JP7806663B2 - Sound source estimation device - Google Patents

Description

This disclosure relates to a sound source estimation device.

Sound source estimation devices that estimate the source of abnormal noise generated by a vehicle have been known for some time (see, for example, Patent Document 1).

Japanese Patent Application Laid-Open No. 2022-100163

In order to determine that the source of the abnormal noise is the propeller shaft, it is necessary to calculate the rotational frequency of the propeller shaft. However, with conventional technology, the calculation results can be subject to errors depending on the vehicle's drive type.

In view of the above issues, the goal is to improve the accuracy of estimating the source of abnormal noise.

In order to solve the above problem, according to one aspect of the present disclosure,
a rotational frequency calculation unit that determines whether a vehicle has a propeller shaft and, if the propeller shaft is present, calculates a rotational frequency of the propeller shaft according to the vehicle;
a sound source estimation unit that estimates whether the source of the abnormal noise is the propeller shaft based on the degree of coincidence between the rotation frequency of the propeller shaft when the abnormal noise occurs and the frequency of the abnormal noise that is actually occurring; and
There is provided a sound source estimation device comprising:

According to one aspect of the present disclosure, it is possible to improve the accuracy of estimating the source of abnormal noise.

1 is a diagram illustrating a configuration of a sound source estimation system including a sound source estimation device according to an embodiment. FIG. 10 is a diagram illustrating a procedure for estimating abnormal noise from a propeller shaft according to an embodiment. FIG. 10 is a diagram illustrating a procedure for estimating a sound source including components other than a propeller shaft according to an embodiment.

The following describes the embodiment with reference to the drawings.

(Configuration of sound source estimation system)
1 is a diagram showing the configuration of a sound source estimation system 1 having a sound source estimation device of an embodiment. The sound source estimation system 1 includes a vehicle 100, a mobile terminal 200, and a server 300 that functions as the sound source estimation device. Note that the mobile terminal 200 may function as the sound source estimation device instead of the server 300. In this case, the sound source estimation system 1 is configured by the vehicle 100 and the mobile terminal 200.

Vehicle 100 is, for example, one of an engine vehicle, a hybrid vehicle, or an electric vehicle. An engine vehicle is equipped with only an internal combustion engine (engine) as a prime mover, a hybrid vehicle is equipped with both an internal combustion engine and an electric motor as a prime mover, and an electric vehicle is equipped with only an electric motor as a prime mover. A hybrid vehicle is, for example, one of an HEV (Hybrid Electric Vehicle), a PHEV (Plug-in Hybrid Electric Vehicle), and a range extender EV. An electric vehicle is, for example, one of a BEV (Battery Electric Vehicle), a hydrogen battery vehicle, etc.

The vehicle 100 includes a vehicle-side control unit 101, a vehicle-side communication unit 102, a sensor 103, and an actuator 104.

The vehicle-side control unit 101 is, for example, an ECU (Electronic Control Unit). The ECU has a CPU (Central Processing Unit), ROM (Read Only Memory), RAM (Random Access Memory), etc. The ECU controls the entire vehicle 100 by loading various programs stored in the ROM into the RAM and executing the various programs on the CPU. The vehicle-side control unit 101 may be composed of multiple ECUs connected to each other so that they can communicate via a CAN (Controller Area Network).

The vehicle-side communication unit 102 is a communication device that is communicatively connected to devices external to the vehicle 100. The vehicle-side communication unit 102 is, for example, a wireless communication module for OBD (On Board Diagnosis), and communicates wirelessly with the mobile terminal 200. The vehicle-side communication unit 102 transmits various vehicle information to the mobile terminal 200 in response to commands from the vehicle-side control unit 101. The vehicle information includes at least information related to the operating state of the vehicle 100 detected by the sensor 103. The information related to the operating state of the vehicle 100 includes at least vehicle speed information.

Sensor 103 is a vehicle condition sensor such as a vehicle speed sensor. The vehicle speed sensor detects the vehicle speed of vehicle 100 using wheel speed sensors provided on the wheels of vehicle 100. Sensor 103 transmits at least vehicle speed information to vehicle-side control unit 101.

The actuator 104 includes various components such as a prime mover, transmission, clutch, propeller shaft, differential gear, drive shaft, wheels, steering system, and brake system. The actuator 104 is controlled in accordance with commands from the vehicle-side control unit 101.

Parts installed in vehicle 100 generate operating noises when they rotate. If there is an abnormality in a part, vehicle 100 will generate an abnormal noise that is different from normal operating noises. If the user of vehicle 100 notices an abnormal noise, the user will take vehicle 100 to a dealer or repair shop. A worker at the dealer or repair shop uses sound source estimation software installed on mobile terminal 200 to execute a process to estimate the source of the abnormal noise from among multiple parts. Based on the abnormal noise source estimation result received from server 300, the worker at the dealer or repair shop checks whether there is an abnormality in the part estimated as the source of the abnormal noise, and repairs or replaces the part as necessary.

The sound source estimation software comprises a terminal-side program and a server-side program. When the mobile terminal 200 functions as a sound source estimation device, the sound source estimation software is composed of the terminal-side program.

The mobile terminal 200 includes a terminal control unit 201, a terminal communication unit 202, a microphone 211, an input unit 212, and an output unit 213.

The terminal-side control unit 201 is a computer having a CPU (Central Processing Unit), ROM (Read Only Memory), RAM (Random Access Memory), etc. The computer controls the entire mobile terminal 200 by loading various programs stored in the ROM into the RAM and executing the various programs on the CPU.

The terminal-side control unit 201 has a user interface unit 203 as a functional configuration. The user interface unit 203 is realized by a terminal-side program executed by a processor such as a CPU.

The user interface unit 203 issues commands to input various information required for sound source estimation via the microphone 211, the terminal-side communication unit 202, and the input unit 212. The user interface unit 203 also issues commands to output various information required for sound source estimation to the output unit 213. The user interface unit 203 also issues commands to send and receive various information required for sound source estimation to and from the server 300 via the terminal-side communication unit 202.

The terminal-side communication unit 202 is a communication device that is communicatively connected to an external device of the mobile terminal 200. The terminal-side communication unit 202 is, for example, a wireless communication module, and communicates wirelessly with the vehicle 100 using radio waves. The terminal-side communication unit 202 receives vehicle speed information of the vehicle 100 at the time of the abnormal noise from the vehicle-side communication unit 102 while the microphone 211 acquires audio information at the time of the abnormal noise.

The terminal-side communication unit 202 also communicates with the server 300 to send and receive various information necessary for sound source estimation. Communication between the terminal-side communication unit 202 and the server 300 is performed via a specified communication line. The specified communication line includes at least one of a one-to-one communication line and a network. The network includes a LAN (Local Area Network) or a WAN (Wide Area Network), and the WAN includes at least one of a mobile communication network, a satellite communication network, the Internet, etc. The terminal-side communication unit 202 may use different communication methods for communication with the vehicle 100 and for communication with the server 300.

The microphone 211 acquires audio information around the mobile terminal 200. The dealer or repair shop worker brings the mobile terminal 200 close to the location on the vehicle 100 where the abnormal noise is occurring. The microphone 211 acquires the audio information when the abnormal noise occurs and transmits it to the terminal control unit 201. The audio information indicates changes in sound pressure level over time. The terminal control unit 201 generates audio information that identifies the abnormal sound portion from the audio information. The abnormal sound portion is specified by the dealer or repair shop worker using the input unit 212 and the functions of the user interface unit 203.

The input unit 212 inputs various information necessary for sound source estimation and transmits it to the terminal-side control unit 201. For example, the input unit 212 may be an input device such as a touch panel, a voice recognition device, or a keyboard. The input unit 212 inputs at least information related to the drive type of the vehicle 100 as information necessary for sound source estimation. Information related to the drive type of the vehicle 100 is specified by a dealer or a repair shop worker using the input unit 212 through the functions of the user interface unit 203.

The output unit 213 outputs various information necessary for sound source estimation in response to commands from the terminal-side control unit 201. For example, the output unit 213 may be an output device such as a display device, speaker, or printer. The output unit 213 outputs, as information necessary for sound source estimation, at least audio information including abnormal noise from the vehicle 100 acquired by the microphone 211, and vehicle speed information of the vehicle 100 acquired simultaneously with the audio information.

The server 300 comprises a server-side control unit 301, a server-side communication unit 302, and a storage unit 306.

The server-side control unit 301 is, for example, a computer. The computer has a CPU (Central Processing Unit), ROM (Read Only Memory), RAM (Random Access Memory), SSD (Solid State Drive), etc. The computer controls the entire server 300 by loading various programs stored in the ROM and various information stored in the SSD into the RAM and executing the various programs with the CPU.

The server-side control unit 301 has, as its functional components, a frequency analysis unit 303, a rotational frequency calculation unit 304, and a sound source estimation unit 305. These functional units are realized by a server-side program executed by a processor such as a CPU. Some of these functional units may also be handled by an external device communicatively connected to the server 300.

The frequency analysis unit 303 analyzes the frequency components in the vehicle 100 audio information received from the mobile terminal 200 and generates audio frequency information that indicates changes in the frequency spectrum over time.

The rotational frequency calculation unit 304 calculates the rotational frequency Pf of the propeller shaft based on the information about the propeller shaft shown in the table below.

The information relating to the propeller shaft is stored in advance in storage unit 306. The information relating to the propeller shaft is associated with at least the type of vehicle 100 and the presence or absence of a propeller shaft corresponding to the type of vehicle 100. The information relating to the propeller shaft is also associated with information on tires synchronized with the propeller shaft, information on the gear ratio (final reduction ratio) between the propeller shaft and tires, and a formula for calculating the propeller shaft rotation frequency.

As can be seen from the formula for calculating the propeller shaft rotational frequency, the propeller shaft rotational frequency Pf [rps] is calculated by multiplying the tire rotation speed Tv [rps] synchronized with the propeller shaft by the final reduction ratio γ between the propeller shaft and the tire.

Furthermore, the tire rotation speed Tv [rps] synchronized with the propeller shaft can be calculated using the following formula (1) based on the vehicle speed V [km/h] of the vehicle 100 and the tire radius r [mm].

The rotational frequency calculation unit 304 identifies the type of vehicle 100 based on information regarding the drive model of the vehicle 100 received from the mobile terminal 200. For example, the rotational frequency calculation unit 304 classifies the engine type of the vehicle 100 into one of FF (front engine front drive) vehicles, FR (front engine rear drive) vehicles, and EV (electric vehicle) vehicles. The rotational frequency calculation unit 304 also classifies the vehicle model type of the vehicle 100 into one of HEV vehicles and conventional vehicles (engine vehicles). The rotational frequency calculation unit 304 also classifies the drive system of the vehicle 100 into 2WD (two wheel drive) vehicles and 4WD (four wheel drive) vehicles.

The rotational frequency calculation unit 304 then determines whether or not a propeller shaft is present based on the type of vehicle 100, and if it determines that a propeller shaft is not present, it sends information indicating this to the sound source estimation unit 305.

If the rotational frequency calculation unit 304 determines that a propeller shaft is present, it calculates the rotational frequency Pf [rps] of the propeller shaft by multiplying the rotational speed Tv [rps] of the tire synchronized with the propeller shaft by the final reduction ratio γ between the propeller shaft and the tire. The rotational frequency calculation unit 304 then sends the rotational frequency Pf [rps] of the propeller shaft to the sound source estimation unit 305.

When the sound source estimation unit 305 receives information indicating that a propeller shaft is not present, it estimates that the source of the abnormal noise is not the propeller shaft.

When the sound source estimation unit 305 receives the rotational frequency Pf [rps] of the propeller shaft, it estimates whether the source of the abnormal noise is the propeller shaft based on the degree of match between the rotational frequency Pf [rps] of the propeller shaft and the frequency [fps] of the abnormal noise in the audio frequency information.

For example, if the degree of match between the rotational frequency Pf [rps] of the propeller shaft and the frequency [fps] of the abnormal sound in the audio frequency information is equal to or greater than a predetermined threshold (e.g., 80%), the sound source estimation unit 305 estimates that the source of the abnormal sound is the propeller shaft. Also, if the degree of match between the rotational frequency Pf [rps] of the propeller shaft and the frequency [fps] of the abnormal sound in the audio frequency information is less than a predetermined threshold, the sound source estimation unit 305 estimates that the source of the abnormal sound is not the propeller shaft.

It is also preferable that the sound source estimation unit 305 outputs the degree of match between the rotational frequency Pf [rps] of the propeller shaft and the frequency [fps] of the abnormal sound in the audio frequency information over a predetermined period of time, i.e., the confidence [%] that the source of the abnormal sound is the propeller shaft. In this case, the sound source estimation unit 305 estimates that the source of the abnormal sound is the propeller shaft if the confidence [%] that the source of the abnormal sound is the propeller shaft is equal to or greater than a predetermined threshold (e.g., 70%).

The sound source estimation unit 305 instructs the server-side communication unit 302 to send the estimation result indicating whether the source of the abnormal noise is the propeller shaft to the mobile device 200.

The server-side communication unit 302 is a communication device that is communicatively connected to devices external to the server 300. The server-side communication unit 302 communicates with the mobile terminal 200 to send and receive various information necessary for sound source estimation to and from the mobile terminal 200. Communication between the server-side communication unit 302 and the mobile terminal 200 is carried out via a specified communication line.

Storage unit 306 is a storage device such as an SSD. Storage unit 306 stores various programs and information. Storage unit 306 also stores at least the information related to the propeller shaft shown in Table 1. Storage unit 306 may be included in an external device communicatively connected to server 300.

(Propeller shaft abnormal noise estimation procedure)
An example of the abnormal noise estimation procedure for a propeller shaft will be described below. Fig. 2 is a diagram showing the abnormal noise estimation procedure for a propeller shaft according to this embodiment. The abnormal noise estimation procedure for a propeller shaft shown in Fig. 2 is executed by a server 300 functioning as a sound source estimation device, but may also be executed by a mobile terminal 200 functioning as a sound source estimation device.

<Step S10>
The server 300 classifies the engine type of the vehicle 100 into one of FF vehicles, FR vehicles, etc., using the rotational frequency calculation unit 304 based on information about the drive type of the vehicle 100.

<Step S11>
If the vehicle 100 is a front-wheel drive vehicle, the server 300 classifies the vehicle type of the vehicle 100 into one of an HV (hybrid vehicle) or a gasoline (engine) vehicle, etc., based on information regarding the drive type of the vehicle 100 using the rotational frequency calculation unit 304.

<Step S12>
If the vehicle 100 is a FF vehicle and a hybrid vehicle, the server 300 determines by the rotation frequency calculation unit 304 that the vehicle 100 does not have a propeller shaft.

<Step S13>
Server 300 estimates that the source of the abnormal noise is not the propeller shaft using sound source estimation unit 305. Server 300 also transmits the estimation result that the source of the abnormal noise is not the propeller shaft to mobile terminal 200 using server-side communication unit 302.

<Step S14>
If the vehicle 100 is a front-wheel drive vehicle and a conventional (engine) vehicle, the server 300 classifies the drive system of the vehicle 100 into one of a 2WD vehicle, a 4WD vehicle, etc., based on information about the drive type of the vehicle 100 using the rotational frequency calculation unit 304.

<Step S12>
If the vehicle 100 is a FF vehicle, an engine vehicle, and a 2WD vehicle, the server 300 determines by the rotation frequency calculation unit 304 that the vehicle 100 does not have a propeller shaft.

<Step S13>
Server 300 estimates that the source of the abnormal noise is not the propeller shaft using sound source estimation unit 305. Server 300 also transmits the estimation result that the source of the abnormal noise is not the propeller shaft to mobile terminal 200 using server-side communication unit 302.

<Step S15>
If the vehicle 100 is an FR vehicle, the server 300 calculates the propeller shaft rotation frequency Pf (rps) based on the rotation speed Tv (rps) of the rear tire synchronized with the propeller shaft using the rotation frequency calculation unit 304. The rotation speed Tv (rps) of the rear tire synchronized with the propeller shaft is calculated from equation (1) based on the vehicle speed V (km/h) of the vehicle 100 and the radius (mm) of the rear tire.

<Step S16>
If the vehicle 100 is a FF, internal combustion, and 4WD vehicle, the server 300 calculates the propeller shaft rotation frequency Pf (rps) based on the rotation speed Tv (rps) of the front tire synchronized with the propeller shaft using the rotation frequency calculation unit 304. The rotation speed Tv (rps) of the front tire synchronized with the propeller shaft is calculated from equation (1) based on the vehicle speed V (km/h) of the vehicle 100 and the radius (mm) of the front tire.

<Step S17>
The server 300 determines the degree of agreement between the propeller shaft rotation frequency Pf [rps] (rotation primary frequency) and the frequency [fps] of the abnormal noise in the audio frequency information using the sound source estimation unit 305. The propeller shaft rotation primary frequency roughly coincides with the muffled noise (primary) of the propeller shaft when the amplitude at the half point of the propeller shaft is larger than that at other points.

<Step S18>
If the degree of match between the propeller shaft rotation frequency (primary rotation frequency) and the frequency of the abnormal noise is equal to or greater than a predetermined threshold (YES in step S17), the server 300 estimates that the source of the abnormal noise is a muffled noise (primary) of the propeller shaft using the sound source estimation unit 305. The server 300 also transmits the estimation result that the source of the abnormal noise is the propeller shaft to the mobile device 200 using the server-side communication unit 302.

<Step S19>
If the degree of match between the propeller shaft rotation frequency and the frequency of the abnormal noise is less than the predetermined threshold (NO in step S17), the server 300 determines the degree of match between the propeller shaft rotation frequency (rotational secondary frequency) and the frequency of the abnormal noise using the sound source estimation unit 305. The propeller shaft rotational secondary frequency is twice the propeller shaft's primary rotational frequency. The propeller shaft rotational secondary frequency roughly matches the propeller shaft muffled noise (secondary) when the amplitudes at the 1/4 and 3/4 points of the propeller shaft are larger than those at other points.

<Step S20>
If the degree of match between the propeller shaft rotation frequency (secondary rotation frequency) and the frequency of the abnormal noise is equal to or greater than a predetermined threshold (YES in step S19), the server 300 estimates that the source of the abnormal noise is a muffled noise (secondary) of the propeller shaft using the sound source estimation unit 305. The server 300 also transmits the estimation result that the source of the abnormal noise is the propeller shaft to the mobile device 200 using the server-side communication unit 302.

<Step S13>
If the degree of match between the propeller shaft rotation frequency (rotational secondary frequency) and the frequency of the abnormal noise is less than a predetermined threshold (NO in step S19), server 300 estimates that the source of the abnormal noise is not the propeller shaft using sound source estimation unit 305. Server 300 also transmits the estimation result that the source of the abnormal noise is not the propeller shaft to mobile device 200 using server-side communication unit 302.

(Sound source estimation procedure including other components)
An example of a sound source estimation procedure including a part other than a propeller shaft will be described below. Fig. 3 is a diagram showing a sound source estimation procedure including a part other than a propeller shaft according to this embodiment. The sound source estimation procedure shown in Fig. 3 is executed by a server 300 functioning as a sound source estimation device, but may also be executed by a mobile terminal 200 functioning as a sound source estimation device.

<Step S21>
The server 300 classifies the diagnosis targets of the vehicle 100 into either engine or drive related parts or brake system related parts, using the server-side control unit 301 .

<Step S22>
When the vehicle diagnosis target is a part related to the engine or drivetrain, the server 300 uses the sound source estimation unit 305 to perform rule discrimination that compares the vehicle information and audio frequency information with technical knowledge (conditions for the occurrence of abnormal noise) about 47 types of parts related to the engine or drivetrain.

Note that the propeller shaft is one of the 47 types of parts, and the abnormal noise estimation procedure for the propeller shaft shown in Figure 2 is one process in step S22.

<Step S23>
The server 300 uses the sound source estimation unit 305 to determine whether the vehicle information and the audio frequency information match the rules (conditions for the occurrence of abnormal noise).

<Step S24>
If the vehicle information and the audio frequency information correspond to the rules related to the 47 types of parts (YES in step S23), the server 300 causes the sound source estimation unit 305 to extract the top three parts with the highest certainty from the 47 types of parts related to the engine or drive.

<Step S25>
If the vehicle information and audio frequency information do not match the rules (conditions for abnormal noise occurrence) related to the 47 types of parts (YES in step S23), the server 300 determines, using the sound source estimation unit 305, whether abnormal noise is occurring during idling.

<Step S26>
If abnormal noise is occurring during idling (YES in step S25), the server 300 uses the sound source estimation unit 305 to perform AI (artificial intelligence) discrimination on five types of engine or drive-related parts that generate abnormal noise during idling.

The sound source estimation unit 305 estimates the source of the abnormal noise using a trained learning model whose input is the state quantity of the frequency [fps] of the abnormal noise and whose output is the confidence [%] of five types of components. The learning model is a neural network, but other learning models such as decision trees (regression trees) and logistic regression may also be used. The learning method used includes supervised learning and semi-supervised learning.

The sound source estimation unit 305 generates a learning model using training data including, for example, the state quantity of the frequency [fps] of the abnormal noise and label data (data in which the certainty of the part that actually produces the abnormal noise out of five types of parts is set to 100%, and the certainty of the remaining parts is set to 0%). In other words, the sound source estimation unit 305 updates the parameters of the learning model (such as the weights of the neural network) so as to minimize the error in the certainty [%] of the five types of parts. The neural network weights can be updated using methods such as backpropagation.

<Step S27>
The sound source estimation unit 305 of the server 300 extracts the top three parts with the highest degree of certainty from five types of parts related to the engine or drive that generate abnormal noise during idling.

<Step S28>
If no abnormal noise occurs during idling (NO in step S25), the server 300 uses the sound source estimation unit 305 to perform AI discrimination on 13 types of parts related to the engine or drive that generate abnormal noise during driving.

The sound source estimation unit 305 estimates the source of the abnormal noise using a trained learning model whose input is the state quantity of the frequency [fps] of the abnormal noise and whose output is the confidence [%] of 13 types of components. The learning model is a neural network, but other learning models such as decision trees (regression trees) and logistic regression may also be used. The learning method used includes supervised learning and semi-supervised learning.

The sound source estimation unit 305 generates a learning model using training data including, for example, the state quantity of the frequency [fps] of the abnormal noise and label data (data in which the certainty of the part that actually produces the abnormal noise out of the 13 types of parts is set to 100%, and the certainty of the remaining parts is set to 0%). In other words, the sound source estimation unit 305 updates the parameters of the learning model (such as the weights of the neural network) so as to minimize the error in the certainty [%] of the 13 types of parts.

<Step S29>
The sound source estimation unit 305 of the server 300 extracts the top three parts with the highest degree of certainty from among 13 types of parts related to the engine or drive that generate abnormal noise during driving.

<Step S30>
If the server 300 is unable to extract the top three parts with the highest confidence levels from among the parts related to the engine or driveline, it transmits information related to the diagnostic guide to the mobile terminal 200. The mobile terminal 200 outputs the diagnostic guide to the output unit 213, such as a display device, via the user interface unit 203. A dealer or a worker at a repair shop manually identifies the source of the abnormal noise while looking at the flow of the output diagnostic guide.

<Step S31>
When the vehicle diagnosis target is a part related to the brake system, the server 300 uses the sound source estimation unit 305 to perform rule discrimination that compares the vehicle information and audio frequency information with technical knowledge (conditions for the occurrence of abnormal noise) regarding nine types of parts related to the brake system.

<Step S32>
The server 300 further compares the vehicle information and the voice frequency information with the questionnaire items previously posed to the dealer or the worker at the repair shop by the sound source estimation unit 305 .

<Step S33>
The server 300 uses the sound source estimation unit 305 to extract the top three parts with the highest certainty from among the nine types of parts related to the brake system.

The server 300 then transmits the top three parts with the highest confidence levels to the mobile device 200 via the server-side communication unit 302, and ends the sound source estimation of the abnormal noise.

(Effects of this embodiment)
From the above, server 300 determines whether vehicle 100 has a propeller shaft, and if a propeller shaft is present, calculates the rotational frequency of the propeller shaft appropriate for vehicle 100. Server 300 then estimates whether the source of the abnormal noise is the propeller shaft based on the degree of match between the rotational frequency of the propeller shaft when the abnormal noise occurs and the frequency of the abnormal noise that is actually occurring. This makes it possible to improve the accuracy of estimating the source of the abnormal noise.

The various functions described in the above embodiments can be realized by one or more processing circuits. In addition to a CPU, processing circuits include ASICs (Application Specific Integrated Circuits) and FPGAs (Field Programmable Gate Arrays) that execute various functions.

Although the preferred embodiment has been described in detail above, the present invention is not limited to the above-described embodiment, and various modifications and substitutions can be made to the above-described embodiment without departing from the scope of the claims.

100 Vehicle 200 Mobile terminal 300 Server (an example of a sound source estimation device)
304 Rotational frequency calculation unit 305 Sound source estimation unit

Claims (1)

a rotational frequency calculation unit that calculates the rotational frequency of the propeller shaft based on the rotational speed of the rear tires when the vehicle is an FR vehicle, and calculates the rotational frequency of the propeller shaft based on the rotational speed of the front tires when the vehicle is an FF, conventional, and 4WD vehicle ;
a sound source estimation unit that estimates whether the source of the abnormal noise is the propeller shaft based on the degree of coincidence between the rotation frequency of the propeller shaft when the abnormal noise occurs and the frequency of the abnormal noise that is actually occurring; and
A sound source estimation device comprising: