates a cancellation sound for the cavity resonance and emits the cancellation sound from the speaker 16. This makes it possible to efficiently suppress cavity resonance, which is particularly likely to be heard as an unpleasant sound by the human ear, and improves noise canceling technology.

[0065] Here, cavity resonance noise refers to noise generated by cavity resonance of the gas filled in the inner cavity of the tires 51, 52 when the tread portions of the tires 51, 52 collide with unevenness in the road surface G and vibrate while the vehicle 50 is traveling. The characteristic information of the tires 51, 52 refers to any information related to the features of the tires 51, 52. It is preferable that the characteristic information of the tires 51, 52 used by the information processing device 10 be information that can affect the spectrum of cavity resonance noise observed inside the vehicle cabin.

[0066] The characteristic information of the tires 51, 52 may include, for example, the size of the tires 51, 52, the type of the tires 51, 52, the presence or absence or type of inclusions, etc. The size of the tires 51, 52 may include the outer diameter of the tires 51, 52, the inner diameter (rim diameter) of the tires 51, 52, etc. The outer diameter of the tires 51, 52 may be the diameter (diameter of a donut-shaped cross section) of the tires 51, 52 when the tires 51, 52 are mounted on a rim, inflated to an appropriate air pressure, and in a state where no load is applied. The outer diameter of the tires 51, 52 may be calculated from the rim diameter, tire width, aspect ratio, etc. The type of the tires 51, 52 may include passenger car tires, light truck tires, small truck tires, truck tires, bus tires, motorcycle tires, etc. The inclusions of the tires 51, 52 may include sound-absorbing sponge, film, etc. Furthermore, the characteristic information of the tires 51, 52 may be, for example, the circumferential length of the tires 51, 52, the number of pitches per circumference, and the like.

[0067] The characteristic information of the tires 51, 52 may also include various other information related to the features of the tires 51, 52. For example, the characteristic information of the tires 51, 52 may include information on changes over time, deterioration, changes due to use, or wear of the tires 51, 52.

[0068] According to the noise canceling technology of the embodiment of the present disclosure, it is possible to cancel out cavity resonance noise, which is particularly likely to be heard as unpleasant sound by the human ear, among noises entering the vehicle cabin. In other words, the information processing device 10 estimates cavity resonance noise based on characteristic information of the tires 51, 52. The information processing device 10 generates a cancellation sound for the estimated cavity resonance noise and emits it from the speaker 16. This makes it possible to efficiently reduce noise based on known information, namely the characteristic information of the tires 51, 52.

[0069] In this way, according to the noise canceling technology of this embodiment, noise can be efficiently reduced by canceling out cavity resonance based on known information, namely, characteristic information of the tires 51, 52, thereby improving the noise canceling technology.

[0070] Referring to the flowchart of FIG. 6, a noise canceling method according to one embodiment of the present disclosure is illustrated.

[0071] Step S11 : The control unit 11 of the information processing device 10 estimates cavity resonance noise based on characteristic information of the tires 51 and 52 .

[0072] For example, the control unit 11 acquires characteristic information of the tires 51, 52 by communicating with the communication devices 53, 54. The control unit 11 may store the characteristic information of the tires 51, 52 in the storage unit 12. In this case, the control unit 11 may estimate cavity resonance noise based on the characteristic information of the tires 51, 52 stored in the storage unit 12.

[0073] Step S12: The control unit 11 generates a cancellation sound for the cavity resonance. For example, the control unit 11 generates a cancellation sound that corresponds to the spectrum of the estimated cavity resonance in order to cancel out the spectrum of the cavity resonance estimated in step S11. By making the cancellation sound correspond to the spectrum of the estimated cavity resonance, the cavity resonance can be appropriately canceled out.

[0074] Step S13: The control unit 11 emits a cancellation sound from the speaker 16. For example, the control unit 11 emits the cancellation sound generated in step S12 from the speaker 16.

[0075] As described above, with the noise canceling technology according to this embodiment, the information processing device 10 estimates cavity resonance noise based on characteristic information about the tires 51, 52 and emits a corresponding cancellation sound, thereby efficiently reducing noise and improving the noise canceling technology.

[0076] Furthermore, the control unit 11 may correct the estimated cavity resonance noise based on vehicle speed information. Actual cavity resonance noise may be affected by the speed of the vehicle 50. Therefore, by correcting the estimated cavity resonance noise based on the vehicle speed information, the control unit 11 can estimate a sound that is closer to the actual noise. This improves the noise cancellation effect. The speed information of the vehicle 50 can be obtained 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 17.

[0077] The control unit 11 may correct the generated cancellation sound based on the measured sound. Here, the measured sound refers to the noise inside the vehicle that is actually observed. The control unit 11 may collect the measured sound using the microphone 15.

[0078] Furthermore, the control unit 11 may observe the measured sound not by the microphone 15 but by the acceleration sensor 17, for example. In this case, the control unit 11 may convert the data observed by the acceleration sensor 17 into a sound spectrum and use the sound spectrum as the measured sound. The acceleration sensor 17 may be installed at any location on the path along which the cavity resonance sound propagates into the vehicle cabin. For example, the acceleration sensor 17 may be installed on the axle.

[0079] The measured sound may include various noises other than cavity resonance. Among noises, cavity resonance is considered to be particularly audible to the human ear as an unpleasant sound. Therefore, in order to efficiently cancel noise by focusing on cavity resonance, the control unit 11 may extract and narrow down a portion of the measured sound that corresponds to the spectral bandwidth of the cancellation sound generated by the control unit 11. 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 a frequency band that corresponds to the frequency band of the cancellation sound generated by the control unit 11.

[0080] Here, correcting the generated cancellation sound based on the measured sound includes the control unit 11 directly correcting the cancellation sound generated in step S12 based on the measured sound. Also, correcting the generated cancellation sound based on the measured sound includes the control unit 11 correcting the cavity resonance sound estimated in step S11. In the latter case, when the control unit 11 corrects the cavity resonance sound estimated in step S11, the cancellation sound generated corresponding to this is corrected in step S12. Thus, in either case, the result is that the control unit 11 corrects the generated cancellation sound based on the measured sound.

[0081] In this way, the control unit 11 can efficiently cancel cavity resonance noise by correcting the cancellation sound based on the measured sound. In other words, the processing efficiency when generating the cancellation sound can be improved compared to when generating cancellation sound that cancels the entire frequency band of the measured sound. Furthermore, by using the microphone 15 or the acceleration sensor 17, the information processing device 10 can observe the measured sound and use this information to generate or correct the cancellation sound, thereby improving the effectiveness of noise cancellation.

[0082] Furthermore, the characteristic information of the tires 51, 52 may include the tire outer diameter and the rim diameter. That is, in step S11, the control unit 11 may estimate the cavity resonance noise based on the characteristic information of the tires 51, 52 including the tire outer diameter and the rim diameter. In this case, for example, the control unit 11 may calculate the thickness of the air layer in the tires 51, 52 based on the tire outer diameter and the rim diameter. In other words, the control unit 11 may calculate the radial cavity length in the doughnut-shaped cross section of the tires 51, 52 based on the tire outer diameter and the rim diameter. The control unit 11 may further estimate the cavity resonance noise based on the calculated cavity length.

[0083] Cavity resonance noise can be affected by the thickness of the cavity inside the tires 51, 52. The thickness of the cavity inside the tires 51, 52 can be calculated from information such as the tire outer diameter and rim diameter. Therefore, by including the tire outer diameter and rim diameter in the characteristic information of the tires 51, 52, cavity resonance noise can be estimated efficiently, and the noise cancellation effect can be improved.

[0084] Furthermore, the control unit 11 may correct the cancellation sound based on the sound observed by the evaluation microphone. The evaluation microphone may at least observe the interior noise canceled by the cancellation sound when the speaker 16 emits the cancellation sound. The control unit 11 may evaluate whether the cavity resonance sound is effectively canceled by the cancellation sound emitted by the speaker 16 based on the sound observed by the evaluation microphone. The control unit 11 may correct the cancellation sound based on the evaluation result. The evaluation microphone may be installed at any location inside the vehicle. The evaluation microphone may be provided separately from the microphone 15 or the acceleration sensor 17, or may be combined with the microphone 15.

[0085] For example, the control unit 11 may use the evaluation microphone to observe the interior vehicle noise before the cancellation sound is emitted. When the cancellation sound is emitted from the speaker 16, the control unit 11 may use the evaluation microphone to observe the interior vehicle noise in a state where it has been canceled by the cancellation sound. The control unit 11 may determine whether the degree to which the interior vehicle noise has been canceled by the cancellation sound is sufficient. If the degree to which the interior vehicle noise has been canceled is insufficient, the control unit 11 may correct the cancellation sound. In this case, the control unit 11 may further determine whether the interior vehicle noise has been sufficiently canceled by the corrected cancellation sound, based on the sound observed by the evaluation microphone.

[0086] In this way, the control unit 11 verifies whether the cancellation sound is effectively canceling out noise based on the sound observed by the evaluation microphone, and corrects the cancellation sound, thereby further enhancing the effectiveness of noise cancellation.

[0087] A specific example of a noise canceling method when training a learning model by machine learning will be shown with reference to the flowchart of FIG.

[0088] Step S21: The control unit 11 of the information processing device 10 trains a learning model based on the characteristic information of the tires 51, 52, vehicle speed information, and measured sound. Here, the learning model uses the characteristic information of the tires 51, 52 and the vehicle speed information as explanatory variables. The learning model is a learning model that causes a computer to function to output the cavity resonance sound, which is the objective variable, based on these explanatory variables. The learning model may be a neural network model generated based on a multilayer perceptron consisting of an input layer, a hidden layer, and an output layer. The learning model may also be a linear model, a nonlinear model, a support vector machine, or the like. The learning model may also be a machine learning model constructed based on, for example, a decision tree. Examples of machine learning models constructed based on a decision tree include, but are not limited to, Light GBM and XGBoost. Alternatively, the learning model may be a model generated based on a machine learning algorithm such as a convolutional neural network (CNN), a recurrent neural network (RNN), or other deep learning algorithms.

[0089] The training data for training the learning model may include characteristic information of the tires 51, 52, vehicle speed information, and the corresponding measured sounds. Specifically, the control unit 11 may train the learning model by providing the characteristic information of the tires 51, 52 and the vehicle speed information of the training data as explanatory variables and the corresponding measured sounds as a target variable. The characteristic information of the tires 51, 52 may include the tire outer diameter and the rim diameter.

[0090] Step S22: The control unit 11 inputs the characteristic information of the tires 51, 52 and the vehicle speed information into the learning model. In other words, the control unit 11 inputs the characteristic information of the tires 51, 52 and the vehicle speed information, which are explanatory variables, into the learning model. For example, the control unit 11 acquires the characteristic information of the tires 51, 52 by communicating with the communication devices 53, 54. The control unit 11 may also store the characteristic information of the tires 51, 52 in the memory unit 12. In this case, the control unit 11 may input the characteristic information of the tires 51, 52 stored in the memory unit 12 into the learning model.

[0091] Step S23: The control unit 11 estimates the cavity resonance noise based on the learning model. Specifically, the control unit 11 inputs the characteristic information of the tires 51, 52 and the vehicle speed information to the learning model, thereby obtaining an output of the cavity resonance noise estimated by the learning model.

[0092] Step S24: The control unit 11 generates a cancellation sound for the cavity resonance. For example, the control unit 11 generates a cancellation sound that corresponds to the estimated spectrum of the cavity resonance in order to cancel out the spectrum of the cavity resonance estimated in step S23. By making the cancellation sound correspond to the spectrum of the estimated cavity resonance, the cavity resonance can be appropriately canceled out.

[0093] Step S25: The control unit 11 emits a cancellation sound from the speaker 16. For example, the control unit 11 emits the cancellation sound generated in step S24 from the speaker 16.

[0094] In this way, by training the learning model using actually measured sounds as training data, the more this technology is implemented, the more accurate the cancellation sound emitted from the speaker 16 can be improved, and the more effective the noise cancellation can be.

[0095] Although the present disclosure has been described based on the drawings and examples, it should be noted that those skilled in the art can easily make various modifications and alterations based on the present disclosure. Therefore, it should be noted that these modifications and alterations are included in the scope of the present disclosure. For example, the functions included in each means or step can be rearranged so as not to be logically inconsistent, and multiple means or steps can be combined or divided into one.

[0096] For example, the information processing device 10 according to this embodiment may not include the microphone 15 or the acceleration sensor 17. For example, the information processing device 10 may not include the microphone 15 or the acceleration sensor 17, or may include at least one of them.

[0097] Furthermore, for example, in the present embodiment, the tire 51 and the tire 52 are provided with the communication device 53 and the communication device 54, respectively, but this is not limiting. The tire 51 and the tire 52 may not be provided with the communication device 53 and the communication device 54. In this case, the control unit 11 may acquire the tire characteristic information by any other method. For example, the control unit 11 may receive input related to the tire characteristic information from an external source and use the input for processing.

[0098] (Operation Example 3) Next, a configuration for canceling pattern noise, in particular, among noises entering the vehicle cabin as an example of noise caused by the characteristics of the tires 51, 52, will be described with reference to FIGS. 8 and 9. The information processing device 10 estimates the pattern noise based on characteristic information about the tires 51, 52 and vehicle speed information. The information processing device 10 generates a cancellation sound for the pattern noise and emits the cancellation sound from the speaker 16. This makes it possible to efficiently suppress the pattern noise, improving noise canceling technology.

[0099] Here, the pattern noise refers to noise emitted when discontinuous portions of the tread patterns of the tires 51, 52 come into contact with the road surface G and collide with the road surface G, causing the tires 51, 52 to vibrate due to the impact force. Furthermore, the characteristic information of the tires 51, 52 refers to any information relating to the features of the tires 51, 52. It is preferable that the characteristic information of the tires 51, 52 used by the information processing device 10 be information that can affect the spectrum of the pattern noise observed inside the vehicle cabin.

[0100] The characteristic information of the tires 51, 52 may be, for example, the circumference of the tires 51, 52, the number of pitches per circumference, etc. Furthermore, the characteristic information of the tires 51, 52 may be the size of the tires 51, 52, the type of tire, the presence or absence or type of inclusions, etc. The size of the tires 51, 52 may include the outer diameter, inner tire diameter (rim diameter), etc. of the tires 51, 52. The outer diameter of the tires 51, 52 may be the diameter (diameter of a donut-shaped cross section) of the tires 51, 52 when the tires 51, 52 are mounted on a rim, inflated to an appropriate air pressure, and in a no-load state. The outer diameter of the tires 51, 52 may be calculated from the rim diameter, tire width, aspect ratio, etc. The types of the tires 51, 52 may include passenger car tires, light truck tires, small truck tires, truck tires, bus tires, motorcycle tires, etc. The inclusions of the tires 51, 52 may include sound-absorbing sponges, films, etc.

[0101] The characteristic information of the tires 51, 52 may also include various other information related to the features of the tires 51, 52. For example, the characteristic information of the tires 51, 52 may include information on changes over time, deterioration, changes due to use, or wear of the tires 51, 52.

[0102] According to the noise canceling technology of the embodiment of the present disclosure, it is possible to cancel out noise entering the vehicle cabin, particularly pattern noise. In other words, the information processing device 10 estimates the pattern noise based on characteristic information of the tires 51, 52 and vehicle speed information. The information processing device 10 generates a cancellation sound for the estimated pattern noise and emits it from the speaker 16. This makes it possible to efficiently reduce noise based on known information, i.e., the characteristic information of the tires 51, 52, and easily available vehicle speed information.

[0103] In this way, according to the noise canceling technology of this embodiment, noise can be efficiently reduced by canceling out pattern noise based on known information, such as characteristic information of the tires 51, 52, and easily available vehicle speed information, thereby improving the noise canceling technology.

[0104] Referring to the flowchart of FIG. 8, a noise canceling method according to one embodiment of the present disclosure is illustrated.

[0105] Step S31: The control unit 11 of the information processing device 10 estimates pattern noise based on the characteristic information of the tires 51, 52 and the vehicle speed information.

[0106] For example, the control unit 11 acquires characteristic information of the tires 51, 52 by communicating with the communication devices 53, 54. The control unit 11 may store the characteristic information of the tires 51, 52 in the storage unit 12. In this case, the control unit 11 may estimate the pattern noise based on the characteristic information of the tires 51, 52 stored in the storage unit 12.

[0107] The speed information of the vehicle 50 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 17.

[0108] Step S32: 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 out the spectrum of the pattern noise estimated in step S31. By making the cancellation sound correspond to the spectrum of the estimated pattern noise, the pattern noise can be appropriately canceled out.

[0109] Step S33: The control unit 11 emits a cancellation sound from the speaker 16. For example, the control unit 11 emits the cancellation sound generated in step S32 from the speaker 16.

[0110] As described above, with the noise canceling technology according to this embodiment, the information processing device 10 estimates pattern noise based on the characteristic information of the tires 51, 52 and the vehicle speed information, and emits a corresponding cancellation sound. This makes it possible to efficiently reduce noise, thereby improving the noise canceling technology.

[0111] Furthermore, the characteristic information of the tires 51, 52 may include the circumferential lengths and the number of pitches per circumference of the tires 51, 52. That is, in step S31, the control unit 11 may estimate the pattern noise based on the characteristic information of the tires 51, 52, including the circumferential lengths and the number of pitches per circumference of the tires 51, 52, and on the basis of vehicle speed information.

[0112] Pattern noise can be affected by the traveling speed, the circumferential length and the number of pitches per circumference of the tires 51, 52. Therefore, by including the characteristic information of the tires 51, 52 in the circumferential length and the number of pitches per circumference of the tires 51, 52, pattern noise can be efficiently estimated and the noise cancellation effect can be improved.

[0113] The control unit 11 may correct the generated cancellation sound based on the measured sound. Here, the measured sound refers to the noise inside the vehicle that is actually observed. The control unit 11 may collect the measured sound using the microphone 15.

[0114] Furthermore, the control unit 11 may observe the measured sound not by the microphone 15 but by the acceleration sensor 17, for example. In this case, the control unit 11 may convert the data observed by the acceleration sensor 17 into a sound spectrum and use it as the measured sound. The acceleration sensor 17 may be installed at any location on the path along which the pattern noise propagates into the vehicle cabin. For example, the acceleration sensor 17 may be installed on the axle.

[0115] The measured sound may include various types of noise other than pattern noise. Among these types of noise, pattern noise can be predicted to a certain extent by calculating using the circumferential lengths of the tires 51 and 52 and the number of pitches per circumference. Therefore, in order to efficiently cancel noise by focusing on pattern noise, the control unit 11 may extract and narrow down a portion of the measured sound that corresponds to the spectral bandwidth of the cancellation sound generated by the control unit 11. 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 a frequency band that corresponds to the frequency band of the cancellation sound generated by the control unit 11.

[0116] Here, correcting the generated cancellation sound based on the measured sound includes the control unit 11 directly correcting the cancellation sound generated in step S32 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 S31. In the latter case, when the control unit 11 corrects the estimated pattern noise in step S31, the cancellation sound generated corresponding to this is corrected in step S32. Thus, in either case, the result is that the control unit 11 corrects the generated cancellation sound based on the measured sound.

[0117] In this way, the control unit 11 can efficiently cancel pattern noise by correcting the cancellation sound based on the measured sound. In other words, the processing efficiency when generating the cancellation sound can be improved compared to when generating a cancellation sound that cancels the entire frequency band of the measured sound. Furthermore, by using the microphone 15 or the acceleration sensor 17, the information processing device 10 can observe the measured sound and use this information to generate or correct the cancellation sound, thereby improving the effectiveness of noise cancellation.

[0118] Furthermore, the pattern noise may be a pattern vibration sound. The pattern vibration sound is a sound generated when the tires 51, 52 vibrate due to the impact when the tires 51, 52 rotate and contact the road surface G. The frequency of the pattern vibration sound is considered to increase in proportion to the traveling speed. Therefore, by using the pattern vibration sound as the pattern noise, the pattern noise can be effectively estimated using vehicle speed information, thereby enhancing the effect of noise cancellation.

[0119] Furthermore, the control unit 11 may correct the cancellation sound based on the sound observed by the evaluation microphone. The evaluation microphone may at least observe the interior noise canceled by the cancellation sound when the speaker 16 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 16 based on the sound observed by the evaluation microphone. The control unit 11 may correct the cancellation sound based on the evaluation result. The evaluation microphone may be installed at any location inside the vehicle. The evaluation microphone may be provided separately from the microphone 15 or the acceleration sensor 17, or may be combined with the microphone 15.

[0120] For example, the control unit 11 may use the evaluation microphone to observe the interior vehicle noise before the cancellation sound is emitted. When the cancellation sound is emitted from the speaker 16, the control unit 11 may use the evaluation microphone to observe the interior vehicle noise in a state where it has been canceled by the cancellation sound. The control unit 11 may determine whether the degree to which the interior vehicle noise has been canceled by the cancellation sound is sufficient. If the degree to which the interior vehicle noise has been canceled is insufficient, the control unit 11 may correct the cancellation sound. In this case, the control unit 11 may further determine whether the interior vehicle noise has been sufficiently canceled by the corrected cancellation sound, based on the sound observed by the evaluation microphone.

[0121] In this way, the control unit 11 verifies whether the cancellation sound is effectively canceling out noise based on the sound observed by the evaluation microphone, and corrects the cancellation sound, thereby further enhancing the effectiveness of noise cancellation.

[0122] A specific example of a noise canceling method in which a learning model is trained by machine learning will be shown with reference to the flowchart in Fig. 9. In the noise canceling technology according to this embodiment, the microphone 15 or the acceleration sensor 17 may be used to acquire training data for training the learning model.

[0123] Step S41: The control unit 11 of the information processing device 10 trains a learning model based on vehicle speed information and measured sound. Here, the learning model uses vehicle speed information as an explanatory variable. The learning model is a learning model that causes a computer to function to output pattern noise, which is a target variable, based on the explanatory variables. The learning model may be a neural network model generated based on a multilayer perceptron consisting of an input layer, a hidden layer, and an output layer. The learning model may also be a linear model, a nonlinear model, a support vector machine, or the like. The learning model may also be, for example, a machine learning model constructed based on a decision tree. Examples of machine learning models constructed based on a decision tree include, but are not limited to, Light GBM and XGBoost. Alternatively, the learning model may be a model generated based on a machine learning algorithm such as a convolutional neural network (CNN), a recurrent neural network (RNN), or other deep learning.

[0124] The training data for training the learning model may be vehicle speed information and the corresponding measured sound. Specifically, the control unit 11 may train the learning model by providing the vehicle speed information of the training data as an explanatory variable and the corresponding measured sound as a target variable.

[0125] Step S42: The control unit 11 estimates the pattern noise based on the vehicle speed information. Specifically, the control unit 11 inputs the vehicle speed information to the learning model, and obtains an output of the pattern noise estimated by the learning model.

[0126] Step S43: 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 out the spectrum of the pattern noise estimated in step S42. By making the cancellation sound correspond to the spectrum of the estimated pattern noise, the pattern noise can be appropriately canceled out.

[0127] Step S44: The control unit 11 emits a cancellation sound from the speaker 16. For example, the control unit 11 emits the cancellation sound generated in step S43 from the speaker 16.

[0128] In this way, by training the learning model using actually measured sounds as training data, the more this technology is implemented, the more the accuracy of the cancellation sound emitted from the speaker 16 can be improved, and the more effective the noise cancellation can be. Furthermore, even in cases where characteristic information on the tires 51, 52 is not available, the control unit 11 can estimate pattern noise using the learning model and emit an appropriate cancellation sound from the speaker 16.

[0129] Although the present disclosure has been described based on the drawings and examples, it should be noted that those skilled in the art can easily make various modifications and alterations based on the present disclosure. Therefore, it should be noted that these modifications and alterations are included in the scope of the present disclosure. For example, the functions included in each means or step can be rearranged so as not to be logically inconsistent, and multiple means or steps can be combined or divided into one.

[0130] For example, the information processing device 10 according to this embodiment may not include the microphone 15 or the acceleration sensor 17. For example, the information processing device 10 may not include the microphone 15 or the acceleration sensor 17, or may include at least one of them.

[0131] Furthermore, for example, in the present embodiment, the tire 51 and the tire 52 are provided with the communication device 53 and the communication device 54, respectively, but this is not limiting. The tire 51 and the tire 52 may not be provided with the communication device 53 and the communication device 54. In this case, the control unit 11 may acquire the characteristic information of the tires 51 and 52 by any other method. For example, the control unit 11 may receive input related to the characteristic information of the tires 51 and 52 from an external source and use the input for processing.

[0132] As described above, the information processing device 10 may generate a cancellation sound for canceling air column resonance sound generated due to air column resonance in the space between the groove portions of the tires 51, 52 of the vehicle 50 and the road surface G with which the tires 51, 52 are in contact, based on the contact shape of the tires 51, 52 with the road surface G. The information processing device 10 may output the generated cancellation sound to the speaker 16. With this configuration, the cancellation sound for canceling the air column resonance sound is output from the speaker 16, making it possible to reduce the harsh air column resonance sound.

[0133] Furthermore, the information processing device 10 may acquire audio information of noise generated while the vehicle 50 is traveling, detected by the microphone 15. The information processing device 10 may analyze the audio information to acquire air column resonance sound generated due to air column resonance in the space between the groove portions of the tires 51, 52 and the road surface G with which the tires 51, 52 are in contact. The information processing device 10 may generate a cancellation sound to cancel the acquired air column resonance sound. With this configuration, a cancellation sound to cancel the air column resonance sound detected based on the audio information is output from the speaker 16, thereby making it possible to effectively reduce the harsh air column resonance sound.

[0134] Furthermore, the information processing device 10 may determine the frequency based on the contact length of the tires 51, 52 in contact with the road surface G. The information processing device 10 may generate a cancellation sound including the determined frequency. With this configuration, a cancellation sound including a frequency according to the contact length of the tires 51, 52 in contact with the road surface G is generated, so that it is possible to effectively reduce air column resonance noise even without the microphone 15.

[0135] Furthermore, the information processing device 10 may acquire the contact length of the tires 51, 52 in contact with the road surface G based on at least one of the weight of the vehicle 50 and the internal pressure of the tires 51, 52. The information processing device 10 may determine the frequency based on the acquired contact length. With this configuration, it is possible to effectively reduce air column resonance noise even if the weight of the vehicle 50 and the internal pressure of the tires 51, 52 change.

[0136] Furthermore, the information processing device 10 may determine the frequency by inputting the acquired contact length into a trained model that has been trained using the contact length, where the tires 51, 52 are in contact with the road surface G, as an explanatory variable and the peak frequency of the air column resonance sound of the tires 51, 52 as a target variable. With this configuration, the frequencies included in the cancellation sound are determined using a trained model that has been trained using the contact length as an explanatory variable and the peak frequency of the air column resonance sound of the tires 51, 52 as a target variable, and therefore it is possible to effectively reduce the air column resonance sound.

[0137] Furthermore, the information processing device 10 may further determine the frequency based on the shape of the tread pattern of the tires 51, 52. With this configuration, the frequency included in the cancellation sound is determined based on the shape of the tread pattern of the tires 51, 52 as well, so that it is possible to effectively reduce the air column resonance sound.

[0138] Furthermore, the information processing device 10 may acquire audio information of observed sounds that are generated while the vehicle 50 is traveling, detected by the microphone 15. The information processing device 10 may correct the cancellation sound based on the audio information. With this configuration, it is possible to further effectively reduce air column resonance sound by verifying whether the cancellation sound is effectively canceling out the air column resonance sound and correcting the cancellation sound.

[0139] Furthermore, the information processing device 10 may estimate cavity resonance noise based on characteristic information of the tires 51, 52. The information processing device 10 may generate a cancellation sound for the cavity resonance noise. The information processing device 10 may emit the cancellation sound from the speaker 16. According to this configuration, the information processing device 10 emits a cancellation sound that cancels out cavity resonance noise, which is likely to be heard as an unpleasant sound by the human ear, based on the characteristic information of the tires 51, 52, thereby efficiently reducing noise and improving noise canceling technology.

[0140] Furthermore, when estimating cavity resonance, the information processing device 10 may further correct the cavity resonance based on vehicle speed information. With this configuration, the information processing device 10 can estimate a sound that is closer to the actual noise, thereby improving the effectiveness of noise cancellation.

[0141] Furthermore, the information processing device 10 may correct the cancellation sound based on the measured sound. With this configuration, the information processing device 10 extracts a portion of the measured sound that corresponds to the frequency band of the generated cancellation sound, and generates a cancellation sound that cancels this, thereby making it possible to efficiently cancel the cavity resonance sound.

[0142] The measured sound may also be measured by the microphone 15 or the acceleration sensor 17. With such a configuration, the information processing device 10 can observe the measured sound and use it to generate or correct the cancellation sound, thereby improving the effectiveness of noise cancellation.

[0143] The characteristic information of the tires 51, 52 may also include the outer diameters and rim diameters of the tires 51, 52. With this configuration, it is possible to efficiently estimate cavity resonance noise based on the outer diameters and rim diameters of the tires 51, 52, thereby improving the noise cancellation effect.

[0144] Furthermore, the information processing device 10 may correct the cancellation sound based on the sound observed by the evaluation microphone. With this configuration, the information processing device 10 can verify whether the cancellation sound is effectively canceling out the noise, and correct the cancellation sound, thereby further improving the effectiveness of noise cancellation.

[0145] Furthermore, the information processing device 10 may train a learning model based on characteristic information, vehicle speed information, and measured sound of the tires 51, 52. The information processing device 10 may input the characteristic information and vehicle speed information of the tires 51, 52 into the learning model to estimate cavity resonance sound. According to such a configuration, by training the learning model using 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 10 can be improved, and the more the noise cancellation effect can be enhanced.

[0146] Furthermore, the information processing device 10 may estimate pattern noise based on characteristic information of the tires 51, 52 and vehicle speed information. The information processing device 10 may generate a cancellation sound for the pattern noise. The information processing device 10 may emit the cancellation sound from the speaker 16. With this configuration, the information processing device 10 emits a cancellation sound that cancels out the pattern noise based on the characteristic information of the tires 51, 52 and vehicle speed information, thereby efficiently reducing noise and improving noise canceling technology.

[0147] Furthermore, the characteristic information of the tires 51, 52 may include the circumferential lengths and the number of pitches per circumference of the tires 51, 52. According to this configuration, the characteristic information of the tires 51, 52 includes the circumferential lengths and the number of pitches per circumference of the tires 51, 52, so that pattern noise can be efficiently estimated and the noise cancellation effect can be improved.

[0148] Furthermore, the information processing device 10 may correct the cancellation sound based on the measured sound. With this configuration, the information processing device 10 extracts a portion of the measured sound that corresponds to the frequency band of the generated cancellation sound, and generates a cancellation sound that cancels this, thereby enabling efficient cancellation of pattern noise.

[0149] The measured sound may also be measured by the microphone 15 or the acceleration sensor 17. With such a configuration, the information processing device 10 can observe the measured sound and use it to generate or correct the cancellation sound, thereby improving the effectiveness of noise cancellation.

[0150] Furthermore, the pattern noise may be a pattern excitation sound. With this configuration, since the pattern noise is a pattern excitation sound, the pattern noise can be effectively estimated using the vehicle speed information, thereby enhancing the effect of noise cancellation.

[0151] Furthermore, the information processing device 10 may correct the cancellation sound based on the sound observed by the evaluation microphone. With this configuration, the information processing device 10 can verify whether the cancellation sound is effectively canceling out the noise, and correct the cancellation sound, thereby further improving the effectiveness of noise cancellation.

[0152] Furthermore, the information processing device 10 may train a learning model based on vehicle speed information and the measured sound. The information processing device 10 may input vehicle speed information into the learning model to estimate pattern noise. The information processing device 10 may generate a cancellation sound for the pattern noise. The information processing device 10 may emit the cancellation sound from the speaker 16. According to this configuration, by training the learning model using vehicle speed information and the 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 10 can be improved, and the noise cancellation effect can be enhanced. Furthermore, according to the present technology, even in cases where characteristic information of the tires 51, 52 is not available, it is possible to estimate pattern noise using the learning model and emit an appropriate cancellation sound without using the characteristic information of the tires 51, 52.

[0153] The present disclosure is not limited to the above-described embodiments. For example, multiple blocks shown in the block diagram may be integrated, or one block may be divided. Multiple steps shown in the flowchart may be executed in parallel or in a different order depending on the processing capacity of the device executing each step, or as needed, instead of being executed in chronological order as described. Other modifications are possible within the scope of the present disclosure. [Contribution to the United Nations-led Sustainable Development Goals (SDGs)]

[0154] The SDGs have been proposed to realize a sustainable society. One embodiment of the present invention is thought to be a technology that can contribute to "No. 9 - Building a foundation for industry and technological innovation."

[0155] REFERENCE SIGNS LIST 10 Information processing device 11 Control unit 12 Memory unit 13 Communication unit 14 Camera 15 Microphone 16 Speaker 17 Acceleration sensor 19 Calculation unit 50 Vehicle 51, 52 Tires 53, 54 Communication device 61 Graph G Road surface

Claims

1. An information processing device comprising: a control unit that generates a cancellation sound for canceling noise caused by characteristics of tires of a vehicle based on characteristic information indicating the characteristics of the tires; and outputs the generated cancellation sound to a speaker.

2. The information processing device according to claim 1, wherein the control unit generates, as the cancellation sound, a cancellation sound for canceling air column resonance sound caused by air column resonance in the space between the grooves of the vehicle's tires and the road surface with which the tires are in contact, based on the contact shape of the tire at the road surface, and outputs the generated cancellation sound to the speaker.

3. The information processing device according to claim 2, wherein the control unit acquires audio information of noise generated while the vehicle is traveling and detected by a microphone, analyzes the audio information to acquire air column resonance sound generated due to air column resonance in the space between the groove portion of the tire and the road surface with which the tire is in contact, and generates an audio for canceling the acquired air column resonance sound as the cancellation sound.

4. The information processing device according to claim 2, wherein the control unit determines a frequency based on a contact length of the tire over which the tire is in contact with the road surface, and generates the cancellation sound including the determined frequency.

5. The information processing device according to claim 4, wherein the control unit obtains the contact length of the tire in contact with the road surface based on at least one of the weight of the vehicle and the internal pressure of the tire, and determines the frequency based on the obtained contact length.

6. The information processing device according to claim 4, wherein the control unit inputs the acquired contact length into a trained model trained with the contact length of the tire in contact with the road surface as an explanatory variable and the peak frequency of the air column resonance sound of the tire as an objective variable, and determines the frequency.

7. The information processing device according to claim 4, wherein the control unit further determines the frequency based on the shape of a tread pattern of the tire.

8. The information processing device according to claim 2, wherein the control unit acquires audio information of an observed sound generated while the vehicle is traveling and detected by a microphone, and corrects the cancellation sound based on the audio information.

9. The information processing device according to claim 1, wherein the control unit estimates cavity resonance noise based on the tire characteristic information, generates a cancellation sound for the cavity resonance noise, and emits the cancellation sound from the speaker.

10. The information processing device according to claim 9, wherein the control unit corrects the cavity resonance noise based on vehicle speed information, and generates a cancellation sound for the corrected cavity resonance noise.

11. The information processing device according to claim 10, wherein the control unit corrects the cancellation sound based on an actual measured sound, and emits the corrected cancellation sound from the speaker.

12. The information processing device according to claim 11, wherein the actual sound is measured by a microphone or an acceleration sensor.

13. The information processing device according to claim 12, wherein the tire characteristic information includes a tire outer diameter and a rim diameter.

14. The information processing device according to claim 13, wherein the control unit corrects the cancellation sound based on an observation sound of an evaluation microphone, and emits the corrected cancellation sound from the speaker.

15. The information processing device according to claim 14, wherein the control unit trains a learning model based on the tire characteristic information, the vehicle speed information and the measured sound, and inputs the tire characteristic information and the vehicle speed information into the learning model to estimate the cavity resonance sound.

16. The information processing device according to claim 1, wherein the control unit estimates pattern noise based on the tire characteristic information and vehicle speed information, generates a cancellation sound for the pattern noise, and emits the cancellation sound from the speaker.

17. The information processing device according to claim 16, wherein the tire characteristic information includes a tire circumference and a number of pitches per circumference.

18. The information processing device according to claim 17, wherein the control unit corrects the cancellation sound based on an actual measurement sound, and emits the corrected cancellation sound from the speaker.

19. The information processing device according to claim 18, wherein the actual sound is measured by a microphone or an acceleration sensor.

20. The information processing device according to claim 19, wherein the pattern noise is a pattern excitation sound.

21. The information processing device according to claim 20, wherein the control unit corrects the cancellation sound based on an observation sound of an evaluation microphone, and emits the corrected cancellation sound from the speaker.

22. The information processing device of claim 1, wherein the control unit trains a learning model based on vehicle speed information and actual measured sound, inputs the vehicle speed information to the learning model to estimate pattern noise, generates a cancellation sound for the pattern noise, and emits the cancellation sound from the speaker.

23. A control method for an information processing device having a control unit, the control unit generating a cancellation sound for canceling noise caused by characteristics of a vehicle's tires based on characteristic information indicating the characteristics of the tires, and outputting the generated cancellation sound to a speaker.

24. A program for causing a computer to operate as an information processing device according to any one of claims 1 to 22.

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