Adaptive noise reduction method for earphone, earphone and storage medium

Abstract

The invention discloses an earphone adaptive noise reduction method, an earphone and a storage medium. The method comprises the following steps: acquiring current position information and an environment audio frequency spectrum of the earphone; key position information is extracted from the position information; determining an initial scene type and an associated scene type based on the key position information; obtaining a plurality of energy characteristic values according to the environment audio frequency spectrum and a preset extraction mode; determining a target scene type from the initial scene type and the associated scene type according to the energy feature value; and obtaining a noise reduction parameter corresponding to the target scene type as a target noise reduction parameter. According to the invention, by using the key position information and the energy characteristic value, rapid primary screening and side repeated screening of the scene types can be realized, the number of the scene types used for scene identification can be effectively reduced, the mode of obtaining the noise reduction parameters is optimized, the efficiency and accuracy of scene identification are improved, the calculation amount of scene identification is reduced, the power consumption of the earphone is reduced, and the user experience is improved. And the user experience is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of headphone noise reduction, and in particular to a headphone adaptive noise reduction method, a headphone and a storage medium. Background Art

[0002] With the rapid development of electronic technology, wireless headphones have become an indispensable item for people's daily travel. When facing various relatively noisy environments, people usually choose to wear wireless headphones to reduce the impact of environmental noise.

[0003] Different noise environments have different requirements for noise reduction. To better cope with the changing noise environment, some existing wireless headphones use adaptive noise reduction to reduce the impact of environmental noise. However, the existing adaptive noise reduction method cannot identify the noise scene relatively accurately and efficiently, which leads to a low matching degree of noise reduction requirements, and the existing adaptive noise reduction has high power consumption, which in turn affects the user experience.

[0004] In view of this, it is necessary to provide a method for adaptive noise reduction of headphones, headphones and storage media to solve the above problems. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a method for adaptive noise reduction of headphones, headphones and a storage medium, which aim to solve the technical problems of low scene recognition efficiency and accuracy and excessive power consumption of adaptive noise reduction of headphones.

[0006] To achieve the above-mentioned purpose, a first aspect of the present invention provides a method for adaptive noise reduction of headphones, comprising:

[0007] Get the current location information of the headset and the ambient audio spectrum;

[0008] Extract key location information from the location information;

[0009] Based on the key location information, determine the initial scene type and the associated scene type;

[0010] According to the ambient audio spectrum and the preset extraction mode, a number of energy characteristic values ​​are obtained;

[0011] According to the energy characteristic value, a target scene type is determined from the initial scene type and the associated scene type;

[0012] Get the noise reduction parameters corresponding to the target scene type as the target noise reduction parameters.

[0013] In a preferred embodiment, the step of determining the initial scene type and the associated scene type based on the key location information includes:

[0014] Obtaining the adaptation value of key location information and preset scene type;

[0015] Based on the adaptation value, determining an initial scene type from preset scene types;

[0016] Get the associated values ​​of the initial scene type and the remaining scene types;

[0017] Based on the association value and a preset first threshold, an associated scene type is obtained from the remaining scene types.

[0018] In a preferred embodiment, the step of determining the initial scene type from preset scene types based on the adaptation value includes:

[0019] Get the maximum value among the adaptation values ​​as the maximum adaptation value;

[0020] The scene type corresponding to the maximum adaptation value is marked as the initial scene type.

[0021] In a preferred embodiment, the step of obtaining the associated scene type from the remaining scene types based on the associated value and the preset first threshold comprises:

[0022] Get the maximum value among the adaptation values ​​as the maximum adaptation value;

[0023] Obtaining a preset adaptation value interval in which the maximum adaptation value falls, and using it as a target adaptation value interval;

[0024] Obtaining a preset first threshold corresponding to the target adaptation value interval;

[0025] Determining whether the correlation value is less than a first threshold;

[0026] If not, the scene type corresponding to the associated value is marked as the associated scene type.

[0027] In a preferred embodiment, the step of obtaining a plurality of energy characteristic values ​​according to the ambient audio spectrum and the preset extraction mode includes:

[0028] Obtaining a preset sensitive interception frequency band and a preset interception bandwidth;

[0029] According to the sensitive interception frequency band, the interception bandwidth and the ambient audio spectrum, several target spectra are obtained;

[0030] The energy characteristic parameters of the target spectrum are calculated respectively to obtain the corresponding energy characteristic values.

[0031] In a preferred embodiment, the step of determining the target scene type from the initial scene type and the associated scene type according to the energy characteristic value includes:

[0032] Obtaining an energy interval of an initial scene type and an energy interval of an associated scene type;

[0033] Obtaining the number of energy characteristic values ​​falling into the energy interval of the initial scene type and the energy interval of the associated scene type;

[0034] Based on the number of falls, the target scene type is obtained.

[0035] In a preferred embodiment, the step of obtaining the target scene type based on the number of falls includes:

[0036] Get the value of the maximum number of numbers that fall into the number;

[0037] If the value is 1, the scene type corresponding to the maximum number of falls is marked as the target scene type;

[0038] If the value is greater than 1, the adaptation value between the scene type corresponding to the maximum number of falls and the key position information is obtained;

[0039] The scene type corresponding to the maximum value in the adaptation value is marked as the target scene type.

[0040] In a preferred embodiment, before the step of obtaining the current position information of the earphone and the ambient audio spectrum, the step further includes:

[0041] Detecting the degree of change of the ambient audio signal;

[0042] If the degree of change of the ambient audio signal exceeds a preset degree value, the current position information of the earphone and the ambient audio spectrum are obtained.

[0043] A second aspect of the present invention provides a headset, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the above steps of the method for adaptive noise reduction of the headset when executing the computer program.

[0044] A third aspect of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of any of the above-mentioned methods for adaptive noise reduction of headphones are implemented.

[0045] The beneficial effect of the present invention is that it can relatively quickly perform preliminary screening of scene types by utilizing key position information to obtain initial scene types and associated scene types, thereby reducing the number of scene types that need to be identified. In combination with a number of energy characteristic values, it can further re-screen the initial scene types and associated scene types with more emphasis to obtain target scene types, thereby optimizing the method for obtaining target noise reduction parameters, reducing the amount of calculation for scene recognition, improving the efficiency and accuracy of adaptive noise reduction, reducing the power consumption of headphones to a certain extent, and improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A schematic diagram of a first process flow of a method for adaptive noise reduction of headphones disclosed in an embodiment of the present invention;

[0047] Figure 2 A second flow chart of the method for adaptive noise reduction of headphones disclosed in an embodiment of the present invention;

[0048] Figure 3 A third flow chart of the method for adaptive noise reduction of headphones disclosed in an embodiment of the present invention;

[0049] Figure 4 A fourth flow chart of the method for adaptive noise reduction of headphones disclosed in an embodiment of the present invention;

[0050] Figure 5 A fifth flow chart of the method for adaptive noise reduction of headphones disclosed in an embodiment of the present invention;

[0051] Figure 6 This is a schematic diagram of the module structure of the earphone disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In the present invention, the terms "disposed", "provided with" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral structure; it can be a mechanical connection, or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be an internal connection between two devices, elements or components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0053] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "plurality" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0054] In addition, some of the above terms may be used to express other meanings in addition to indicating orientation or positional relationship. For example, the term "on" may also be used to express a certain dependency or connection relationship in some cases. For those skilled in the art, the specific meanings of these terms in the present invention can be understood according to specific circumstances.

[0055] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0056] The following is the content of the first aspect of the present invention:

[0057] Please refer to Figure 1 In this embodiment, the method for adaptive noise reduction of headphones includes the following steps:

[0058] S1. Obtain the current position information of the headset and the ambient audio spectrum.

[0059] Among them, the earphones mentioned in this embodiment are earphones with active noise reduction function. The earphones can be wireless earphones or wired earphones. They include a processor and an audio output unit. The processor can generate an audio signal with a phase opposite to the ambient noise through the acquired noise reduction parameters, thereby offsetting the external ambient noise.

[0060] The location information of the headset can be location information with the headset as the positioning center, or it can be location information with the positioning center of the terminal device connected to the headset. Accordingly, the location information of the headset can be obtained directly or indirectly. Specifically, a positioning module can be set inside the headset, and the location information of the current headset can be directly obtained through the positioning module; it can also be indirectly obtained through a terminal device (such as a mobile phone, smart watch, etc.) with a positioning module set inside, and the location information of the terminal device is approximately equivalent to the location information of the headset. In the daily use of the headset, the headset is mostly connected to a terminal device for use. Preferably, the location information of the terminal device can be used to indirectly obtain the location information of the headset.

[0061] The ambient audio spectrum is a spectrum diagram obtained by processing and transforming the ambient audio signal, which can be used to reflect the sound characteristics of the current scene of the headset. The ambient audio signal can be obtained in real time through the acquisition microphone set on the headset, or it can be obtained periodically through the acquisition microphone set on the headset. The ambient audio spectrum can be the spectrum obtained by the short-time Fourier transform of the ambient audio signal, or it can be a Mel spectrum, etc., which can be selected according to the actual design requirements.

[0062] Preferably, the ambient audio spectrum is a Mel spectrum. It is easy to understand that the Mel spectrum can better reflect the characteristic information of the ambient audio signal in the frequency band sensitive to the human ear. Specifically, after obtaining the ambient audio signal, the ambient audio signal is first framed and windowed, and then each frame after windowing is Fourier transformed, and then the obtained spectrum is passed through a preset Mel filter group, the power spectrum is mapped to the Mel frequency, and then the logarithm is taken to obtain the logarithmic power spectrum, and then a discrete cosine transform is performed to obtain the Mel spectrum.

[0063] S2, extracting key location information from the location information;

[0064] S3. Based on the key location information, determine the initial scene type and the associated scene type.

[0065] Among them, the key position information is the marking parameter information used to reflect the position characteristics of the scene the headset is currently in. The initial scene type is the scene type used for subsequent recognition and determination obtained from the preset scene types through the key position information. The associated scene type is a scene type that has a certain degree of similarity between the sound characteristics and the determined initial scene type.

[0066] Specifically, a geographic information database that preliminarily determines the scene type based on the key location information is pre-set in the headset system or the headset's accompanying APP. By comparing the extracted key location information, the corresponding scene type can be confirmed. Similarly to the method of obtaining location information, the key location information can be extracted through the headset or through a terminal device connected to the headset.

[0067] After extracting the key position information from the position information, the initial scene type and the associated scene type can be determined respectively through the key position information, that is, the adaptation degree of the key position information is used as the basis for division, and then the initial scene type and the associated scene type are obtained respectively; the initial scene type can also be determined first through the key position information, and then the associated scene type is determined according to the degree of association between the initial scene type and other remaining scene types.

[0068] It can be understood that by determining the initial scene type and associated scene type through key location information, the scene types can be preliminarily screened relatively quickly, effectively reducing the number of scene types used for identification and comparison, improving the efficiency and accuracy of scene recognition, reducing the computational complexity of subsequent scene recognition, and reducing the power consumption of the headphones to a certain extent.

[0069] S4, obtaining a number of energy characteristic values ​​according to the ambient audio spectrum and the preset extraction mode;

[0070] S5. Determine the target scene type from the initial scene type and the associated scene type according to the energy characteristic value;

[0071] S6. Obtain noise reduction parameters corresponding to the target scene type as target noise reduction parameters.

[0072] The energy characteristic value is the energy parameter information corresponding to the frequency band of the ambient audio spectrum, which can be used to reflect the energy characteristic of the ambient audio spectrum. The target scene type is the scene type that best meets the requirements between the initial scene type and the associated scene type.

[0073] Specifically, the extraction mode can be to extract the entire frequency band of the ambient audio spectrum, or to extract a specific frequency band of the ambient audio signal. Preferably, the extraction mode is to extract a specific frequency band of the ambient audio signal. It is understandable that since the human ear has different perceptual sensitivities to different frequencies, and the initial scene type and the associated scene type are both scene types obtained after preliminary screening, the method of extracting a specific frequency band of the ambient audio spectrum can be combined with the characteristics of the human ear to more focus on extracting feature information of the frequency band to which the human ear is relatively sensitive. While improving the accuracy of feature extraction, it reduces the amount of feature information to be extracted, thereby improving the efficiency and accuracy of screening using feature information in subsequent scene recognition.

[0074] Based on the preset extraction mode, several required intercepted spectra can be intercepted from the ambient audio spectrum, and then corresponding energy characteristic values ​​can be extracted from the obtained several intercepted spectra. The energy characteristic value can be the average value of the energy values ​​of each frequency on the intercepted spectrum, the maximum value of the energy values ​​of each frequency on the intercepted spectrum, or the variance value of the energy values ​​of each frequency on the intercepted spectrum.

[0075] After obtaining the energy characteristic value, the target scene type can be determined from the initial scene type and the associated scene type according to the energy characteristic value by obtaining the energy interval of the initial scene type and the associated scene type, and then comparing the energy characteristic value with the corresponding energy interval, and then screening the target scene type according to the number of energy characteristic values ​​falling into the energy interval; or it can be based on the preset energy characteristic value weight formula, calculate the energy integration value, and then compare it with the preset comparison interval to determine the target scene type. After determining the target scene type, the noise reduction parameters corresponding to the target scene type can be obtained, thereby realizing the adaptive noise reduction function of the headphones.

[0076] It can be understood that the present invention obtains the initial scene type and the associated scene type by utilizing the key position information, and can perform preliminary screening of the scene types relatively quickly, thereby reducing the number of scene types that need to be identified. Combined with a number of energy characteristic values, the initial scene type and the associated scene type can be further re-screened with more emphasis to obtain the target scene type, thereby optimizing the method of obtaining the target noise reduction parameters, reducing the amount of calculation for scene recognition, improving the efficiency and accuracy of adaptive noise reduction, reducing the power consumption of the headphones to a certain extent, and improving the user experience.

[0077] For further information, please refer to Figure 2 In one embodiment, step S3 of determining the initial scene type and the associated scene type based on the key location information includes:

[0078] S31, obtaining an adaptation value between key location information and a preset scene type;

[0079] S32: Determine an initial scene type from preset scene types based on the adaptation value

[0080] S33, obtaining the association value between the initial scene type and the remaining scene types;

[0081] S34. Obtaining an associated scene type from the remaining scene types based on the associated value and a preset first threshold.

[0082] Among them, the adaptation value is the adaptation degree value between the key position information and each scene type, which can be used to quantify the adaptation degree between each scene type and the key position information. The association value reflects the similarity of the characteristics of each scene type on the spectrum. The association value is a parameter generated by pre-training and analyzing each scene type. Each scene type generates a corresponding association value for the other scene types. The first threshold is a value used for association value screening. The association value can be screened to obtain the associated scene type that meets the requirements.

[0083] Specifically, when obtaining the adaptation value of the key position information and the scene type, a weight relationship can be assigned to specific parameters in the key position information to determine the adaptation degree value of the key position information and each scene type, and then determine the initial scene type that meets the requirements. It is easy to understand that the number of initial scene types that can be determined by the key position information can be one or more, and can be set according to the actual design requirements to control the number of initial scene types obtained.

[0084] It can be understood that the method of using adaptation values ​​to filter the initial scene types can achieve quantitative filtering of the initial scene types, which can better control the number of initial scene types obtained. At the same time, the method of adaptation values ​​can also better control the number of subsequent associated scene types obtained.

[0085] After obtaining the adaptation value, the method of determining the initial scene type from the scene types according to the adaptation value can be by setting a comparison value for screening, and then screening out the initial scene type that meets the requirements; or it can be by screening out the scene type with the highest degree of adaptation in the adaptation value as the initial scene type, which can be selected according to actual design requirements.

[0086] In a preferred embodiment, the step S32 of determining the initial scene type from the preset scene types based on the adaptation value includes:

[0087] S321. Obtain the maximum value among the adaptation values ​​as the maximum adaptation value;

[0088] S322: Mark the scene type corresponding to the maximum adaptation value as the initial scene type.

[0089] Specifically, after the adaptation value is obtained, a plurality of adaptation values ​​are compared to determine a maximum value among the plurality of adaptation values, and the adaptation value of the maximum value is marked as a maximum adaptation value.

[0090] It can be understood that the method of determining the initial scene type through the maximum adaptation value ensures the accuracy of the initial scene types obtained through screening. At the same time, it can effectively control the number of initial scene types obtained, and to a certain extent reduce the number of associated scene types obtained subsequently, thereby reducing the computational complexity of subsequent scene type identification to a certain extent.

[0091] After the initial scene type is determined, the association value between the initial scene type and the remaining scene types can be obtained. After the association value is obtained, the association value is combined with the comparison result of the first threshold value to determine the associated scene type. It is easy to understand that the method of determining the associated scene type by the association value of the initial scene type can ensure the accuracy of the scene type used for subsequent scene recognition.

[0092] In the process of screening associated scene types, the setting of the first threshold will affect the screening results of the associated scene types. Specifically, the setting method of the first threshold can be to set a fixed value, or to establish a database of the first threshold, and then call the corresponding first threshold based on the specific situation of the associated value.

[0093] It can be understood that through the adaptation value of key position information and scene type, the preset scene types can be preliminarily screened to determine the initial scene type, and then according to the association value between the initial scene type and the remaining scene types, the associated scene types that meet the similarity requirements with the initial scene type can be determined, thereby ensuring the efficiency and accuracy of subsequent scene recognition, reducing the number of scene types for subsequent scene recognition, reducing the computational complexity of subsequent scene type recognition, and reducing the power consumption of the headphones.

[0094] For further information, please refer to Figure 3 In a preferred embodiment, the step S34 of obtaining the associated scene type from the remaining scene types based on the associated value and the preset first threshold value includes:

[0095] S341. Obtain the maximum value among the adaptation values ​​as the maximum adaptation value;

[0096] S342, obtaining a preset adaptation value interval in which the maximum adaptation value falls, and using it as a target adaptation value interval;

[0097] S343, obtaining a preset first threshold corresponding to the target adaptation value interval;

[0098] S344, determining whether the correlation value is less than a first threshold;

[0099] S345: If not, mark the scene type corresponding to the associated value as an associated scene type.

[0100] Among them, the adaptation value interval is a pre-set comparison interval for determining the first threshold corresponding to the maximum adaptation value. Each adaptation value interval corresponds to a first threshold. The larger the endpoint of the adaptation value interval, the larger the corresponding first threshold. The number and interval size of the adaptation value interval can be set according to actual design requirements.

[0101] Specifically, after obtaining the maximum adaptation value, the adaptation value interval in which the maximum adaptation value falls can be determined first, and then the corresponding first threshold value can be obtained according to the adaptation value interval in which it falls. When the maximum adaptation value is relatively high, a first threshold value with a relatively large value can be obtained, and when the maximum adaptation value is relatively low, a first threshold value with a relatively small value can be obtained.

[0102] It is easy to understand that by obtaining the corresponding first threshold based on the maximum adaptation value, it is possible to dynamically adjust the difficulty of obtaining associated scene types according to the degree of adaptation of the key position information and the scene type, thereby controlling the number of associated scene types obtained according to the adaptation value. That is, when the maximum adaptation value is relatively high, the size of the first threshold can be appropriately increased, which can effectively reduce the amount of associated scene models obtained, thereby improving the efficiency of subsequent scene recognition; when the maximum adaptation value is relatively low, the size of the first threshold can be appropriately reduced, and relatively more associated scene models can be obtained, thereby ensuring the accuracy of subsequent scene recognition.

[0103] For further information, please refer to Figure 4 In one embodiment, step S4 of obtaining a plurality of energy characteristic values ​​according to the ambient audio spectrum and the preset extraction mode includes:

[0104] S41, obtaining a preset sensitive interception frequency band and a preset interception bandwidth;

[0105] S42, obtaining a plurality of target spectra according to the sensitive interception frequency band, the interception bandwidth and the ambient audio spectrum;

[0106] S43, respectively calculating energy characteristic parameters of the target spectrum to obtain corresponding energy characteristic values.

[0107] The sensitive interception frequency band is a specific frequency range for feature extraction pre-set according to the auditory characteristics of the human ear. Preferably, the sensitive interception frequency band is 1000Hz to 4000Hz. The interception bandwidth is an interception unit for further intercepting the spectrum within the sensitive interception frequency band from the frequency dimension. The target spectrum is the spectrum obtained after two interceptions of the ambient audio spectrum, which is the spectrum calculation unit of the energy characteristic value.

[0108] After obtaining the ambient audio spectrum, the corresponding sensitive interception frequency band and interception bandwidth are obtained, and then the ambient audio spectrum is intercepted according to the obtained sensitive interception frequency band and interception bandwidth. Specifically, the ambient audio spectrum can be first processed according to the sensitive interception frequency band to obtain a sensitive spectrum, and then the sensitive spectrum is processed using the interception bandwidth to obtain several target spectra. The ambient audio spectrum can also be first processed according to the interception bandwidth to obtain several sub-spectra, and then the sub-spectra within the sensitive interception frequency band are obtained from the several sub-spectra as the target spectrum.

[0109] After obtaining a number of target spectra, the energy characteristic parameters in the target spectrum are extracted, and then the energy characteristic value is calculated. The energy characteristic value can be the energy average value in the target spectrum, the maximum value of the energy in the target spectrum, or the energy variance value in the target spectrum, which can be selected according to the actual design requirements. Preferably, the energy characteristic value is the energy average value of the target spectrum. It is easy to understand that the energy characteristic value is the energy average value in the target spectrum, which can better reflect the overall energy situation of the target spectrum and effectively reduce the impact of energy mutation.

[0110] It can be understood that by setting the sensitive interception frequency band and interception bandwidth, a target spectrum that is more in line with the auditory characteristics of the human ear can be obtained, and then energy eigenvalues ​​that are more in line with the auditory characteristics of the human ear and have a more obvious impact on the noise reduction effect can be extracted, thereby reducing the amount of computational cost of feature extraction, improving the efficiency and accuracy of feature extraction, and thus improving the efficiency and accuracy of subsequent scene recognition.

[0111] For further information, please refer to Figure 5 In one embodiment, step S5 of determining the target scene type from the initial scene type and the associated scene type according to the energy characteristic value includes:

[0112] S51, obtaining an energy interval of an initial scene type and an energy interval of an associated scene type;

[0113] S52, obtaining the number of energy characteristic values ​​falling into the energy interval of the initial scene type and the energy interval of the associated scene type;

[0114] S53: Based on the number of falls, obtain the target scene type.

[0115] The energy interval is the energy fluctuation interval of the corresponding frequency band in the scene type, which represents the acceptable range of energy fluctuation of the scene type in the corresponding frequency band. The falling number is the number of energy characteristic values ​​falling into the corresponding energy interval.

[0116] After obtaining the energy characteristic value, the energy interval of the initial scene type and the associated scene type is obtained, and then the energy characteristic value is compared with the energy interval corresponding to the initial scene type and the associated scene type item by item, and then the number of energy characteristic values ​​falling into the corresponding energy interval can be obtained. After obtaining the number of falls corresponding to the initial scene type and the associated scene type, the target scene type can be screened according to the specific situation of the number of falls. Specifically, by comparing the number of falls, the scene type corresponding to the maximum number of falls can be used as the target scene type; the corresponding situation can also be divided according to the number of maximum falls, and then the target scene type can be obtained; it can also be combined with the key position information and the adaptability of the scene type for comprehensive screening, and then the target scene type can be obtained.

[0117] In a preferred embodiment, step S53 of obtaining the target scene type based on the number of falls includes:

[0118] S531, obtaining the value of the maximum number of falling numbers among the falling numbers;

[0119] S532: If the value is 1, the scene type corresponding to the maximum number of falls is marked as the target scene type;

[0120] S533: If the number is greater than 1, obtain the adaptation value between the scene type corresponding to the maximum number of falls and the key position information;

[0121] S534: Mark the scene type corresponding to the maximum value of the adaptation values ​​as the target scene type.

[0122] After obtaining the number of falls, the numbers of falls are compared with each other to obtain the maximum number of falls, and then the number of the maximum number of falls can be obtained. When the number of the maximum number of falls is only one, it means that there is a scene type in which the degree of conformity of the energy characteristic values ​​of the human ear's high-sensitivity frequency band exists between the initial scene type and the associated scene type. Therefore, the scene type corresponding to the maximum number of falls can be directly used as the target scene type. When the number of the maximum number of falls is greater than one, the target scene type can be comprehensively determined in combination with the adaptation value of the key position information. By comparing the adaptation values ​​between the scene types corresponding to the maximum number of falls, the maximum adaptation value is determined, and the scene type corresponding to the maximum number of falls and the maximum adaptation value is used as the target scene type.

[0123] It can be understood that by comparing the energy characteristic value with the energy interval and based on the number of energy characteristic values ​​falling into the energy interval, the target scene type can be determined, and then the initial scene type and the associated scene type can be further re-screened, thereby optimizing the method of obtaining the target scene type and improving the efficiency and accuracy of scene type re-screening.

[0124] Furthermore, in one embodiment, before the step S1 of obtaining the current position information of the earphone and the ambient audio spectrum, the method further includes:

[0125] S10, detecting the degree of change of the ambient audio signal;

[0126] S20: If the degree of change of the ambient audio signal exceeds a preset degree value, obtain the current position information of the earphone and the ambient audio spectrum.

[0127] It can be understood that before triggering the scene recognition of adaptive noise reduction, the adaptive noise reduction function will be triggered by detecting the degree of change of the ambient audio signal. Only when the degree of change of the ambient audio signal exceeds the preset value will the scene recognition be started. This can effectively avoid the frequent triggering of scene recognition of adaptive noise reduction and the frequent false triggering and switching of noise reduction parameters, which can effectively reduce the power consumption of headphones and improve the user experience.

[0128] To summarize, the present application utilizes key position information to obtain the initial scene type and the associated scene type, and can perform preliminary screening of the scene types relatively quickly, thereby reducing the number of scene types that need to be identified. Combined with a number of energy characteristic values, the initial scene type and the associated scene type can be further re-screened with more focus to obtain the target scene type, thereby optimizing the method of obtaining the target noise reduction parameters, reducing the amount of calculation for scene recognition, improving the efficiency and accuracy of adaptive noise reduction, reducing the power consumption of the headphones to a certain extent, and improving the user experience.

[0129] The following is the content of the second aspect of the present invention:

[0130] The present invention provides an earphone, such as Figure 6 As shown, the headset includes a memory 10, a processor 20, and a method program instruction 30 for adaptive noise reduction of the headset stored in the memory 10 and executable on the processor 20. When the method program instruction 30 for adaptive noise reduction of the headset is executed by the processor 20, the aforementioned method for adaptive noise reduction of the headset is implemented.

[0131] In some embodiments, the processor may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor is generally used to control the overall operation of the headset. In this embodiment, the processor is used to run program codes stored in a readable storage medium or process data.

[0132] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a readable storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0133] The following is the content of the third aspect of the present invention:

[0134] The present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned headphone adaptive noise reduction method are implemented.

[0135] The above is only a specific implementation method of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for adaptive noise reduction of headphones, characterized in that: include: Get the current location information of the headset and the ambient audio spectrum; extracting key location information from the location information; Based on the key location information, determining an initial scene type and an associated scene type; According to the ambient audio spectrum and a preset extraction mode, a plurality of energy characteristic values ​​are obtained; Determining a target scene type from the initial scene type and the associated scene type according to the energy characteristic value; A noise reduction parameter corresponding to the target scene type is obtained as a target noise reduction parameter.

2. The method for adaptive noise reduction of headphones according to claim 1, characterized in that: The step of determining the initial scene type and the associated scene type based on the key position information includes: Obtaining an adaptation value between the key location information and a preset scene type; Based on the adaptation value, determining an initial scene type from preset scene types; Obtaining association values ​​between the initial scene type and the remaining scene types; Based on the association value and a preset first threshold, an associated scene type is obtained from the remaining scene types.

3. The method for adaptive noise reduction of headphones according to claim 2, characterized in that: The step of determining the initial scene type from the preset scene types based on the adaptation value comprises: Obtaining a maximum value among the adaptation values ​​as a maximum adaptation value; The scene type corresponding to the maximum adaptation value is marked as the initial scene type.

4. The method for adaptive noise reduction of headphones according to claim 2, characterized in that: The step of obtaining the associated scene type from the remaining scene types based on the associated value and the preset first threshold comprises: Obtaining a maximum value among the adaptation values ​​as a maximum adaptation value; Obtaining a preset adaptation value interval within which the maximum adaptation value falls, and using the preset adaptation value interval as a target adaptation value interval; Obtaining a preset first threshold corresponding to the target adaptation value interval; Determining whether the correlation value is less than the first threshold; If not, the scene type corresponding to the associated value is marked as an associated scene type.

5. The method for adaptive noise reduction of headphones according to claim 1, characterized in that: The step of obtaining a plurality of energy characteristic values ​​according to the ambient audio spectrum and the preset extraction mode comprises: Obtaining a preset sensitive interception frequency band and a preset interception bandwidth; According to the sensitive interception frequency band, the interception bandwidth and the ambient audio spectrum, a plurality of target spectrums are obtained; The energy characteristic parameters of the target spectrum are calculated respectively to obtain corresponding energy characteristic values.

6. The method for adaptive noise reduction of headphones according to claim 1, characterized in that: The step of determining the target scene type from the initial scene type and the associated scene type according to the energy characteristic value comprises: Acquire an energy interval of the initial scene type and an energy interval of an associated scene type; Obtaining the number of times that the energy characteristic value falls within the energy interval of the initial scene type and the energy interval of the associated scene type; Based on the falling number, a target scene type is obtained.

7. The method for adaptive noise reduction of headphones according to claim 6, characterized in that: The step of obtaining the target scene type based on the falling number includes: Obtain the maximum number of falling numbers among the falling numbers; If the number is 1, the scene type corresponding to the maximum number of falls is marked as the target scene type; If the number is greater than 1, obtaining an adaptation value between the scene type corresponding to the maximum number of falls and the key position information; The scene type corresponding to the maximum value of the adaptation values ​​is marked as the target scene type.

8. The method for adaptive noise reduction of headphones according to claim 1, characterized in that: Before the step of obtaining the current position information of the earphone and the ambient audio spectrum, the following step is further included: Detecting the degree of change of the ambient audio signal; If the degree of change of the ambient audio signal exceeds a preset degree value, the current position information of the earphone and the ambient audio spectrum are obtained.

9. A headset comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the headphone adaptive noise reduction method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for adaptive noise reduction of headphones according to any one of claims 1 to 8 are implemented.

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