Home theater configuration method, system and device and storage medium

Abstract

The embodiment of the invention provides a home theater configuration method, system and device and a storage medium, a home theater comprises a control terminal, user equipment and one or more sound equipment, the control terminal, the user equipment and the sound equipment are provided with Bluetooth devices, and the method comprises the steps of obtaining a target position relation based on the Bluetooth devices, the target position relationship comprises a relative position relationship among the control terminal, the user equipment and the sound equipment; generating a target configuration parameter of the sound equipment based on the target position relationship; generating an updating instruction based on the target configuration parameter, and sending the updating instruction to the target sound equipment for execution, so that the target sound equipment performs parameter updating according to the target configuration parameter, and the home theater meets the target configuration condition; the target sound equipment is sound equipment needing parameter updating; the target configuration condition is that the user equipment position is in a target position range.

Description

Technical Field

[0001] The present invention relates to the field of home theater, and in particular to a home theater configuration method, system, device and storage medium. Background Art

[0002] In a home theater scenario, when audio equipment plays audio files, there is often a position with the best sound quality, i.e., the best listening position. Currently, the best listening position can be set at a user-specified position (e.g., where the user usually sits at home) by adjusting the configuration of the audio equipment, but it usually requires professionals to calibrate the best listening position on site, and it cannot be achieved by the user on their own.

[0003] In view of this, it is desired to provide a home theater configuration method, system, device and storage medium that can automatically adjust the configuration parameters of the audio equipment in the home theater, calibrate the optimal listening position to the user-specified position, reduce the difficulty and cost of manual calibration during the home theater installation process, and improve the convenience of home theater configuration. Summary of the invention

[0004] One or more embodiments of the present specification provide a method for configuring a home theater, wherein the home theater includes a control terminal, a user device, and one or more audio devices, wherein the control terminal, the user device, and the audio device are equipped with a Bluetooth device, and the method includes: obtaining a target position relationship based on the Bluetooth device, wherein the target position relationship includes a relative position relationship between the control terminal, the user device, and the audio device; generating target configuration parameters of the audio device based on the target position relationship; generating an update instruction based on the target configuration parameters, and sending the update instruction to a target audio device for execution, so that the target audio device updates parameters with the target configuration parameters, so that the home theater meets a target configuration condition; the target audio device is the audio device for which parameters need to be updated; and the target configuration condition is that the user device is located within a target position range.

[0005] One or more embodiments of the present specification provide a home theater configuration system, wherein the home theater includes a control terminal, a user device and one or more audio devices, wherein the control terminal, the user device and the audio device are equipped with a Bluetooth device, and the system includes: a position relationship acquisition module, configured to acquire a target position relationship based on the Bluetooth device, wherein the target position relationship includes a relative position relationship between the control terminal, the user device and the audio device; a parameter generation module, configured to generate target configuration parameters of the audio device based on the target position relationship; an instruction execution module, configured to generate an update instruction based on the target configuration parameters, and send the update instruction to a target audio device for execution, so that the target audio device is updated with the target configuration parameters, so that the home theater meets the target configuration condition; the target audio device is the audio device for which parameters need to be updated; the target configuration condition is that the user device is located within the target position range.

[0006] One or more embodiments of the present specification provide a home theater configuration device, including a processor, wherein the processor is used to execute any of the home theater configuration methods described above.

[0007] One or more embodiments of the present specification provide a computer-readable storage medium, wherein the storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes any of the above-described home theater configuration methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0009] Figure 1 is a schematic diagram of an application scenario of a home theater configuration system according to some embodiments of this specification;

[0010] Figure 2 is an exemplary module diagram of a home theater configuration system according to some embodiments of the present specification;

[0011] Figure 3 is an exemplary flow chart of a home theater configuration method according to some embodiments of this specification;

[0012] Figure 4 is an exemplary flow chart of generating target configuration parameters according to some embodiments of this specification;

[0013] Figure 5 It is an exemplary schematic diagram of the sound field determination model shown in some embodiments of this specification. DETAILED DESCRIPTION

[0014] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0015] It should be understood that the "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0016] As shown in this specification and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0017] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0018] There is an optimal listening position for the home theater's audio equipment when playing sound sources. Users can adjust the configuration of the audio equipment to set the optimal listening position at a common viewing position. However, this generally requires a technician to come to the site to calibrate the optimal listening position, which increases the cost of manual calibration of the optimal listening position during the home theater installation process.

[0019] The present invention provides a home theater configuration method, system, device and storage medium, which reduce the cost of manual calibration by automatically adjusting various configuration parameters of audio equipment.

[0020] Figure 1 It is a schematic diagram of an application scenario of a home theater configuration system according to some embodiments of this specification.

[0021] The home theater configuration system can be applied to a home theater. This manual mainly focuses on adjusting the configuration parameters of all audio devices in the home theater and determining the best listening position through Bluetooth positioning in the home theater.

[0022] A home theater is a home audio-visual system composed of multiple audio equipment, a large-screen TV, and high-quality A / V program sources, which has surround sound theater audio-visual effects. The embodiments of this specification can be described by taking the application of a home theater configuration system in a home theater as an example.

[0023] In some embodiments, the application scenario 100 of the home theater configuration system may include a processor 110 , a storage device 120 , a network 130 , a control terminal 140 , a user device 150 , an audio device 160 , and a Bluetooth device 170 .

[0024] In some embodiments, the processor 110 can process data and / or information obtained from components of the home theater configuration system or other devices. The processor can execute program instructions based on the data, information and / or processing results to perform one or more functions described in the present application, such as obtaining target position relationships, generating target configuration parameters and updating instructions, etc.

[0025] In some embodiments, the processor 110 may be a computer, a user console, a single processor, a processor group, etc. The processor group may be centralized or distributed. In some embodiments, the processor 110 may be implemented on a cloud platform. For example, the cloud platform may include one or any combination of a private cloud, a public cloud, a hybrid cloud, etc.

[0026] In some embodiments, the processor 110 may be an independent device or may be integrated into the control terminal 140 .

[0027] In some embodiments, the storage device 120 may be used to store data and / or instructions. For example, the storage device 120 may store target configuration conditions, user accuracy requirements, and the like.

[0028] The storage device 120 may include one or more storage components, each of which may be an independent device or a part of another device. In some embodiments, the storage device 120 may include a random access memory (RAM), a read-only memory (ROM), etc. In some embodiments, the storage device 120 may be implemented on a cloud platform. As an example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, etc., or any combination thereof.

[0029] In some embodiments, the storage device 120 can communicate with one or more components in the application scenario 100 of the home theater configuration system through the network 130 .

[0030] In some embodiments, the storage device 120 may include one or more databases, such as a first vector database, a second vector database, and the like.

[0031] The network 130 includes any suitable network capable of facilitating information and / or data exchange of the application scenario 100 of the home theater configuration system. In some embodiments, one or more components of the application scenario 100 of the home theater configuration system (e.g., the storage device 120, the control terminal 140, and the audio device 160, etc.) can exchange information and / or data through the network 130.

[0032] The network 130 may be any one or more of a wired network or a wireless network. For example, the network 130 may include a cable network, an optical fiber network, a telecommunication network, a cable connection, etc., or any combination thereof.

[0033] The control terminal 140 refers to a device that implements control functions in the home theater configuration system. In some embodiments, the control terminal 140 may be equipped with a Bluetooth device 170 .

[0034] The Bluetooth device 170 refers to a Bluetooth device for receiving and sending signals, for example, a Bluetooth receiver and a Bluetooth transmitter.

[0035] In some embodiments, the control terminal 140, the user device 150, and the audio device 160 may be equipped with a Bluetooth device 170. In some embodiments, the Bluetooth device 170 may be used for Bluetooth positioning, and may also be used for communication between devices. For example, the control terminal 140 may be equipped with a Bluetooth receiver, and the user device 150 and the audio device 160 may be equipped with a Bluetooth transmitter. The Bluetooth receiver of the control terminal 140 may receive Bluetooth signals sent by the Bluetooth transmitters of the user device 150 and the audio device 160 for Bluetooth positioning, so as to obtain the relative position relationship between the control terminal, the user device, and the audio device.

[0036] For another example, the control terminal 140, the user device 150 and the audio device 160 can all be equipped with a Bluetooth receiver and a Bluetooth transmitter to achieve communication and data interaction between the devices. For example, the Bluetooth receiver of the audio device 160 can receive an update instruction sent by the Bluetooth transmitter of the control terminal 140.

[0037] It should be noted that the Bluetooth device 170 in this specification is a device that supports Bluetooth version 5.1 or higher to provide more accurate positioning technology for obtaining the target position relationship.

[0038] In some embodiments, the control terminal may further include a preset number of antenna arrays, where the preset number is no less than 3.

[0039] An antenna array is an array structure composed of multiple individual antenna elements, which is used to determine the relative direction of the signal transmitter based on the phase difference of different antennas receiving the same signal at the same time. The location of the signal transmitter can be determined by using a preset number of antenna arrays.

[0040] The preset number of groups refers to the number of antenna arrays that are preset. In some embodiments, the preset number of groups can be determined in a variety of ways, such as by default setting by a processor or by a technician based on experience.

[0041] In some embodiments, at least three groups of antenna arrays can achieve Bluetooth positioning, and the preset number of groups is not less than 3.

[0042] In some embodiments, the preset number of groups can be determined based on scene space information and user accuracy requirements.

[0043] The scene space information refers to the space information of the home theater configuration scene, for example, the size and shape of the home interior space. The larger the space size and the more complex the space shape, the larger the corresponding preset group number can be.

[0044] User accuracy requirement refers to the user's requirement for Bluetooth positioning accuracy. In some embodiments, the processor can obtain the user accuracy requirement uploaded by the user input from the user device. When the user accuracy requirement is high, the preset number of antenna arrays can be high to improve the accuracy of Bluetooth positioning.

[0045] In some embodiments, the processor may construct a first target feature vector based on scene spatial information and user accuracy requirements, and determine a preset number of groups of the antenna array by querying a first vector database.

[0046] In some embodiments, the first vector database includes multiple first reference feature vectors and corresponding first tags, the first reference feature vector is composed of reference scene space information and reference user accuracy requirements, and the first tag is the reference preset group number corresponding to the first reference feature vector. The antenna array of the reference preset group number can meet the corresponding reference user accuracy requirements and can achieve accurate Bluetooth positioning. In some embodiments, the first vector database can be constructed by a processor and / or technician based on historical data.

[0047] In some embodiments, the processor can search in the first vector database, calculate the first similarity between the first target feature vector and multiple first reference feature vectors, determine the first reference feature vector with the highest first similarity, and determine the first label corresponding to it as the preset number of groups of the antenna array. The first similarity can be represented by cosine similarity or Euclidean distance.

[0048] In some embodiments of the present specification, the number of antenna array groups is determined by scene space information and user accuracy requirements, which can improve the accuracy of Bluetooth positioning while meeting user needs, thereby improving the accuracy of determining the best listening position.

[0049] In some embodiments of the present specification, multiple antenna arrays are deployed in the control terminal for Bluetooth positioning, which can achieve better signal transmission and reception performance, and is conducive to further improving the accuracy of Bluetooth positioning.

[0050] The user device 150 refers to a device used by a user to interact with the home theater configuration system. For example, the user device 150 may include a desktop computer 150-1, a tablet computer 150-2, a smart phone 150-3, etc. In some embodiments, the user may interact with other components of the home theater configuration system through the user device 150. For example, the user may input and upload the user accuracy requirement to the processor 110 and / or the storage device 120, etc. through the user terminal.

[0051] In some embodiments, the user equipment 150 may be equipped with a Bluetooth device 170 for Bluetooth positioning and data interaction. For the content of this part, please refer to the relevant description above.

[0052] The user device position refers to the placement of the user device, which can represent the optimal listening position required by the user as specified by the user, for example, the position of the sofa where the user often sits.

[0053] The best listening position refers to the position with the best listening effect. In some embodiments, the user can place the user device at the user device position, set the best listening position to the user device position, and the user can hear the best sound effect at the user device position.

[0054] The audio equipment 160 refers to equipment used for playing and amplifying sound in a home theater, such as a sound box, a speaker, etc.

[0055] In some embodiments, the audio device 160 may be equipped with a Bluetooth device 170 for Bluetooth positioning and data interaction. For the content of this part, please refer to the relevant description above.

[0056] In some embodiments, the audio device may further include a rotating component, and the rotating component may be configured to change the orientation of the audio device. The target configuration parameter may further include a target audio orientation.

[0057] The rotating part refers to a part that supports the rotation of the audio equipment. For example, the rotating part may include a driving part and a rotating part. The driving part may be a motor, a servo motor, etc., and the rotating part may be a rotating platform, a rotating bracket, etc. connected to the main part of the audio equipment.

[0058] The direction of the audio device refers to the direction in which the audio device is facing, for example, 30° east of north.

[0059] For more information about target configuration parameters and target sound orientation, see Figure 3 Related description.

[0060] In some embodiments of the present specification, changing the orientation of the audio equipment by rotating components is helpful to better control the propagation direction of sound waves, making the sound field more uniform, reducing sound reflection and interference, and making the sound effect at the best listening position more ideal.

[0061] In some embodiments, the control terminal 140 , the user equipment 150 and the audio device 160 may interact based on the Bluetooth device 170 . For example, the user equipment 150 and the audio device 160 may send Bluetooth signals to the control terminal 140 via the Bluetooth device 170 .

[0062] In some embodiments, the control terminal 140 , the user equipment 150 , the audio device 160 , the Bluetooth device 170 , the processor 110 , and the storage device 120 may also exchange information and / or data via the network 130 .

[0063] For more information about the above-mentioned related parameters (such as relative position relationship, update instructions, target configuration parameters and target sound direction, etc.), please refer to Figure 3-Figure 5 Related description.

[0064] It should be noted that the application scenarios are provided for illustrative purposes only and are not intended to limit the scope of this specification. For those of ordinary skill in the art, various modifications or variations can be made based on the description of this specification. For example, the application scenarios can be implemented on other devices to achieve similar or different functions. However, the changes and modifications will not deviate from the scope of this specification.

[0065] Figure 2 is an exemplary module diagram of a home theater configuration system according to some embodiments of the present specification.

[0066] In some embodiments, Figure 2 As shown, the home theater configuration system 200 may include a position relationship acquisition module 210 , a parameter generation module 220 and an instruction execution module 230 .

[0067] The position relationship acquisition module 210 is a module for acquiring a target position relationship. In some embodiments, the position relationship acquisition module 210 may be configured to acquire a target position relationship based on a Bluetooth device.

[0068] The parameter generation module 220 is a module for generating target configuration parameters. In some embodiments, the parameter generation module 220 can be configured to generate target configuration parameters of the audio device based on the target position relationship.

[0069] In some embodiments, the parameter generation module 220 can be further configured to: obtain one or more groups of candidate configuration parameters; determine the spatial feature map through the feature extraction layer of the sound field determination model based on historical playback data, historical reception data, scene spatial information and target position relationship; for a group of candidate configuration parameters, determine the predicted optimal positions corresponding to the candidate configuration parameters through the position prediction layer of the sound field determination model based on the candidate configuration parameters and the spatial feature map; determine the target configuration parameters based on one or more predicted optimal positions corresponding to one or more groups of candidate configuration parameters.

[0070] The instruction execution module 230 is a module for generating update instructions and sending them for execution. In some embodiments, the instruction execution module 230 can be configured to generate update instructions based on target configuration parameters, and send the update instructions to the target audio device for execution, so that the target audio device is updated with the target configuration parameters, so that the home theater meets the target configuration conditions.

[0071] In some embodiments, Figure 2 As shown, the home theater configuration system 200 may further include a parameter redetermining module 240 .

[0072] The parameter redetermining module 240 is a module for redetermining target configuration parameters. In some embodiments, the parameter redetermining module 240 may be configured to: redetermine the target configuration parameters in response to satisfying the configuration update condition.

[0073] For more information about the position relationship acquisition module 210 , the parameter generation module 220 , the instruction execution module 230 , and the parameter redetermination module 240 , please refer to the relevant description below.

[0074] It should be noted that the above description of the home theater configuration system and its modules is only for convenience of description and does not limit the present specification to the scope of the embodiments. It is understandable that, after understanding the principle of the system, those skilled in the art may arbitrarily combine the modules or form a subsystem to connect with other modules without deviating from the principle.

[0075] Figure 3 is an exemplary flow chart of a method for configuring a home theater according to some embodiments of this specification. Figure 3As shown, the process 300 includes the following steps 310 to 330. In some embodiments, the process 300 can be executed by a processor. The processor can be integrated into the position relationship acquisition module 210, the parameter generation module 220, the instruction execution module 230 and the parameter re-determination module 240 of the home theater configuration system 200. For more information about the processor, see Figure 1 Related description.

[0076] In some embodiments, a home theater may include a control terminal, a user device, and one or more audio devices. The control terminal, the user device, and the audio device are equipped with Bluetooth devices. The processor may obtain a target position relationship based on the Bluetooth device. The target position relationship includes the relative position relationship between the control terminal, the user device, and the audio device; based on the target position relationship, generate target configuration parameters of the audio device; based on the target configuration parameters, generate an update instruction, and send the update instruction to the target audio device for execution, so that the target audio device updates parameters with the target configuration parameters, so that the home theater meets the target configuration conditions; the target audio device is an audio device that needs to have its parameters updated; the target configuration condition is that the user device is within the target position range.

[0077] For more information about home theater, control terminals, user equipment, audio equipment, and Bluetooth devices, please refer to Figure 1 Related description.

[0078] Step 310: Acquire the target location relationship based on the Bluetooth device.

[0079] The target position relationship refers to the position relationship of each component in the home theater. In some embodiments, the target position relationship may include the relative position relationship between the control terminal, the user equipment, and all the audio equipment.

[0080] In some embodiments, the target position relationship can be represented in a variety of ways. For example, it can be represented by the spatial coordinates of the control terminal, the user device, and all the audio devices in the same coordinate system. The coordinate system can be a fixed coordinate system or a custom coordinate system. For example, a three-dimensional rectangular coordinate system is established with the location of the control terminal as the origin and any three mutually perpendicular directions as coordinate axes.

[0081] For example, the spatial coordinates of the control terminal are AP(0,0,0), and the spatial coordinates of the user equipment are BP(x B ,y B , z B ), there are 3 audio devices in the home theater, and their spatial coordinates are C 1 P(x c1 ,y c1 , z c1 ), C 2 P(xc2 ,y c2 , z c2 ), C 3 P(x c3 ,y c3 , z c3 ), then the target position relationship can be expressed as [AP(0, 0, 0), BP(x B ,y B , z B ), C 1 P(x c1 ,y c1 , z c1 ), C 2 P(x c2 ,y c2 , z c2 ), C 3 P(x c3 ,y c3 , z c3 )], where A, B, C 1 , C 2 , C 3 Respectively represent control terminal, user equipment, audio equipment 1, audio equipment 2, audio equipment 3, AP, BP, C 1 P.C. 2 P.C. 3 P represents the spatial coordinates of the control terminal, the user device, the audio device 1, the audio device 2, and the audio device 3 respectively.

[0082] In some embodiments, the processor can obtain the target position relationship through the Angle-of-Arrival (AoA) technology based on the Bluetooth device. The AoA technology may include: the control terminal 140 can receive multiple signals sent by the Bluetooth transmitters of the user device 150 and all the audio devices 160 based on its own Bluetooth receiver; for each signal, the phase difference of the signal received on different groups of antenna arrays can be obtained through the Bluetooth positioning protocol; based on the phase difference, the angle information of the Bluetooth transmitter corresponding to the signal and the antenna arrays of different groups is determined to obtain the relative position of the Bluetooth receiver and multiple Bluetooth transmitters, and the relative position relationship between the control terminal 140 and the user device 150 and all the audio devices 160 can be determined, that is, the target position relationship is obtained.

[0083] Step 320: Generate target configuration parameters of the audio device based on the target position relationship.

[0084] Target configuration parameters refer to the configuration parameters required by the audio equipment to make the user device position the best listening position. Configuration parameters refer to the setting parameters of the relevant performance of the audio equipment, such as volume, channel type, delay, gain, etc.

[0085] In some embodiments, the target configuration parameters may include a target volume, a target channel type, a target delay, and a target gain.

[0086] For more information on user device locations and sweet spot positions, see Figure 1 Related description.

[0087] The target volume refers to the volume level required by the audio equipment to make the user's device position the best listening position, for example, 20dB.

[0088] The target channel type refers to the type of role that the audio equipment plays in a multi-channel system in order to make the user device position the best listening position, for example, left channel, front channel, surround channel, etc.

[0089] Target delay refers to the delay time of the sound signal output by the audio device to make the user's device position the best listening position, that is, the time difference from the sound source input to the audio device to the sound device output. For example, 0.5ms, etc. Target delay can achieve stereo effects or sound field positioning, etc.

[0090] Target gain refers to the degree of signal amplification required by the audio equipment to make the user's device position the best listening position. Positive gain means that the signal is amplified, and negative gain means that the signal is weakened. For example, +10dB means that the signal power is amplified 10 times, and -10dB means that the signal power is weakened 10 times.

[0091] In some embodiments, the target configuration parameters may also include a target sound orientation.

[0092] The target sound orientation refers to the direction that the sound equipment needs to face in order to make the user's device position the best listening position.

[0093] In some embodiments, the target configuration parameters can be expressed as a sequence including target volume, target channel type, target delay, target gain, and target audio orientation. When playing an audio file in a home theater, one audio device corresponds to one target configuration parameter, and multiple audio devices correspond to a group of target configuration parameters.

[0094] For example, a home theater includes two audio devices. When playing audio file 1, the target configuration parameter of audio device 1 is C 1 S(vol 1 ,cha 1 ,del 1 , inc 1 , tow 1 ), the target configuration parameter of audio device 2 is C 2 S(vol 2 ,cha 2 ,del2 , inc 2 , tow 2 ), when the home theater plays the audio file 1, a set of target configuration parameters of all the audio devices in the home theater can be expressed as [C 1 S(vol 1 ,cha 1 ,del 1 , inc 1 , tow 1 ), C 2 S(vol 2 ,cha 2 ,del 2 , inc 2 , tow 2 )], where vol, cha, del, inc, tow represent the target volume, target channel type, target delay, target gain, and target sound direction, respectively. 1 S.C. 2 S represents the target configuration parameters of the audio device 1 and the target configuration parameters of the audio device 2 respectively.

[0095] An audio file refers to the audio data played by an audio device. An audio file can be a file in a variety of formats, such as a Waveform Audio File Format (WAV), an Audio Interchange File Format (AIFF), etc. For example, a track in WAV format.

[0096] In some embodiments, the processor may construct a second position feature vector based on the target position relationship and the audio device type, and determine the target configuration parameters by querying a second vector database.

[0097] In some embodiments, the second vector database includes a plurality of second reference feature vectors and corresponding second labels, the second reference feature vectors are composed of a reference target position relationship and a reference audio device type, and the second label is a reference configuration parameter corresponding to the second reference feature vector. In some embodiments, the second vector database can be constructed by a processor and / or a technician using actual experimental data.

[0098] In some embodiments, the processor may search in the second vector database, calculate the second similarity between the second position feature vector and the plurality of second reference feature vectors, determine the second reference feature vector with the highest second similarity, and determine the second label corresponding thereto as the target configuration parameter. The second similarity may be represented by cosine similarity or Euclidean distance.

[0099] In some embodiments, the processor can obtain one or more sets of candidate configuration parameters; based on historical playback data, historical reception data, scene space information and target position relationship, determine the spatial feature map through the feature extraction layer of the sound field determination model; for a set of candidate configuration parameters, based on the candidate configuration parameters and the spatial feature map, determine the predicted optimal position corresponding to the candidate configuration parameters through the position prediction layer of the sound field determination model; determine the target configuration parameters based on one or more predicted optimal positions corresponding to one or more sets of candidate configuration parameters. For more information about this part, please refer to Figure 4 Related description.

[0100] In some embodiments, the processor may generate one or more sets of candidate configuration parameters based on a preset algorithm iteration; for a set of candidate configuration parameters, based on the candidate configuration parameters and the spatial feature map, determine the predicted optimal position corresponding to the candidate configuration parameters through the position prediction layer of the sound field determination model; determine the adaptation score of the candidate configuration parameters based on the predicted optimal position; in response to meeting the algorithm stop condition, stop iteration, and determine the target configuration parameters based on one or more adaptation scores of the one or more sets of candidate configuration parameters. For more information about this part, please refer to the relevant description below.

[0101] Step 330: Generate an update instruction based on the target configuration parameters, and send the update instruction to the target audio device for execution, so that the target audio device updates parameters with the target configuration parameters, so that the home theater meets the target configuration conditions.

[0102] The update instruction refers to an instruction for adjusting the configuration parameters of the target audio device. In some embodiments, the update instruction may include the target audio device for which the parameters need to be updated and its corresponding target configuration parameters.

[0103] The target audio device refers to the audio device whose configuration parameters need to be adjusted.

[0104] In some embodiments, the processor may compare the target configuration parameters and initial configuration parameters of all audio devices in the home theater, determine that the audio device with inconsistent target configuration parameters and initial configuration parameters is the target audio device, and determine the update instruction based on the target audio device and the corresponding target configuration parameters. The initial configuration parameters refer to the initial values ​​of the configuration parameters of the audio device before adjustment, and the initial configuration parameters can be directly obtained by the processor reading the audio device.

[0105] For example, the initial configuration parameters of all audio equipment in a home theater are When playing audio file 1, a set of target configuration parameters for all audio devices is [C 1 S(vol 1 ,cha 1 ,del 1, inc 1 , tow 1 ), C 2 S(vol 2 ,cha 2 ,del 2 , inc 2 , tow 2 )], compare the initial configuration parameters with the target configuration parameters, where vol 1 and Different, 1 and Different, 2 and different, it is determined that audio device 1 and audio device 2 are both target audio devices, and the update instruction can be expressed as , C 1 S * , C 2 S * They represent the initial configuration parameters of audio device 1 and the initial configuration parameters of audio device 2 respectively.

[0106] In some embodiments, the control terminal may send the update instruction to the target audio device based on a Bluetooth device and / or a network.

[0107] In some embodiments, the target audio device may adjust and update various configuration parameters that need to be adjusted based on the received update instruction, and adjust the initial configuration parameters to the target configuration parameters.

[0108] In some embodiments, when the target configuration parameters of the target audio device in the update instruction are (0, 0, 0, 0, 0), the target audio device may adjust the configuration parameters from the initial configuration parameters to the target configuration parameters, or may automatically shut down or disconnect.

[0109] Target configuration conditions refer to the configuration conditions of a home theater that meets the user's listening needs.

[0110] In some embodiments, the target configuration condition may be that the user device is located within a target location range. If the home theater satisfies the target configuration condition, the audio equipment is configured according to the target configuration parameters, so that the user device is located within the target location range, that is, the user device can receive the best listening effect.

[0111] The target position range refers to the range of the position area where the best listening effect can be achieved. In some embodiments, the target position range may refer to a smaller range centered on the best listening position, for example, a range no more than 50 cm from the best listening position.

[0112] In some embodiments of the present specification, the relative position relationship of the control terminal, user equipment and audio equipment is obtained through Bluetooth positioning, so that the configuration parameters of the audio equipment are adjusted so that the user equipment position is within the optimal listening position range, saving the cost of manual calibration by technicians during the home theater installation process and improving the convenience of home theater installation and configuration.

[0113] Figure 4 is a flowchart of generating target configuration parameters according to some embodiments of this specification. Figure 4 As shown, the process 400 includes the following steps 410 to 440. In some embodiments, the process 400 may be executed by a processor.

[0114] In some embodiments, the processor can obtain one or more groups of candidate configuration parameters; based on historical playback data, historical reception data, scene spatial information and target position relationship, determine the spatial feature map through the feature extraction layer of the sound field determination model; for a group of candidate configuration parameters, based on the candidate configuration parameters and the spatial feature map, determine the predicted optimal positions corresponding to the candidate configuration parameters through the position prediction layer of the sound field determination model; determine the target configuration parameters based on one or more predicted optimal positions corresponding to one or more groups of candidate configuration parameters.

[0115] For more information about scene space information, target position relationships, and target configuration parameters, see Figure 1 and Figure 2 Related description.

[0116] Step 410: Obtain one or more sets of candidate configuration parameters.

[0117] Candidate configuration parameters refer to configuration parameters for candidate target configuration parameters. In some embodiments, the candidate configuration parameters may include candidate volume, candidate channel type, candidate delay, and candidate gain. For more information about candidate volume, candidate channel type, candidate delay, and candidate gain, see Figure 3 The meanings of the target volume, target channel type, target delay, and target gain are similar and will not be repeated here.

[0118] In some embodiments, the processor may randomly generate one or more sets of candidate configuration parameters within the effective range of the configuration parameters, wherein the effective range of the configuration parameters may be set by default by the processor based on historical data, or may be pre-set by the user based on historical data or prior experience.

[0119] Step 420, based on the historical playback data, the historical reception data, the scene space information and the target position relationship, the feature extraction layer of the sound field determination model is determined to determine the spatial feature map.

[0120] The historical playback data refers to the relevant data of the historical playback of the audio device. In some embodiments, the historical playback data of an audio device may include one or more historical audio files historically played by the audio device. The historical playback data may include multiple historical playback data corresponding to all audio devices in the home theater.

[0121] For more information about audio files, see Figure 3 Related description.

[0122] In some embodiments, the processor may directly obtain the historical playback data from a storage device or a storage component of the audio device.

[0123] The historical reception data refers to the relevant data of the historical reception of the sound receiving device. The sound receiving device may include but is not limited to a microphone, a microphone array, etc. In some embodiments, the sound receiving device may be installed at the audio device to facilitate the reception of the sound, and one or more sound receiving devices may be installed at one audio device.

[0124] In some embodiments, the historical reception data of a sound receiving device may include one or more historical sound receiving files of the sound receiving device. The historical sound receiving files may be waveform audio files, audio exchange files, etc. formed by converting the sound data historically received by the sound receiving device. The historical reception data may include multiple historical sound receiving data corresponding to all the sound receiving devices in the home theater.

[0125] In some embodiments, the processor can directly obtain the historical reception data from the storage device or the storage component of the radio device. For more information about the storage device, see Figure 1 Related description.

[0126] In some embodiments, historical playback data of an audio device corresponds to historical reception data of one or more audio receiving devices installed at the audio device, and the data difference between the two can be used to identify spatial features.

[0127] A spatial feature map refers to an image showing the spatial features in a scene. For example, a spatial feature map may be a three-dimensional image including the shape and size of each object in the scene space, as well as the location of each object. Among them, objects may include but are not limited to user devices, audio equipment, control terminals, furniture, etc.

[0128] In some embodiments, the processor may determine a spatial feature map through a feature extraction layer of a sound field determination model based on historical playback data, historical reception data, scene spatial information, and target position relationships.

[0129] The sound field determination model refers to a model that predicts the optimal position based on the sound field determination. In some embodiments, the sound field determination model can be a machine learning model, such as a convolutional neural network (CNN), a recurrent neural network (RNN), etc.

[0130] Figure 5 It is a schematic diagram of a sound field determination model according to some embodiments of this specification.

[0131] In some embodiments, Figure 5 As shown, the sound field determination model 520 may include a feature extraction layer 520 - 1 and a position prediction layer 520 - 2 .

[0132] The feature extraction layer 520-1 refers to a model layer structure for extracting spatial features. In some embodiments, the feature extraction layer 520-1 may be a machine learning model, such as CNN.

[0133] In some embodiments, Figure 5 As shown, the input of the feature extraction layer 520-1 may include historical playback data 510-1, historical reception data 510-2, scene space information 510-3, target position relationship 510-4 and initial configuration parameters 510-5, and the output may be a spatial feature map 530-1. For more information about the initial configuration parameters, see Figure 3 Related description.

[0134] In some embodiments, the feature extraction layer 520 - 1 and the position prediction layer 520 - 2 of the sound field determination model 520 may be separately trained and acquired.

[0135] The processor may train the feature extraction layer 520 - 1 based on multiple groups of first training samples with first training labels.

[0136] In some embodiments, a set of first training samples may include sample playback data, sample reception data, sample scene space information, sample target position relationship, and sample initial configuration parameters. The first training sample may be acquired based on historical data, and the first training label may be an actual spatial feature map corresponding to the first training sample. The first training label may be drawn and annotated by a processor and / or manually based on historical data.

[0137] In some embodiments, the processor may input the first training sample into the initial feature extraction layer, construct a first loss function based on the spatial feature map output by the initial feature extraction layer and the first training label, update the initial feature extraction layer based on the first loss function, and when the first preset condition is met, the initial feature extraction layer training is completed to obtain a trained feature extraction layer. The first preset condition may be that the first loss function converges, the number of iterations reaches a preset threshold, etc.

[0138] In some embodiments, the input of the feature extraction layer 520 - 1 of the sound field determination model 520 may also include a preset number of groups 510 - 6 of antenna arrays.

[0139] For details about antenna arrays and preset groups, see Figure 1 and its related description.

[0140] In some embodiments, the first training sample may further include a preset number of sample groups of the sample antenna array. The preset number of sample groups may be acquired based on historical data. For example, the historical actual number of groups of the antenna array. In some embodiments, the processor may train the feature extraction layer based on the first training sample including the preset number of sample groups. For the training process of the feature extraction layer, please refer to the relevant description above.

[0141] In some embodiments of the present specification, considering the preset number of antenna arrays when determining the spatial feature map is helpful in improving the positioning accuracy of the audio equipment.

[0142] Step 430 , for a set of candidate configuration parameters, based on the candidate configuration parameters and the spatial feature map, the position prediction layer of the sound field determination model is used to determine the predicted optimal position corresponding to the candidate configuration parameters.

[0143] The predicted optimal position refers to the predicted optimal listening position of the user.

[0144] In some embodiments, the processor may determine the predicted optimal position corresponding to the candidate configuration parameters based on the candidate configuration parameters and the spatial feature map by determining the position prediction layer of the model through the sound field.

[0145] The location prediction layer 520-2 refers to a model layer structure for predicting the best location. In some embodiments, the location prediction layer 520-2 may be a machine learning model, such as RNN.

[0146] In some embodiments, Figure 5 As shown, the input of the position prediction layer 520 - 2 may include a spatial feature map 530 - 1 , candidate configuration parameters 530 - 2 , audio device data 530 - 3 , and an audio file type 530 - 4 , and the output may include a predicted optimal position 540 .

[0147] The audio device data 530-3 refers to data related to the audio device itself. For example, the audio device data may include the audio device type, the audio device model, etc. In some embodiments, the processor may read the audio device data from the factory parameters of the audio device.

[0148] The audio file type 530-4 refers to the subject type of the audio file, for example, horror movies, comedy movies, family ethics TV series, etc. In some embodiments, the processor can directly read the audio file type from the audio file.

[0149] In some embodiments, the processor may train the position prediction layer 520 - 2 based on multiple groups of second training samples with second training labels.

[0150] In some embodiments, a set of second training samples may include sample space feature maps, sample candidate configuration parameters, sample audio device data, and sample audio file types. The second training samples may be obtained based on experimental data, and the second training labels may be actual optimal positions corresponding to the second training samples.

[0151] In some embodiments, the technician can obtain multiple experimental scenes by changing scene space data (e.g., changing the placement of objects), setting multiple sets of candidate configuration parameters, changing audio equipment data (e.g., changing the type of audio equipment), changing the type of audio files, etc., and determine multiple sets of second training samples based on the experimental data of the multiple experimental scenes. When collecting the second training samples, the technician can obtain the second training labels corresponding to the second training samples in a variety of ways.

[0152] For example, for a set of candidate configuration parameters, the technician can place different audio equipment at different experimental positions in the experimental scene space to obtain corresponding audio data; analyze the audio data collected by each audio equipment to determine the frequency spectrum, signal-to-noise ratio, and sound loudness of the audio data; determine the experimental position corresponding to the audio data with sound loudness within a comfortable range and the smallest frequency loss in the frequency spectrum as the actual optimal position, or determine the experimental position corresponding to the audio data with sound loudness within a comfortable range and the highest signal-to-noise ratio as the actual optimal position corresponding to the set of candidate configuration parameters; or the technician can conduct a comprehensive evaluation of the frequency spectrum, signal-to-noise ratio, and sound loudness of the audio data to determine the scores of the corresponding experimental positions, and determine the experimental position with the highest score as the actual optimal position, that is, the actual optimal position can be marked as the second training label.

[0153] The spectrum of the sound recording data can be determined by spectrum analysis, and the minimum frequency loss in the spectrum can refer to the minimum frequency components missing or severely attenuated in the spectrum. The signal-to-noise ratio can be represented by the ratio between the sound intensity of the audio file and the background noise. The comfort range refers to the range of sound loudness that the user feels comfortable with, which can be set by the processor by default or pre-entered by the user.

[0154] For another example, for a set of candidate configuration parameters, the technician can directly play the audio file in the experimental scene space, stand in different experimental positions to listen, select the experimental position with the best listening experience as the actual optimal position, and mark it as the second training label.

[0155] The process of training the position prediction layer based on the second training samples and the second training labels can refer to the process of training the feature extraction layer based on the first training samples and the first training labels above, which will not be described in detail here.

[0156] In some embodiments, the input of the position prediction layer 520 - 2 of the sound field determination model 520 may also include playback sound information 530 - 5 .

[0157] The play sound information 530-5 refers to the sound information related to the audio file. For example, the play sound information may include the original sound of the audio file, the spectrogram, etc. The original sound of the audio file may include human voice lines, music, background sound, etc. The spectrogram can be obtained by the processor performing spectrum analysis on the audio file. The spectrum analysis method includes but is not limited to fast Fourier transform, etc. For more information about audio files, please refer to Figure 3 Related description.

[0158] In some embodiments, the play sound information may be the play sound information corresponding to a complete audio file, or may be the play sound information corresponding to a segment of an audio file.

[0159] The audio file segment refers to a segment in the audio file, for example, the 1st minute 50 seconds to the 3rd minute 20 seconds, the 6th minute 30 seconds to the 11th minute 30 seconds, etc. of the audio file.

[0160] In some embodiments, the processor may determine the audio file segments in a variety of ways. For example, the audio file is segmented according to a preset length to determine a plurality of audio file segments. For another example, based on a spectrogram, the audio file is segmented at a time point when a sudden change in frequency occurs to determine a plurality of audio file segments. A sudden change in frequency may refer to a difference between a frequency at a certain time point and a frequency at a previous time point exceeding a preset difference threshold.

[0161] In some embodiments, the second training sample may further include sample playback sound information. The sample playback sound information may be acquired based on historical data or experimental data. In some embodiments, the processor may train the position prediction layer based on the second training sample including the sample playback sound information. For the training process of the position prediction layer, please refer to the relevant description above.

[0162] In some embodiments of the present specification, the playback sound information is considered when determining the predicted optimal position, so as to further improve the accuracy of the predicted optimal position determined by the model, thereby facilitating the subsequent acquisition of more accurate target configuration parameters.

[0163] Step 440 : determining target configuration parameters based on one or more predicted optimal positions corresponding to one or more sets of candidate configuration parameters.

[0164] In some embodiments, the processor may determine one or more predicted best locations within the target location range, select the predicted best location with the smallest location distance from the user device location, and determine the candidate configuration parameters corresponding to the predicted best location as the target configuration parameters. For more information about user device location and target location range, see Figure 1 and Figure 3 Related description.

[0165] In some embodiments, if all predicted optimal positions corresponding to each group of candidate configuration parameters are not within the target position range, one or more groups of candidate configuration parameters are regenerated, and steps 410-440 are repeated until a group of candidate configuration parameters whose corresponding predicted optimal positions are within the target position range is found and determined as the target configuration parameters.

[0166] In some embodiments of the present specification, a sound field determination model is used to determine a spatial feature map and predict an optimal position based on historical playback data, historical reception data, scene space information, and target position relationships. This can accurately predict the optimal position of a user device in the scene space, and further determine the target configuration parameters based on the predicted optimal position corresponding to the candidate configuration parameters. This helps to avoid errors in the target configuration parameters caused by deviations from the optimal position, thereby improving configuration efficiency and ensuring configuration quality.

[0167] In some embodiments, the processor may also perform multiple rounds of iterative updates on the parameter matrix based on a preset algorithm, and in response to satisfying the algorithm stop condition, stop the iteration, and determine the target configuration parameters. The iterative process may include: for one round of iteration, the processor may obtain the initial parameter matrix, and based on a set of initial candidate configuration parameters and a spatial feature map, determine the position prediction layer of the model through the sound field, determine the predicted optimal position corresponding to the initial candidate configuration parameters, and further determine the adaptation score of the initial candidate configuration parameters; based on the adaptation score, determine whether the algorithm stop condition is satisfied, and in response to not satisfying, update the initial parameter matrix of the iteration to generate the intermediate matrix parameters of the iteration; use the intermediate parameter matrix generated by the previous iteration as the initial parameter matrix of the next iteration, perform multiple rounds of iterations in sequence, until the number of iterations and / or the adaptation score meet the algorithm stop condition, stop the iteration, determine the target parameter matrix, and determine the target configuration parameters based on the target parameter matrix.

[0168] The preset algorithm refers to an algorithm for iteratively updating the parameter matrix. In some embodiments, the parameter matrix may include one or more groups of candidate configuration parameters. The candidate configuration parameters in the parameter matrix are all in the parameter space.

[0169] A parameter matrix refers to a matrix including one or more sets of candidate configuration parameters for one or more audio devices.

[0170] The initial parameter matrix refers to an initial parameter matrix of a round of iteration. In some embodiments, the initial parameter matrix of a round of iteration may include one or more groups of initial candidate configuration parameters.

[0171] The parameter space refers to the valid range of candidate configuration parameters. For example, the volume space can be (0dB-40dB), the channel type space can be (1, 2, 3, 4, 5), 1, 2, 3, 4, 5 represent the left channel, right channel, center channel, surround channel, stereo channel respectively, the delay space can be (0ms-200ms), the gain space can be (-6dB to +6dB), and the sound orientation space can be (45° north east to 45° north west).

[0172] In some embodiments, the candidate configuration parameters in the parameter matrix are all within the parameter space. For more information about configuration parameters, candidate configuration parameters, and valid ranges, see Figure 3 and Figure 4 Related description.

[0173] In some embodiments, the parameter space may be determined by a processor or technician based on historical data. For example, in the historical data, if the historical volume range is 0dB-40dB, the volume space may be defined as 0dB-40dB.

[0174] In some embodiments, the processor may obtain one or more sets of candidate configuration parameters and construct a parameter matrix based on the one or more sets of candidate configuration parameters. For instructions on obtaining one or more sets of candidate configuration parameters, see Figure 4 Relevant content of step 410.

[0175] Exemplarily, the parameter matrix can be expressed as:

[0176] Among them, X i =(X i1 , X i2 ,......,X ij ) is the i-th row vector of the parameter matrix, representing the i-th group of candidate configuration parameters; X ij represents each candidate configuration parameter of each audio device in the i-th group of candidate configuration parameters; j=5×(d-1)+m, represents the m-th candidate configuration parameter of the d-th audio device; d=1, 2, ..., n, respectively represent audio device 1, audio device 2, ..., audio device n, n represents the total number of audio devices in the home theater; m=1, 2, ..., 5, respectively represent candidate volume, candidate channel type, candidate delay, candidate gain, candidate audio orientation. For example, j=8, then d=2, m=3, X 1(8) Represents the candidate delay of audio device 2 in the first set of candidate configuration parameters.

[0177] Correspondingly, the initial parameter matrix of the first round of iteration can be expressed as:

[0178] In some embodiments, the processor determines the position prediction layer of the model through the sound field based on a set of initial candidate configuration parameters and the spatial feature map to obtain the predicted optimal position corresponding to the set of initial candidate configuration parameters. Figure 4 and Figure 5 Related description.

[0179] The adaptation score refers to the score of the adaptation degree of the predicted best position. In some embodiments, the adaptation score can be represented by a numerical value, and the lower the numerical value, the better the adaptation degree, that is, the closer the predicted best position is to the user equipment position (that is, the best listening position).

[0180] In some embodiments, for one round of iteration, the processor may calculate the distance between the predicted optimal position corresponding to a set of initial candidate configuration parameters and the position of the user equipment as the adaptation score of the set of initial candidate configuration parameters for the round of iteration.

[0181] The algorithm stopping condition refers to the condition used to determine whether to stop iterating the parameter matrix. In some embodiments, the algorithm stopping condition may include the iteration reaching the maximum number of iterations, the adaptation score being less than a preset score threshold, the adaptation score converging, etc.

[0182] The maximum number of iterations and the preset scoring threshold may be set by default by the processor or may be pre-set by a technician based on prior experience.

[0183] In some embodiments, in response to the algorithm stop condition not being met, the processor may update the initial parameter matrix of the round iteration and generate the intermediate matrix parameters of the round iteration. The updating method may include multiple methods. For example, the elements in the initial parameter matrix of the round iteration are updated according to a preset direction and a preset step size to generate the intermediate matrix parameters of the round iteration. Among them, the preset direction and the preset step size can be set by the processor by default or by the technician according to experience. Exemplarily, for a certain element representing the volume of a certain audio device, the preset direction is to increase, the preset step size is 1dB, etc.

[0184] For another example, the element adjustment speed of the previous iteration of this iteration is updated, the element adjustment speed of this iteration is determined, and based on the element adjustment speed of this iteration, the initial parameter matrix of this round is updated to generate the intermediate matrix parameters of this iteration. For this part, please refer to the description below.

[0185] The intermediate parameter matrix refers to the parameter matrix generated during the update iteration process. The intermediate parameter matrix may include one or more sets of intermediate candidate configuration parameters. In some embodiments, the intermediate parameter matrix generated in the previous round of iteration may be used as the initial parameter matrix for the next round of iteration.

[0186] Exemplarily, the intermediate parameter matrix of the t-th iteration, that is, the initial parameter matrix of the t+1-th iteration, can be expressed as:

[0187] Among them, t represents the number of iterations, X i (t) represents the i-th group of intermediate candidate configuration parameters in the t-th iteration.

[0188] In some embodiments, the processor may update the element adjustment speed of the previous iteration, determine the element adjustment speed of this iteration, and based on the determined element adjustment speed of this iteration, update the initial parameter matrix of this round to generate an intermediate parameter matrix of this iteration.

[0189] The element adjustment speed refers to the speed at which the elements in the parameter matrix are updated.

[0190] In some embodiments, the processor may update the element adjustment speed of the previous iteration based on the following formula (1) to determine the element adjustment speed: V ij (t+1)=wV ij (t)+c 1 r 1 (Pbest ij -X ij (t))+c 2 r 2 (Gbest j -X ij (t)) (1)

[0191] Among them, V ij (t+1) represents the initial parameter matrix of the t+1th iteration; X ij (t) element adjustment speed, V ij (t) represents the initial parameter matrix of the tth iteration, X ij (t-1) element adjustment speed, w represents the inertia weight, which is used to control the element to maintain the original speed, and has the ability to balance global search and local search, c 1 and c 2 is the acceleration constant, which represents the degree of trust in local experience and the degree of trust in global experience, w and c 1 and c 2 It can be set by the processor default or by technicians based on prior experience. 1 and r 2 is a random number, the value range can be [0, 1], Pbest i Represents X i The local optimal solution in multiple historical iterations, Pbest ij Represents X ij The local optimal solution in multiple historical iterations, an X i Corresponding to a Pbest i , an X ij Corresponding to a Pbest ij , Gbest j Represents the jth element of the global optimal solution X in multiple historical iterations. The global optimal solution X is Pbest i The optimal solution in .

[0192] In some embodiments, the processor may determine Pbest based on the adaptation score. ij and Gbest j For example, for the i-th group of candidate configuration parameters, that is, the i-th row vector X of the parameter matrix i , in multiple rounds of iterations, they are respectively X i (0), Xi (1), ..., X i (t), the processor can calculate X i (0), X i (1), ..., X i (t) respectively corresponds to multiple adaptation scores, denoted as f i (0), f i (1), ..., f i (t). For example, if f i (5) minimum, then X i (5) is X i The local optimal solution, Pbest i =X i (5) = [X i1 (5), X i2 (5), ..., X ij (5)], then Pbest ij For X ij (5).

[0193] In some embodiments, for each X i , the processor can calculate the corresponding Pbest through the above process i , determine multiple Pbest i Pbest with the smallest adaptation score i , then its corresponding X i (k) is the global optimal solution X, then Gbest j For X ij (k).

[0194] For example, determine X 1 , X 2 ,......,X i The corresponding Pbest 1 、Pbest 2 , …, Pbest i Pbest 7 Minimum, and Pbest is determined by the above steps 7 =X 7 (9) = [X 71 (9), X 72 (9), ..., X 7j (9)], that is, X 7 (9) is the global optimal solution, that is, among the multiple sets of candidate configuration parameters in multiple rounds of iterations, the adaptation score of the 7th set of intermediate candidate configuration parameters in the initial parameter matrix of the 10th round of iteration (that is, the intermediate parameter matrix of the 9th round of iteration) is the lowest, then Gbest j For X 7j (9).

[0195] In some embodiments, the processor may adjust the speed based on the elements of this round of iteration, update the initial parameter matrix of this round, and generate the intermediate parameter matrix of this round of iteration. The updating process may be implemented based on the following formula (2): X ij (t+1)=X ij (t)+V ij (t+1) (2)

[0196] Among them, X ij (t+1) represents the intermediate parameter matrix of the t+1th iteration, V ij (t+1) represents the initial parameter matrix of the t+1th iteration, X ij (t) element adjustment speed, X ij (t) represents the initial parameter matrix of the t+1th round and is also the intermediate parameter matrix of the tth round.

[0197] In some embodiments, the processor may use the intermediate parameter matrix generated by the previous round of iteration as the initial parameter matrix for the next round of iteration, and perform multiple rounds of iterations in sequence until the number of iterations and / or the adaptation score meet the algorithm stop condition, stop the iteration, and determine the target parameter matrix; the processor may determine one or more groups of candidate configuration parameters based on the target parameter matrix, and then determine the target configuration parameters based on one or more groups of candidate configuration parameters.

[0198] In some embodiments, the target parameter matrix is ​​the initial parameter matrix corresponding to the last iteration. For example, if the adaptation score of the initial parameter matrix of the 11th iteration is less than the preset score threshold, then the target parameter matrix is ​​the initial parameter matrix of the 11th iteration (also the intermediate parameter matrix of the 10th iteration), that is,

[0199] For more information about determining target configuration parameters based on one or more sets of candidate configuration parameters, see Figure 4 and Figure 5 Related description.

[0200] In some embodiments of the present specification, based on the initial parameter matrix and the spatial feature map, the predicted optimal position corresponding to the initial candidate configuration parameters is determined by the sound field determination model, and further based on the adaptation score determined by the predicted optimal position, the initial parameter matrix is ​​iterated multiple times to determine the target configuration parameters, which is beneficial to improving the accuracy of determining the target configuration parameters and optimizing the configuration effect of the home theater.

[0201] In some embodiments, in response to the configuration update condition being satisfied, the processor may redetermine the target configuration parameters.

[0202] The configuration update condition refers to a condition that requires re-determining the target configuration parameters. In some embodiments, the configuration update condition may include that the moving distance of the user equipment position exceeds a preset change distance.

[0203] In some embodiments, the movement distance caused by the user's movement may be represented by the distance between the coordinates of the user device's location before and after the movement.

[0204] In some embodiments, in response to the moving distance exceeding a preset variation distance, the processor may re-determine the target configuration parameter.

[0205] In some embodiments, the preset variation distance may be set by default by the processor or by a technician based on experience, for example, 1 meter.

[0206] In some embodiments, the processor may also determine the environmental complexity based on the spatial feature map; and determine the preset change distance based on the environmental complexity. For more information about the spatial feature map, see Figure 4 Related description.

[0207] The environmental complexity refers to the complexity of the scene space that the home theater is located in. In some embodiments, the environmental complexity can be determined by a processor based on a spatial feature map.

[0208] For example, the processor may obtain the number of objects and the space occupied by the objects in the scene space based on the spatial feature map, and determine the environmental complexity based on the number of objects and the space occupied by the objects. The space occupied by the objects may be represented by the ratio of the volume of all objects in the scene space to the volume of the scene space.

[0209] For example, the environmental complexity can be expressed by the following formula (3): R=a×S+b×T (3)

[0210] Among them, R represents the complexity of the environment, a and b represent the coefficient of the number of objects and the coefficient of the space occupied by the objects respectively, S represents the number of objects, and T represents the space occupied by the objects. a and b can be set by the processor by default or by the technician based on experience.

[0211] In some embodiments, the processor may also determine the environmental complexity based on the spatial feature map in other feasible ways, which are not limited here, such as a trained machine learning model for outputting environmental complexity.

[0212] In some embodiments, the processor may determine the environmental complexity by querying a preset table based on the environmental complexity.

[0213] The preset table may include multiple environmental complexities and corresponding preset change distances. The preset change distance is negatively correlated with the environmental complexity. The higher the environmental complexity, the smaller the preset change distance. The preset table may be determined by a technician based on historical data or historical experience.

[0214] In some embodiments of the present specification, according to the complexity of the environment, it is helpful to determine the preset change distance in a targeted manner in combination with the actual situation, thereby improving the accuracy of the configuration update conditions and the environmental adaptability, thereby accurately judging whether the target configuration parameters need to be adjusted.

[0215] In some embodiments, the configuration update condition may also include that the audio file is switched or the audio file segment of the audio file is switched from the current segment to the next segment. The processor may also determine file division information based on the audio file through a segment analysis model; and determine the audio file segment based on the file division information.

[0216] For more information about audio files and audio file segments, see Figure 3 and Figure 4 Related description.

[0217] In some embodiments, an audio file or an audio file segment corresponds to a target configuration parameter. When the audio file is switched or the audio file segment of the audio file is switched, the target configuration parameter should be re-determined. The processor can re-input the corresponding audio file type and the playback sound information after the switch into the position prediction layer of the sound field determination model to re-determine the target configuration parameter. For more information about this part, please refer to Figure 5 Related description.

[0218] The file division information refers to the division information of the audio file. In some embodiments, the file division information may be a division time period. For example, the file division information of audio file A may be 2 division time periods [(0-4 min), (4-15 min)], then audio file A is divided into 2 audio file segments, audio file segment A and audio file segment B. 1 The segment from 0 to 4 minutes in audio file A, audio file segment A 2 It is a 4 to 15 minute segment in audio file A.

[0219] In some embodiments, the processor may determine the file division information based on the audio file in a variety of ways, such as a preset division rule.

[0220] In some embodiments, the processor may determine the file division information based on the audio file by using a segment analysis model.

[0221] The fragment analysis model refers to a model used to determine file partition information. In some embodiments, the fragment analysis model can be a machine learning model, such as RNN, Long Short-Term Memory (LSTM), etc.

[0222] In some embodiments, the input of the segment analysis model may include playback sound information, audio equipment data, and spatial feature maps, and the output may include file partitioning information.

[0223] For more information about playing sound information, audio equipment data, and spatial feature maps, see Figure 4 and Figure 5 Related description.

[0224] In some embodiments, the processor may train the segment analysis model based on multiple groups of third training samples with third training labels.

[0225] In some embodiments, a set of third training samples may include sample playback sound information, sample audio equipment data, and sample space feature graphs. The third training label may be actual file division information corresponding to the third training sample.

[0226] In some embodiments, the third training sample can be obtained based on historical data, and the third training label can be annotated by the processor and / or manually. For example, the processor and / or technicians can filter out multiple audio file segments with different playback sound information (such as different background music) from a large amount of historical data, and the processor can splice several audio file segments with different playback sound information, and the splicing result is a sample audio file. Based on the historical data, the historical playback sound information, historical audio equipment data, and historical spatial feature map of the sample audio file are obtained, that is, a set of third training samples is determined; split between two adjacent audio file segments with different playback sound information to determine the actual file division information of the sample audio file, that is, determine the third training label corresponding to the third training sample.

[0227] For example, for the historical audio file segment B 1 , B 2 , B 3 The splicing is performed to form a sample audio file based on the historical audio file segment B. 1 , B 2 , B 3 Corresponding historical playback sound information, historical audio equipment data and historical spatial feature map, determine a set of third training samples. 1 , B 2 , B 3The historical playback sound information of each group is different (for example, the background music is lyric music, rock music, and narrative music), then the third training label of the third training sample of this group is [B 1 , B 2 , B 3 ]; If the historical audio file segment B 1 and B 2 The same historical playback sound information as B 1 With B 3 The historical playback sound information is different (for example, B 1 , B 2 , B 3 The background music is lyric music, lyric music, and rock music), then the third training label of the third training sample in this group is [(B 1 +B 2 ) corresponding to the time period, B 3 corresponding time period].

[0228] The training process of the fragment analysis model is similar to the training process of the feature extraction layer or the position prediction layer. Figure 5 The relevant description will not be repeated here.

[0229] In some embodiments, the processor may divide the audio file into a plurality of audio file segments based on the file division information.

[0230] In some embodiments of the present specification, updating the target configuration parameters according to the switching of the audio file or the switching of the audio file segment of the audio file from the current segment to the next segment is conducive to timely updating of the target configuration parameters, thereby improving the user's listening experience and reducing the number of manual adjustments to the target configuration parameters.

[0231] In some embodiments, the processor can re-determine the predicted optimal position based on the updated playback file type and playback sound information, through the position prediction layer of the sound field determination model, and re-determine the target configuration parameters based on the re-determined predicted optimal position. For the specific process of this part, please refer to Figure 4 and Figure 5 Related description.

[0232] In some embodiments, in response to an audio file being switched or an audio file segment of an audio file being switched from a current segment to a next segment, the processor may pre-determine the target configuration parameters. For example, at a first moment, the processor acquires an audio file to be played at a second moment, and may replace the playback sound information and audio file type in the position prediction layer of the current sound field determination model with the audio file type and playback sound information of the audio file to be played, so as to output the predicted optimal position of the audio file to be played, so as to pre-determine the target configuration parameters. When the home theater starts playing or is about to play the audio file to be played, that is, at the second moment or when the second moment is about to be reached, the processor may directly send the pre-determined target configuration parameters to the audio equipment. The first moment is before the second moment.

[0233] In some embodiments, the configuration update condition being satisfied may include the moving distance exceeding the preset change distance, the audio file being switched, or the audio file segment of the audio file being switched from the current segment to the next segment, etc. If the moving distance of the user device position exceeds the preset change distance due to the audio file being switched or the audio file segment of the audio file being switched from the current segment to the next segment, the processor still needs to re-determine the target configuration parameters after the processor re-determines the target configuration parameters. The opposite is also true, that is, if any configuration update condition is satisfied, the target configuration parameters need to be re-determined.

[0234] In some embodiments of the present specification, in response to the configuration update condition being met, the processor re-determines the target configuration parameters, which is conducive to timely updating the audio device configuration, thereby improving the playback effect of the playback file and bringing a better listening experience to the user.

[0235] One or more embodiments of the present specification provide a home theater configuration device, the device comprising at least one memory and at least one processor, the at least one memory being used to store computer instructions, and the at least one processor executing the computer instructions or part of the instructions to implement a home theater configuration method.

[0236] One or more embodiments of the present specification provide a computer-readable storage medium, wherein the storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes a home theater configuration method.

[0237] The embodiments of this specification are only for illustration and description, and do not limit the scope of application of this specification. For those skilled in the art, various modifications and changes that can be made under the guidance of this specification are still within the scope of this specification.

[0238] Furthermore, certain features, structures or characteristics in one or more embodiments of this specification may be appropriately combined.

[0239] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0240] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0241] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0242] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this specification are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the contents of this specification, documents that limit the broadest scope of the claims of this specification (currently or later attached to this specification) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the materials attached to this specification are inconsistent or conflicting with the contents described in this specification, the descriptions, definitions, and / or use of terms in this specification shall prevail.

[0243] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A method for configuring a home theater, characterized in that: The home theater comprises a control terminal, a user device and one or more audio devices, wherein the control terminal, the user device and the audio device are equipped with a Bluetooth device, and the method comprises: Acquiring a target position relationship based on the Bluetooth device, wherein the target position relationship includes a relative position relationship among the control terminal, the user device, and the audio device; Based on the target position relationship, generating target configuration parameters of the audio device; An update instruction is generated based on the target configuration parameters, and the update instruction is sent to the target audio device for execution, so that the target audio device updates its parameters with the target configuration parameters so that the home theater meets the target configuration conditions; the target audio device is the audio device that needs to be updated with parameters; the target configuration condition is that the user device location is within the target location range.

2. The method according to claim 1, characterized in that The control terminal includes a preset number of antenna arrays, and the preset number is not less than 3.

3. The method according to claim 2, characterized in that The method further comprises: The preset number of groups is determined based on scene space information and user accuracy requirements.

4. The method according to claim 1, characterized in that The generating the target configuration parameters of the audio device based on the target position relationship includes: Obtain one or more sets of candidate configuration parameters; Based on the historical playback data, the historical reception data, the scene space information and the target position relationship, a spatial feature map is determined through a feature extraction layer of a sound field determination model; the sound field determination model is a machine learning model; For a set of candidate configuration parameters, based on the candidate configuration parameters and the spatial feature map, determining the predicted optimal position corresponding to the candidate configuration parameters through the position prediction layer of the sound field determination model; The target configuration parameters are determined based on the one or more predicted optimal positions corresponding to the one or more sets of candidate configuration parameters.

5. The method according to claim 1, characterized in that The method further comprises: In response to a configuration update condition being met, the target configuration parameter is re-determined; the configuration update condition includes that the moving distance of the user equipment position exceeds a preset change distance.

6. A home theater configuration system, characterized in that: The home theater comprises a control terminal, a user device and one or more audio devices, wherein the control terminal, the user device and the audio device are equipped with a Bluetooth device, and the system comprises: A position relationship acquisition module, configured to acquire a target position relationship based on the Bluetooth device, wherein the target position relationship includes a relative position relationship among the control terminal, the user equipment, and the audio equipment; A parameter generation module, configured to generate target configuration parameters of the audio device based on the target position relationship; The instruction execution module is configured to generate an update instruction based on the target configuration parameters, and send the update instruction to the target audio device for execution, so that the target audio device is updated with the target configuration parameters so that the home theater meets the target configuration conditions; the target audio device is the audio device that needs to be updated with parameters; the target configuration condition is that the user device location is within the target location range.

7. The system according to claim 6, characterized in that The parameter generation module is further configured to: Obtain one or more sets of candidate configuration parameters; Based on the historical playback data, the historical reception data, the scene space information and the target position relationship, a spatial feature map is determined through a feature extraction layer of a sound field determination model; the sound field determination model is a machine learning model; For a set of candidate configuration parameters, based on the candidate configuration parameters and the spatial feature map, determining the predicted optimal position corresponding to the candidate configuration parameters through the position prediction layer of the sound field determination model; The target configuration parameters are determined based on the one or more predicted optimal positions corresponding to the one or more sets of candidate configuration parameters.

8. The system according to claim 6, characterized in that The system further comprises a parameter redetermining module, wherein the parameter redetermining module is configured to: In response to satisfying a configuration update condition, the target configuration parameter is re-determined; the configuration update condition includes that the moving distance of the user equipment position exceeds a preset change distance.

9. A home theater configuration device, characterized in that: The device includes at least one memory and at least one processor, wherein the at least one memory is used to store computer instructions, and the at least one processor executes the computer instructions or part of the instructions to implement the home theater configuration method according to any one of claims 1-5.

10. A computer-readable storage medium, characterized in that: The storage medium stores computer instructions. When a computer reads the computer instructions, the computer executes the home theater configuration method according to any one of claims 1 to 5.

Images