Sound effect enhancement method, vehicle machine and program product

Abstract

The invention discloses a sound effect enhancement method, a vehicle machine and a program product, and relates to the technical field of intelligent cabins, and the method comprises the steps: collecting vehicle condition information and road condition information in a driving process in real time; executing current scene research and judgment based on the vehicle condition information and the road condition information; and applying different enhanced sound effects based on a current scene research and judgment result. According to the sound effect enhancement method, the vehicle machine and the program product provided by the invention, the sound effect played in the vehicle can be closely associated with the actual scene of the vehicle.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent cockpit, in particular to a sound effect enhancement method, a car machine and a program product. BACKGROUND

[0002] In the process of driving, playing music in the cockpit is the only choice for many drivers in real life. Studies have shown that listening to music during driving has considerable benefits for the physical and mental health of drivers and passengers.

[0003] However, in the actual driving process, the playing of music is usually unrelated to the driving process. In extreme cases, the outside of the car is infinite poetry and a distant place, and if the passengers in the car choose to play sad music, the passengers in the car can only do so. SUMMARY

[0004] The purpose of the present application is to provide a sound effect enhancement method, a car machine and a program product, which can closely associate the sound effect played in the car with the actual scene of the vehicle.

[0005] The present application provides the following solutions:

[0006] According to one aspect of the present application, a sound effect enhancement method is provided, which comprises:

[0007] collecting vehicle condition information and road condition information in real time during driving;

[0008] based on the vehicle condition information and the road condition information, performing current scene judgment;

[0009] based on the result of the current scene judgment, applying different enhanced sound effects.

[0010] Optionally, collecting vehicle condition information and road condition information in real time during driving comprises:

[0011] collecting vehicle speed information in real time;

[0012] collecting current geographic location information in real time;

[0013] collecting video image information outside the current vehicle in real time.

[0014] Optionally, based on the vehicle condition information and the road condition information, performing current scene judgment comprises:

[0015] clustering historical vehicle condition information and road condition information;

[0016] based on the clustering result, setting a plurality of scene categories;

[0017] based on the set scene categories, performing current scene judgment.

[0018] Optionally, based on the vehicle condition information and the road condition information, a current scene judgment is performed, including:

[0019] An SVM for scene classification is established;

[0020] Based on the established SVM, a current scene judgment is performed.

[0021] Optionally, based on the result of the current scene judgment, different enhanced sound effects are applied, including:

[0022] When the vehicle passes through a sea-crossing bridge, sea wave sound is mixed in the playing music.

[0023] Optionally, based on the result of the current scene judgment, different enhanced sound effects are applied, including:

[0024] When the vehicle enters a tunnel, an echo effect is added.

[0025] Optionally, based on the result of the current scene judgment, different enhanced sound effects are applied, including:

[0026] When the vehicle is in high-speed cruising, an anti-fatigue sound effect is started.

[0027] According to the two aspects of the present application, a sound effect enhancement device is provided, which includes:

[0028] A collection module is configured to collect vehicle condition information and road condition information in real time during driving;

[0029] A judgment module is configured to perform a current scene judgment based on the vehicle condition information and the road condition information;

[0030] An application module is configured to apply different enhanced sound effects based on the result of the current scene judgment.

[0031] According to the three aspects of the present application, a car machine is provided, which includes a processor and a memory, and the memory stores a computer program, which is loaded and executed by the processor to implement the sound effect enhancement method as described above.

[0032] According to the four aspects of the present application, a computer program product is provided, including a computer program, characterized in that the computer program is executed by a processor to implement the sound effect enhancement method as described above.

[0033] Through the above scheme, the following beneficial technical effects are obtained:

[0034] The sound effect enhancement method can change the music sound effect played in the cabin in real time according to the changes of the scene outside the vehicle, and automatically match the scene throughout the process without manual operation by the driver and passenger in the cabin, so that the scene matching process is not felt by the driver and passenger in the cabin. Attached Figure Description

[0035] Figure 1 This is a flowchart of a sound enhancement method provided in one or more embodiments of the present invention;

[0036] Figure 2 This is a flowchart of the acquisition operation in the sound enhancement method provided in one or more embodiments of the present invention;

[0037] Figure 3 This is a flowchart of the judgment operation in the sound effect enhancement method provided in one or more embodiments of the present invention;

[0038] Figure 4 This is a flowchart of the judgment operation in the sound effect enhancement method provided in one or more embodiments of the present invention;

[0039] Figure 5 This is a flowchart of a sound enhancement method provided in one or more embodiments of the present invention;

[0040] Figure 6 This is a flowchart of a sound enhancement method provided in one or more embodiments of the present invention;

[0041] Figure 7 This is a flowchart of a sound enhancement method provided in one or more embodiments of the present invention;

[0042] Figure 8 This is a structural diagram of the sound enhancement device provided in one or more embodiments of the present invention;

[0043] Figure 9 This is a structural diagram of the vehicle system provided in one or more embodiments of the present invention. Detailed Implementation

[0044] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] Figure 1 This illustrates the specific execution process of the sound enhancement method provided by one or more embodiments of the present invention. See also Figure 1 The sound enhancement method includes the following steps:

[0046] S11 collects vehicle and road condition information in real time during driving.

[0047] S12 performs current scenario analysis based on vehicle and road condition information.

[0048] S13, based on the result of the current scene, apply different enhanced sound effects.

[0049] With the further evolution of the current intelligent cockpit technology, playing music during driving has become a very common choice for drivers and passengers.

[0050] In most current cockpits, what kind of music the driver and passenger hear, or the sound quality they listen to, has nothing to do with the specific driving scene. That is, what music is listened to in the cockpit is entirely the result of the driver and passenger's autonomous choice, and has nothing to do with the current driving scene outside the vehicle.

[0051] The technical solution given in this embodiment attempts to establish a link between the scene outside the cockpit and the listening sound effect inside the cockpit. That is, according to the specific scene of the current driving outside the cockpit, it is determined how to enhance the music being played inside the cockpit.

[0052] For example, the vehicle is driving in the desert next door, and the sound of cold wind can be added to the music being played in the cockpit. For example, the vehicle is driving by the sea, and from the window you can see an endless, blue sponge. At this time, the sound of the sea can be added to the music being played in the cockpit.

[0053] In general, the core of the technical solution provided in this embodiment is to determine how to enhance the sound effect played in the cockpit according to the actual driving scene of the vehicle.

[0054] So, to enhance the sound effect played in the cockpit, first of all, it is necessary to determine what kind of scene the vehicle is currently in.

[0055] To determine the current scene of the vehicle, it should be clear about the vehicle condition information and road condition information during driving.

[0056] Vehicle condition information refers to the current state of the vehicle. How fast is the vehicle driving, where is the vehicle located, is the vehicle using electric power or fuel during driving, or is it a hybrid, etc.

[0057] Road condition information is the specific situation of the vehicle's driving route. Is there a dense crowd of pedestrians or vehicles around the vehicle, how far is the vehicle from the surrounding vehicles, what is the average driving speed of the road, etc.

[0058] The data source of the vehicle condition information is mostly the vehicle's own measurement equipment. For example, the vehicle speed parameter can be measured by the vehicle's own speed sensor.

[0059] Road condition information is mostly obtained through the vehicle's V2X network.

[0060] For example, the road condition outside the cabin of the vehicle can be determined by first determining the current location of the vehicle through the positioning device provided in the vehicle, and then searching for the road condition information according to the obtained positioning information through the V2X network, so as to obtain the road condition outside the cabin of the vehicle.

[0061] After obtaining the vehicle condition information and the road condition information, the current scene of the vehicle is judged according to the obtained vehicle condition information and the road condition information.

[0062] In some typical application scenarios, the possible values of the judgment result of the current scene are usually limited. That is, the possible values of the current scene are limited in number. Then, the judgment of the current scene can be regarded as classification among the limited possible judgment results. The basis of classification is the obtained vehicle condition information and road condition information. The judgment of the current scene is to classify the current scene of the vehicle based on the obtained vehicle condition information and road condition information, and the result of classification is the current scene of the vehicle.

[0063] After the judgment of the current scene is completed, the sound effect corresponding to the current scene can be applied in the cabin based on the judgment result of the scene.

[0064] Figure 2 A flowchart of the acquisition operation in the sound effect enhancement method provided by one or more embodiments of the present application is shown. Referring to Figure 2 , the operation steps of real-time acquisition of vehicle condition information and road condition information include the following steps:

[0065] S21, real-time acquisition of vehicle speed information.

[0066] S22, real-time acquisition of current geographical position information.

[0067] S23, real-time acquisition of current video image information outside the vehicle.

[0068] Generally, the data source for acquisition of vehicle condition information and road condition information can be the sensors provided on the vehicle itself, or can be obtained from external data sources through the V2X network.

[0069] According to the nature of the data itself, the vehicle condition information is mostly obtained from the sensors provided on the vehicle itself. For example, the vehicle speed information can be obtained by installing a rotation sensor on the tire of the vehicle, reading the rotation speed of the tire of the vehicle per unit time through the rotation sensor, and then calculating the current vehicle speed according to the outer diameter of the tire of the vehicle.

[0070] The geographical position information of the vehicle is an example of the most typical data type that needs to be acquired from an external data source. Usually, the vehicle acquires its geographical position information from a GNSS network.

[0071] The acquired geographical position information plays a very important role in the scene judgment process. Because, from the current location of the vehicle, it can be inferred that the vehicle is in what kind of environment, is it next to the desert, or is it surrounded by trees, or is it in the city center.

[0072] Although the geographical position information can complete the inference of the scene where the vehicle is located, there is a weakness in the scene inference based on the geographical position information alone, that is, such inference is usually inaccurate. The same geographical position may be a bustling city at certain times, and may be deserted at other times.

[0073] In order to accurately determine the scene where the vehicle is located, a video image collector can be installed on the vehicle to collect real-time cabin-out images of the vehicle passing through the location.

[0074] In the scene inference process, through the geographical position information of the vehicle, it is inferred what the possible scene is, and further through the analysis of the real-time collected video images, the specific scene where the vehicle is located is accurately positioned, which can ensure that the inferred scene is correct, and also makes various collected data of the vehicle can be applied in practice.

[0075] Figure 3 The execution process of the judgment operation in the sound effect enhancement method provided by one or more embodiments of the present application is shown. Referring to Figure 3 , based on the vehicle condition information and the road condition information, the current scene judgment includes the following operation steps:

[0076] S31, clustering the historical vehicle condition information and the road condition information.

[0077] S32, based on the clustering result, setting a plurality of scene categories.

[0078] S33, based on the set scene categories, performing current scene judgment.

[0079] As described in the foregoing embodiments of the present application, the judgment process of the scene is equivalent to the process of classifying the current scene based on the collected vehicle information and road condition information. Specifically, the classification result corresponds to different scenes where the vehicle is located. That is, the input data of the classification algorithm is the collected vehicle condition information and road condition information, and the output data of the classification algorithm is the judgment result of different scenes obtained by classification, which completes the judgment process of the current scene.

[0080] In this embodiment, the vehicle scene classification is completed by clustering the vehicle condition information and the road condition information, that is, the scene judgment operation. More specifically, the k-means algorithm can be used to cluster the vehicle condition information and the road condition information.

[0081] Since the data amount of the video image itself is very large, if the originally collected video image data directly participates in the k-means operation, the calculation amount will be difficult to control. In order to solve this problem, a preprocessor can be provided for the video image to perform a step of feature extraction operation on the video image. Then the features extracted from the video image are input into the actual execution process of the k-means algorithm. Such a process design can greatly reduce the calculation amount of the k-means algorithm itself.

[0082] The preprocessor for pre-processing the video image preferentially selects the CNN algorithm model. Due to the characteristics of the convolution algorithm, it is very suitable for processing large-scale sequence data such as video images distributed in time and space. The CNN model is very suitable for feature extraction of video image data.

[0083] Since the k-means algorithm is a clustering algorithm, the actual clustering result cannot be predicted before the algorithm is run. Therefore, after the execution process of the adopted k-means algorithm is completed, the clustering result needs to be further defined so that each clustering result can match the actual application scene and correspond to the finally added sound effect.

[0084] Figure 4 The execution process of the scene judgment operation in the sound effect enhancement method provided by one or more embodiments of the present application is shown. Referring to Figure 4 , based on the vehicle condition information and the road condition information, the current scene judgment includes the following operation steps:

[0085] S41, establishing an SVM for scene classification.

[0086] S42, performing current scene judgment based on the established SVM.

[0087] Different from the foregoing embodiments of the present application, the current scene judgment in this embodiment is no longer performed by clustering, but by a classification algorithm.

[0088] The advantage of using a classification algorithm is that all collected vehicle condition information and road condition information will finally be classified into which scene type, which can be predicted before the scene judgment is run.

[0089] For example, there are 50,000 different vehicle condition information and road condition information samples waiting to be classified, that is, the current scene is judged. If the clustering method is used to judge the current scene, before running the clustering algorithm, it is impossible to know in advance how many scenes the 50,000 different samples will be finally divided into. Only after the clustering algorithm is run, can it be known how many possible scenes will be generated in the middle.

[0090] And using the classification algorithm to perform the above scene recognition, before running the above classification algorithm, the technical personnel have an expectation of how many scenes the vehicle is in.

[0091] For example, before running the classification algorithm, the technical personnel can consider that the vehicle is not fast and is in the suburbs, which belongs to a scene. While the vehicle is driving at high speed in the city, it is another scene. After running the classification algorithm, different data samples will be classified into different categories according to the characteristics of the data itself, that is, recognized as different scenes.

[0092] SVM is such a classification algorithm. SVM algorithm completes the classification of original input data by establishing a hyperplane in data space. For example, if all the original samples with vehicle condition information and road condition information need to be divided into 6 different scenes, then SVM needs to establish 5 different hyperplanes in the sample space.

[0093] The same as the foregoing embodiment of the present application is that in order to reduce the calculation amount of applying video data to the classifier, the video data can also be preprocessed. That is, for the video data, feature extraction is performed first, and then the extracted features are input into the classifier together with other types of data to run the classification calculation.

[0094] The model for preprocessing the video data is the CNN model.

[0095] As can be imagined, after the preprocessing of the CNN model, the amount of data from the video data actually input into the SVM model is actually greatly reduced, only the features extracted from the video data are input. Using these feature data for actual SVM classification not only no longer needs to process a large amount of irrelevant data, but also can still guarantee the effectiveness of classification and realize accurate identification of the scene.

[0096] Figure 5 The execution process of the sound effect enhancement method provided by one or more embodiments of the present application is shown. Referring to Figure 5 , the sound effect enhancement method includes the following operation steps:

[0097] S51, real-time collection of vehicle condition information and road condition information in the driving process.

[0098] S52, scene judgment based on the vehicle condition information and the road condition information.

[0099] S53, when the vehicle passes through the sea-crossing bridge, the sea wave sound is mixed in the playing music.

[0100] The embodiment gives a specific example of adding sound effects.

[0101] In the technical solution given in the embodiment, through comprehensive judgment on the collected vehicle condition information and road condition information, the scene where the vehicle is currently located is that the vehicle is passing through a sea-crossing bridge. Outside the vehicle cabin, there are vast waves of Hangzhou Bay or Hong Kong-Zhuhai-Macao.

[0102] At this time, the sea wave sound is added to the music played in the cabin. The driving personnel are outside the window, and the sea surface is endless, and the music played in the cabin often sounds the sound of the sea wave, and the mind is clear and refreshing.

[0103] The technical solution of adding sound effects given in the embodiment is to add specific sound effects to the music played in the cabin according to the geographical environment where the vehicle is located. In fact, there can be many similar sound effect adding scenes, such as adding the sound effect of the Marseillaise when the vehicle passes through the Louvre Square, adding the sound effect of the star-spangled flag when the vehicle passes through Capitol Hill, adding the sound effect of I and you when the vehicle passes through the Bird's Nest, adding the sound effect of Argentina, don't cry for me when the vehicle passes through May Square, and so on.

[0104] Figure 6 The execution process of the sound effect enhancement method provided by one or more embodiments of the application is shown. Referring to Figure 6 , the sound effect enhancement method includes the following operation steps:

[0105] S61, real-time collection of vehicle condition information and road condition information in the driving process.

[0106] S62, scene judgment based on the vehicle condition information and the road condition information.

[0107] S63, when the vehicle enters the tunnel, the echo effect is added.

[0108] The embodiment gives another specific example of adding sound effects.

[0109] In the technical solution of the embodiment, based on the comprehensive judgment on the collected vehicle condition information and road condition information, the judgment result is that the vehicle is passing through a submarine tunnel.

[0110] Based on this judgment result, the echo sound effect is added in the music played in the vehicle cabin. For example, in the chorus part of the song, the key lyrics or background music are delayed and then superimposed with the original music to form the echo effect.

[0111] The echo sound effect played in the cabin can make the music sound empty and spiritual, making the song more pleasant.

[0112] The technical solution of the sound effect addition given in this embodiment is also a technical solution of sound effect addition based on the scene outside the vehicle. Similar technical solutions can also be used, such as adding Han Tang ancient music when the vehicle passes through the Crescent Lake, adding Dongfanghong sound effect when the vehicle passes through the Jiuquan Satellite Launch Center, and so on.

[0113] Figure 7 The execution process of the sound effect enhancement method provided by one or more embodiments of the application is shown. Referring to Figure 7 , the sound effect enhancement method includes the following operation steps:

[0114] S71, real-time collection of vehicle condition information and road condition information in the driving process.

[0115] S72, based on the vehicle condition information and the road condition information, performing current scene judgment.

[0116] S73, when the vehicle is in high-speed cruising, starting the anti-fatigue sound effect.

[0117] This embodiment is still a specific case of sound effect addition. However, the technical solution given in this embodiment is no longer a technical solution of determining the sound effect addition content based on the scene detected outside the vehicle.

[0118] During high-speed cruising of the vehicle, the external environment of the vehicle will not change for a long time, and the driver and passenger in the cabin are prone to drowsiness.

[0119] In order to prevent the driver and passenger in the cabin from being drowsy and causing unnecessary traffic accidents, an anti-fatigue sound effect is added to the music played in the cabin in the high-speed cruising scene.

[0120] For example, the original drum sound in the played music can be emphasized, or drum sounds matching the rhythm of the song can be added to play an anti-fatigue role.

[0121] The purpose of adding the anti-fatigue sound effect is to avoid the drowsiness of the driver and passenger in the cabin and to avoid unnecessary traffic accidents.

[0122] Figure 8 is a structural diagram of the sound effect enhancement device provided by one or more embodiments of the application. Referring to Figure 8 , the sound effect enhancement device includes:

[0123] The acquisition module 81 is configured to acquire the vehicle condition information and the road condition information in real time.

[0124] The judgment module 82 is configured to perform current scene judgment based on the vehicle condition information and the road condition information.

[0125] The application module 83 is configured to apply different enhanced sound effects based on the result of the current scene judgment.

[0126] It is worth noting that, although only some basic functional modules are disclosed in the embodiments of the present application, it does not mean that the composition of the system is limited to the above-mentioned basic functional modules. On the contrary, the meaning expressed by the embodiments is that one or more functional modules can be added to the above-mentioned basic functional modules by those skilled in the art in combination with the prior art to form infinite embodiments or technical solutions. That is to say, the system is open rather than closed, and the protection scope of the claims of the present application cannot be limited to the disclosed basic functional modules. At the same time, for the convenience of description, the above device is described as various units and modules. Of course, the functions of the units and modules can be realized in the same software and / or hardware in the implementation of the present application.

[0127] As shown in FIG. 1, Figure 9 The present application also provides a car machine, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus; the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the sound effect enhancement method.

[0128] Figure 9 is a structural schematic diagram of a car machine provided by the embodiments of the present application. As shown in the structure of FIG. 1, Figure 9 The car machine provided in the embodiments of the present application comprises one or more processors 910 and memories 920; the processor 910 in the car machine can be one or more, Figure 9 for example, one processor 910; the memory 920 is configured to store one or more programs; the one or more programs are executed by the one or more processors 910, so that the one or more processors 910 implement the sound effect enhancement method according to any one of the embodiments of the present application.

[0129] The car machine can also comprise an input device 930 and an output device 940.

[0130] The processor 910, the memory 920, the input device 930 and the output device 940 in the car machine can be connected through a bus or other means, Figure 9 for example, through a bus.

[0131] The memory 920 in the car machine as a kind of computer readable storage medium, it can be used to store one or more programs, the program can be software program, computer executable program and module, such as the program instruction / module corresponding to the sound effect enhancement method provided in the embodiment of the application.Processor 910 by running the software program, instruction and module stored in memory 920, thereby executing the various functional applications and data processing of car machine, that is, realizing the sound effect enhancement method in the above method embodiment.

[0132] Memory 920 can include program storage area and data storage area, wherein the program storage area can store operating system, at least one application required by function;Data storage area can store data created according to the use of electronic equipment and the like.In addition, the memory 920 can include high-speed random access memory, and can also include nonvolatile memory, such as at least one magnetic disk storage device, flash memory device, or other nonvolatile solid-state memory device.In some examples, the memory 920 can further include memory remotely arranged with respect to the processor 910, which can be connected to the device through network.The above-mentioned examples of network include but are not limited to Internet, intranet, local area network, mobile communication network and combination thereof.

[0133] Input device 930 can be used to receive input digital or character information, and generate key signal input related to user settings and function control of data center.Output device 940 can include display device such as display screen.

[0134] The application also provides a computer readable storage medium, which stores a computer program executable by the car machine, when the computer program runs on the car machine, so that the car machine executes the steps of the sound effect enhancement method.

[0135] In particular, the computer storage medium of the embodiments of the present application can adopt any combination of one or more computer readable mediums. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium may, for example, be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination thereof. More specific examples (non-exhaustive list) of the computer readable storage medium include: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present embodiment, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or apparatus.

[0136] The present application also provides a vehicle provided with the sound effect enhancement device as described above.

[0137] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A sound effect enhancement method, characterized by, The sound effect enhancement method comprises: Real-time acquisition of vehicle condition information and road condition information in the driving process; Based on the vehicle condition information and the road condition information, the current scene is judged; Based on the result of the current scene judgment, different enhanced sound effects are applied.

2. The method of claim 1, wherein, Real-time acquisition of vehicle condition information and road condition information in the driving process comprises: Real-time acquisition of vehicle speed information; Real-time acquisition of current geographical position information; Real-time acquisition of current video image information outside the vehicle.

3. The method of claim 1, wherein, Based on the vehicle condition information and the road condition information, the current scene is judged, comprising: Cluster historical vehicle condition information and road condition information; Based on the clustering result, set several scene categories; Based on the set scene category, the current scene is judged.

4. The method of claim 1, wherein, Based on the vehicle condition information and the road condition information, the current scene is judged, comprising: Establishing SVM for scene classification; Based on the established SVM, the current scene is judged.

5. The method of claim 1, wherein, Based on the result of the current scene judgment, different enhanced sound effects are applied, comprising: When the vehicle passes through the sea-crossing bridge, the sea wave sound is mixed in the playing music.

6. The method of claim 1, wherein, Based on the result of the current scene judgment, different enhanced sound effects are applied, comprising: When the vehicle enters the tunnel, the echo effect is added.

7. The method of claim 1, wherein, Based on the result of the current scene judgment, different enhanced sound effects are applied, comprising: When the vehicle is in high-speed cruise, the anti-fatigue sound effect is started.

8. An audio enhancement device, characterized by The sound effect enhancement device comprises: An acquisition module for real-time acquisition of vehicle condition information and road condition information in the driving process; A judgment module for judging the current scene based on the vehicle condition information and the road condition information; An application module for applying different enhanced sound effects based on the result of the current scene judgment.

9. A car kit, characterized by The car machine comprises a processor and a memory, the memory stores a computer program, the computer program is loaded and executed by the processor to realize the sound effect enhancement method of any one of claims 1 to 7.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to realize the sound effect enhancement method of any one of claims 1 to 7.

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