Sound field calibration device, remote controller and sound system

Abstract

The invention relates to the field of intelligent sound production equipment, in particular to a sound field calibration device, a remote controller and a sound system. The device is applied to sound equipment control equipment and comprises a sound acquisition module used for acquiring sound signals in a sound listening environment; the pose acquisition module is used for acquiring pose information of the sound equipment control equipment in the sound listening environment; the sound field calibration module is in communication connection with the sound acquisition module and the pose acquisition module and is used for generating sound field calibration parameters according to the sound signals and the pose information; the adaptive learning module is in communication connection with the sound field calibration module and the pose acquisition module and is used for establishing or learning a sound field calibration model based on the pose information and the sound field calibration parameters of the sound equipment control equipment, and the sound field calibration model is used for outputting sound field calibration parameters based on the pose information; and the communication module is in communication connection with the sound field calibration module and the adaptive learning module and is used for sending the sound field calibration parameters to the sound equipment.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of intelligent sound production devices, and more particularly, to a sound field calibration apparatus, a remote controller and a sound system. BACKGROUND

[0002] In the field of sound field calibration technology of a sound system, the traditional calibration method is usually a complex and time-consuming process. The user needs to use an independent professional test microphone and special software to place the microphone in the listening area as required, and a series of test sounds are played by the sound system to collect and analyze the impulse response, frequency characteristics and other data of the room, and finally generate calibration parameters. The whole process has many steps, requires user intervention and cooperation, and the complete process needs to be repeated every time. Even some simplified solutions integrated into smart devices cannot avoid the complete signal collection and analysis process due to their single and static measurement mode, resulting in cumbersome calibration operation, poor experience, and users often give up using or rarely use the function because they are afraid of the trouble. SUMMARY

[0003] An object of the present disclosure is to provide a sound field calibration apparatus that significantly improves the efficiency of sound field calibration, improves the effect of audio output, and enhances user experience.

[0004] According to a first aspect of the present disclosure, a sound field calibration apparatus is provided, comprising: a sound collection module for collecting sound signals in a listening environment; a pose acquisition module for acquiring pose information of the sound control device in the listening environment; a sound field calibration module in communication connection with the sound collection module and the pose acquisition module, for generating sound field calibration parameters according to the sound signals and the pose information; an adaptive learning module in communication connection with the sound field calibration module and the pose acquisition module, for establishing or learning a sound field calibration model based on the pose information of the sound control device and the sound field calibration parameters, the sound field calibration model being used for outputting sound field calibration parameters based on the pose information; a communication module in communication connection with the sound field calibration module and the adaptive learning module, for sending the sound field calibration parameters to a sound device.

[0005] According to a second aspect of the present disclosure, a remote controller is provided, comprising the apparatus of any one of the first aspect.

[0006] According to a third aspect of the present disclosure, a sound system is provided, comprising: the remote controller of the second aspect; at least one sound device in communication connection with the communication module, for receiving the sound field calibration parameters and adjusting audio output according to the sound field calibration parameters.

[0007] One technical effect of the present disclosure is to provide a sound field calibration device that improves calibration efficiency through an adaptive learning module. The module can continuously establish or learn a sound field calibration model based on historical pose information and corresponding sound field calibration parameters that have been verified as valid by the sound field calibration module. Once the model is established, when the pose acquisition module again identifies that the device is in a learned or similar pose, the adaptive learning module can immediately infer the adaptive sound field calibration parameters through the model and send them to the sound equipment through the communication module. This eliminates the lengthy process of having to re-collect complete sound signals and complex analysis every time in the traditional method, so that repeated calibration or calibration for common use scenarios can be completed in a very short time, achieving "one-key fast optimization", and significantly improving user convenience and willingness to use.

[0008] Other features and advantages of the embodiments of the present disclosure will be apparent from the following detailed description of exemplary embodiments thereof, taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0009] The accompanying drawings, which constitute a part of this specification, illustrate embodiments of the present disclosure and together with the description, explain the principles of the embodiments of the present disclosure.

[0010] Figure 1 is a schematic diagram of a sound field calibration device according to one embodiment; Figure 2 is a schematic diagram of a sound box system according to one embodiment. DETAILED DESCRIPTION

[0011] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. Note that the relative arrangement, numerical expressions, and numerical values of components and steps set forth in these embodiments are not limiting to the scope of the present disclosure unless otherwise specifically stated.

[0012] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way limiting to the scope of the application and its applications or uses.

[0013] Techniques and equipment known to those of ordinary skill in the relevant art can not be discussed in detail, but should be considered part of the specification where appropriate.

[0014] In all examples shown and discussed herein, any specific values should be interpreted as merely illustrative, and not as a limitation. Thus, other examples of the exemplary embodiments can have different values.

[0015] It should be noted that like reference numerals and characters refer to like elements throughout the following description and the claims attached hereto. Therefore, once any certain element is defined in one drawing, it is not necessary to discuss it further in the subsequent drawings.

[0016] It should be noted that the collection, storage, use, processing, transmission, provision, disclosure, deletion, etc. of data in the present disclosure are carried out in compliance with the data protection regulations of the place of residence and with the full authorization of the corresponding data owner.

[0017] The embodiments of the present application disclose a sound field calibration device 100, which is applied to a sound control equipment, such as Figure 1 As shown, it comprises a sound collection module 101 for collecting sound signals in a listening environment; a pose acquisition module 102 for acquiring pose information of the sound control equipment in the listening environment; an adaptive learning module 104 in communication connection with the sound field calibration module and the pose acquisition module, for establishing or learning a sound field calibration model based on the pose information of the sound control equipment and the sound field calibration parameters, the sound field calibration model being used for outputting the sound field calibration parameters based on the pose information; and a communication module 105 in communication connection with the sound field calibration module and the adaptive learning module, for sending the sound field calibration parameters to the sound equipment.

[0018] In the present example, the sound control equipment is in the form of a remote controller, a smart phone, etc., through which the user operates the sound equipment and enjoys an enhanced auditory experience. The sound control equipment referred to herein is a portable handheld terminal. The sound collection module can be a high-sensitivity microphone or a similar sound sensor. This module is responsible for capturing the original sound signals in the listening environment, including the test sound played by the sound equipment, the environmental noise, and the comprehensive effect of the sound after reflection and superposition in the room. For example, when the calibration program is started, the sound equipment will play a frequency scanning signal, and the sound collection module will record these sounds in real time and convert them into electrical signals for subsequent processing and analysis. In this way, the module can obtain detailed information such as the frequency, amplitude, and phase of the sound, laying a foundation for accurately evaluating the characteristics of the room sound field.

[0019] The pose acquisition module is used to determine the real-time position and attitude of the sound control device in the listening environment. The pose information includes the three-dimensional coordinates of the device in space and its pitch, yaw and roll angles, etc. This module can be realized by a combination of various sensors. For example, a satellite positioning receiver provides the approximate geographical position; an inertial measurement unit continuously detects the acceleration and angular velocity changes of the device, thereby calculating the attitude angle and relative displacement; in indoor environments, an ultra-wideband positioning module or a Bluetooth beacon network can be used to obtain more accurate indoor coordinates. When the user holds the device, the pose acquisition module continuously outputs its position and orientation data. For example, it can be determined whether the device is located in the center of the room, whether it is facing the main speaker array, or whether it is held at an angle. These information provides the necessary reference for the subsequent spatial correlation of sound signals.

[0020] The sound field calibration module is in communication connection with the sound acquisition module and the pose acquisition module. This module receives the sound signals provided by the sound acquisition module and the pose information provided by the pose acquisition module. It analyzes and calculates in combination with these two types of information. It evaluates the acoustic characteristics of the listening environment at the position point according to the sound signals associated with a specific spatial position and attitude. Based on this analysis, the module generates a set of control instructions for adjusting the output characteristics of the sound equipment, which are the sound field calibration parameters.

[0021] For example, these parameters can include gain compensation for each channel volume, equalization adjustment for different frequency bands of sound, or fine-tuning of the sound delay time for multiple speakers. The purpose is to optimize the output of the sound system for the specific position where the device is currently located, in order to obtain a sound effect that is more in line with the target listening experience at that point.

[0022] The adaptive learning module is connected with the sound field calibration module and the pose acquisition module, respectively. This module receives and records the pose information from the pose acquisition module and the sound field calibration parameters generated by the sound field calibration module for this pose. By accumulating and analyzing multiple sets of "pose information-sound field calibration parameter" corresponding relationship data, this module performs a learning process, the goal of which is to build a calculation model that describes the internal relationship between the pose information and the adapted sound field calibration parameters, i.e. the sound field calibration model. Once the model is established or trained to a usable state, when the pose acquisition module provides a new pose information again, the adaptive learning module can directly call this sound field calibration model to calculate and output the corresponding sound field calibration parameters according to the input pose information, without the need to start the complete sound acquisition and calibration analysis process every time. This enables the system to quickly and individually optimize the sound field for repeated or similar listening positions and attitudes based on historical experience.

[0023] The communication module is connected with the sound field calibration module and the adaptive learning module respectively. The module receives the sound field calibration parameters generated by the sound field calibration module or the adaptive learning module. The core function of the module is to send the received sound field calibration parameters to the designated sound equipment through wireless communication. For example, the communication module can integrate Bluetooth, Wi-Fi or infrared emission unit. When the module obtains a set of sound field calibration parameters, the parameter data packet is sent to the sound system paired with it through the established communication link. After the sound equipment receives these parameters, it will adjust the settings of the internal audio processing circuit accordingly, so as to apply the new sound field calibration effect.

[0024] In this example, a sound field calibration device is provided, and the calibration efficiency is improved through the adaptive learning module. The module can continuously establish or learn the sound field calibration model based on the historical pose information and the sound field calibration parameters verified by the sound field calibration module. Once the model is established, when the pose acquisition module identifies that the device is in the learned or similar pose again, the adaptive learning module can directly infer the adaptive sound field calibration parameters through the model and send them to the sound equipment through the communication module. This saves the long process of re-acquiring complete sound signals and complex analysis in the traditional method every time, so that the repeated calibration or calibration for common use scenarios can be completed in a very short time, realizing "one-key fast optimization", and significantly improving the convenience and use willingness of users.

[0025] In one example of the embodiment, the sound field calibration module has a first working mode and a second working mode; in the first working mode, the sound field calibration module calls the sound field calibration model established in the adaptive learning module based on the current pose information provided by the pose acquisition module, generates and outputs the sound field calibration parameters to the communication module; in the second working mode, the sound field calibration module performs sound field characteristic analysis based on the sound signals collected by the sound acquisition module and the pose information provided by the pose acquisition module to generate sound field calibration parameters, and sends the sound field calibration parameters to the adaptive learning module for establishing or updating the sound field calibration model.

[0026] In one example of the embodiment, in the second working mode, the sound field calibration module is further used to output the sound field calibration parameters to the communication module after performing sound field characteristic analysis to generate sound field calibration parameters, so that the communication module sends the sound field calibration parameters to the sound equipment.

[0027] In the first mode, the module initiates a request to the adaptive learning module and calls the internal sound field calibration model built in the adaptive learning module directly according to the current pose information provided by the pose acquisition module in real time. The model calculates according to the input pose information and directly outputs the sound field calibration parameters matched therewith. Subsequently, the sound field calibration module forwards the parameters to the communication module. This mode is usually used for listening positions or poses that have been learned by the system, and can achieve instantaneous response and fast calibration.

[0028] In the second mode, the workflow of the sound field calibration module is different. It receives real-time sound signals from the sound acquisition module and current pose information from the pose acquisition module at the same time. The module first performs a complete sound field characteristic analysis on the sound signals, integrates the current pose information, and generates a new set of sound field calibration parameters through calculation. The parameters are sent to the communication module for application by the sound equipment, and the newly generated parameters are sent to the adaptive learning module together with the pose information that triggered it. The adaptive learning module uses these new "pose-parameter" pairs to establish an initial sound field calibration model or update and optimize the existing model. This mode is mainly used for initial calibration, recalibration after a significant change in the environment, or to collect training data for the learning model.

[0029] In one example of the embodiment, the adaptive learning module includes a device characteristic learning unit configured to: interact with the sound equipment through the communication module to obtain device performance parameters of the sound equipment; and build and maintain a device parameter database based on the obtained device performance parameters. When establishing or learning the sound field calibration model, the adaptive learning module is configured to call the parameters in the device parameter database to generate sound field calibration parameters matched with the sound equipment.

[0030] In this example, the adaptive learning module includes a device characteristic learning unit. The unit can establish bidirectional data interaction with the sound equipment through the communication module to obtain device performance parameters of the sound equipment. These parameters describe the inherent acoustic and electrical characteristics of the sound equipment, such as the equalizer center frequency and bandwidth supported by the device, the maximum sound pressure level that each channel can achieve, the delay time range allowed by each speaker unit, etc.

[0031] The device characteristic learning unit is responsible for collecting and managing this information, building and maintaining a dedicated device parameter database. When the adaptive learning module performs its core function—building or learning a sound field calibration model—it calls upon the information stored in this device parameter database. By combining environmental, pose, and other information with the specific performance parameters of the audio equipment, the adaptive learning module ensures that the final sound field calibration parameters it generates or that are output by the model are within the actual performance boundaries and capabilities of the target audio equipment, thereby generating a matching and effectively executable calibration scheme.

[0032] In one example of this embodiment, the adaptive learning module further includes a user habit learning unit, which is used to: record the sound field adjustment operations performed by the user on the audio device through the audio control device; generate and update the user sound quality preference model based on the recorded sound field adjustment operations; wherein, the user habit learning unit is configured to provide the user sound quality preference model to the adaptive learning module for incorporating user preferences when establishing or learning the sound field calibration model.

[0033] In this embodiment, the adaptive learning module may further include a user habit learning unit. This unit is responsible for recording the user's active interventions and adjustments to the sound effects output by the audio equipment when using the audio control device. These interventions are collectively referred to as sound field adjustment operations, which may specifically include manually adjusting the gain of each frequency band of the equalizer, changing the volume balance between each channel, switching preset sound field modes, or fine-tuning the automatic calibration results. The user habit learning unit continuously records the specific content of these operations and the context in which they occur. By analyzing this historical operation data, the unit can summarize the user's stable preferences for sound characteristics, such as a preference for stronger bass, brighter treble, or a wider sound field. Based on these analyses, the unit generates and continuously updates an abstract user sound quality preference model, which mathematically represents the user's personalized listening preferences.

[0034] When the adaptive learning module builds or optimizes the sound field calibration model, the user habit learning unit provides its maintained user sound quality preference model to the main learning process. This allows the final sound field calibration model to not only adapt to environmental and device characteristics, but also automatically incorporate and reflect the user's personal hearing preferences when outputting calibration parameters, thereby achieving a highly personalized sound calibration effect.

[0035] In one example of this embodiment, the user habit learning unit is further configured to: distinguish and identify different usage habit patterns based on the pose data acquired by the pose acquisition module; associate the sound field adjustment operation with the usage habit pattern to establish a user sound quality preference model corresponding to different users; wherein, the adaptive learning module is configured to determine the target user based on the currently identified usage habit pattern, and provide the user sound quality preference model corresponding to the target user to the adaptive learning module for incorporating the target user's preference when establishing or learning the sound field calibration model.

[0036] User habit learning analyzes continuously acquired pose data to identify statistically significant, recurring combinations of device position and posture. These stable combinations are categorized into different usage patterns. For example, the system might identify two main patterns: one corresponding to the device being frequently placed in a fixed position and in a horizontal posture; and the other corresponding to the device being frequently held at a specific height and tilted at a specific angle. Each identified usage pattern is typically associated with a specific primary user or a typical usage scenario.

[0037] The user habit learning unit associates and categorizes each recorded sound field adjustment operation with the corresponding usage habit pattern. In this way, the unit can build and maintain an independent user sound quality preference model for each identified usage habit pattern. These models each accumulate and reflect the personalized adjustment history and sound quality preferences of the user corresponding to that pattern.

[0038] When the audio system is running, the adaptive learning module can quickly match and identify the currently active usage pattern based on the real-time pose information provided by the pose acquisition module, thereby determining the target user. Subsequently, the module calls upon the user's corresponding sound quality preference model and incorporates its personalized preference parameters into the establishment or learning process of the current sound field calibration model. This ensures that the final calibration parameters accurately match the actual user's auditory preferences.

[0039] In one example of this embodiment, the adaptive learning module further includes an environmental change learning unit, which is used to: monitor at least one environmental parameter in the listening environment; and dynamically adjust the sound field calibration model according to the changes in the monitored environmental parameters. The environmental change learning unit is configured to trigger the adaptive learning module to update the sound field calibration model when the change in the environmental parameter exceeds a preset threshold, so as to generate sound field calibration parameters adapted to the environmental changes.

[0040] In this embodiment, the adaptive learning module may further include an environmental change learning unit, which continuously monitors physical conditions in the listening environment that may affect sound propagation and hearing. These monitored conditions are collectively referred to as environmental parameters, and may specifically include, but are not limited to, the sound pressure level and spectrum of ambient background noise, air temperature and humidity, and the movement of people or large objects within the space. Based on the continuous data of the monitored environmental parameters, the established sound field calibration model is dynamically fine-tuned or compensated. Its working principle is that changes in environmental parameters lead to changes in the acoustic characteristics of the room; for example, changes in temperature and humidity affect the speed of sound propagation, and an increase in the number of people changes the sound absorption characteristics of the room. This unit learns the correlation between environmental parameters and changes in acoustic characteristics, enabling the sound field calibration model to adapt to these slow environmental drifts.

[0041] To achieve an effective response, this unit is configured with preset thresholds. When the monitoring system detects that the magnitude or rate of change of one or more key environmental parameters exceeds their corresponding preset thresholds—for example, a sudden and significant increase in ambient noise or a significant drop in temperature within a short period—the environmental change learning unit determines that the current environment has undergone a substantial change. At this point, it will trigger a notification to the adaptive learning module to initiate an update process for the sound field calibration model. Through this update, the system can generate a set of sound field calibration parameters suitable for the new state following the environmental change, thereby maintaining the accuracy and robustness of the calibration effect.

[0042] In one example of this embodiment, a user interaction module is also included. The user interaction module is communicatively connected to the adaptive learning module and is used to: receive user feedback information on the sound field calibration results; and send the feedback information to the adaptive learning module. The adaptive learning module is configured to optimize the sound field calibration model or the user sound quality preference model based on the feedback information.

[0043] In this example, the user interaction module serves as the interface through which the user directly communicates with the entire sound field calibration system; it is connected to the adaptive learning module. This module collects the user's subjective evaluation of the sound field calibration effect. After the calibration process is complete, the user interaction module can present the calibration status to the user through its interface (such as a combination of a display screen and buttons on a remote control) and prompt the user to provide feedback. The user can submit feedback on the current sound effect by pressing specific "Satisfied" or "Dissatisfied" buttons, or by selecting a rating slider. Upon receiving this explicit feedback, the user interaction module converts it into a standardized data format and sends it to the adaptive learning module.

[0044] The adaptive learning module uses this feedback as a crucial basis for optimization. For example, when receiving "satisfactory" feedback, the module strengthens the correlation between the current calibration parameters and their corresponding conditions, making the model more inclined to output such parameters under similar conditions. Conversely, when receiving "unsatisfactory" feedback, the module records this calibration failure case, which may trigger a recalibration process. It then uses this result to adjust its internal sound field calibration model or user sound quality preference model, such as reducing the weight of certain parameters or correcting biases in the user preference model. Through this closed loop of "execution-feedback-optimization," the system can continuously improve the accuracy and personalization of its calibration scheme.

[0045] In a complete embodiment, during the initial learning phase of first-time use, after the user starts the device, the adaptive learning module automatically discovers and scans nearby connectable audio devices through the communication module, acquiring their device performance parameters to establish an initial device parameter database. Subsequently, the user is guided to perform a complete initial sound field calibration. During this process, the sound acquisition module and pose acquisition module work together to collect spatial sound field data of the current listening position, and the sound field calibration module generates and applies a basic calibration scheme based on this data. Simultaneously, the user interaction module guides the user to input preliminary sound quality preferences, and the user habit learning unit builds an initial user sound quality preference model based on this information.

[0046] Once in the continuous learning and dynamic calibration phase, the device operates multiple learning mechanisms in the background. When a new audio device is added, the device characteristic learning unit automatically collects its parameters and updates the database, achieving plug-and-play adaptation. The user habit learning unit continuously records every active sound field adjustment operation by the user. When it identifies repeated occurrences of the same or similar adjustment patterns, it automatically integrates these adjustment tendencies into the corresponding user's sound quality preference model, achieving iterative evolution of the model. The environmental change learning unit monitors environmental parameters in real time. Once a change is detected exceeding a threshold, it triggers the adaptive learning module to fine-tune the sound field calibration model to compensate for the impact of environmental changes. In addition, based on the high-frequency usage periods recorded by the user habit learning unit, more in-depth calibration analysis can be automatically performed during off-peak usage periods to update the sound field optimization scheme, thereby avoiding interference with normal user operation.

[0047] During the calibration feedback and optimization phase, after each calibration, the user interaction module proactively solicits feedback from the user, such as providing satisfaction selection options on the display screen. The user-submitted feedback is immediately sent to the adaptive learning module. Based on the feedback, this module optimizes and adjusts the relevant sound field calibration model or user sound quality preference model, thus forming a closed-loop learning system of "execution-evaluation-optimization".

[0048] Through the above process, the sound field calibration module and the adaptive learning module are deeply integrated, enabling the continuous construction and optimization of a highly personalized sound field calibration model. This allows the system to quickly output the most suitable sound field optimization parameters based on the real-time detected device pose, greatly improving the ease of operation and the final user listening experience.

[0049] This application also provides a remote control, including any of the sound field calibration devices in the sound field calibration device embodiments.

[0050] like Figure 2 As shown, an audio system 200 includes any of the remote controllers 201 in the remote controller embodiments, and at least one audio device 202. The audio device 202 is communicatively connected to a communication module for receiving sound field calibration parameters and adjusting the audio output according to the sound field calibration parameters.

[0051] The various embodiments in this disclosure are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device and apparatus embodiments are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0052] The foregoing has described specific embodiments of this disclosure. In some cases, the described actions or steps may be performed in a different order than those shown in the embodiments and the desired results may still be achieved. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0053] Embodiments of this disclosure may be systems, methods, and / or computer program products. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of the embodiments of this disclosure.

[0054] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0055] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0056] Computer program instructions used to perform the operations of embodiments of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on a user's computer, partially on a user's computer, as a standalone software package, partially on a user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays, may execute the computer-readable program instructions to implement various aspects of embodiments of this disclosure by utilizing state information from the computer-readable program instructions.

[0057] Various aspects of embodiments of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0058] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0059] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0060] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions. It will be known to those skilled in the art that implementation in hardware, implementation in software, and implementation in a combination of software and hardware are equivalent.

[0061] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, and are not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. An acoustic field calibration device, characterized in that, Applied to a sound control device, comprising: a sound collection module configured to collect a sound signal in a listening environment; a pose acquisition module configured to acquire pose information of the sound control device in the listening environment; a sound field calibration module in communication connection with the sound collection module and the pose acquisition module, configured to generate a sound field calibration parameter according to the sound signal and the pose information; an adaptive learning module in communication connection with the sound field calibration module and the pose acquisition module, configured to establish or learn a sound field calibration model based on the pose information of the sound control device and the sound field calibration parameter, the sound field calibration model being configured to output the sound field calibration parameter based on the pose information; a communication module in communication connection with the sound field calibration module and the adaptive learning module, configured to send the sound field calibration parameter to a sound device.

2. The apparatus of claim 1, wherein, The sound field calibration module has a first working mode and a second working mode; in the first working mode, the sound field calibration module calls the sound field calibration model established in the adaptive learning module based on the current pose information provided by the pose acquisition module, generates and outputs the sound field calibration parameter to the communication module; in the second working mode, the sound field calibration module performs sound field characteristic analysis based on the sound signal collected by the sound collection module and the pose information provided by the pose acquisition module to generate the sound field calibration parameter, and sends the sound field calibration parameter to the adaptive learning module for establishing or updating the sound field calibration model.

3. The apparatus of claim 1, wherein, In the second working mode, the sound field calibration module is further configured to output the sound field calibration parameter to the communication module after performing sound field characteristic analysis to generate the sound field calibration parameter, so that the communication module sends the sound field calibration parameter to the sound device.

4. The apparatus of claim 1, wherein, The adaptive learning module includes a device characteristic learning unit, which is configured to: interact with the sound device through the communication module to obtain device performance parameters of the sound device; construct and maintain a device parameter database based on the obtained device performance parameters; wherein, when establishing or learning the sound field calibration model, the adaptive learning module is configured to call parameters in the device parameter database to generate sound field calibration parameters matched with the sound device.

5. The apparatus of claim 1 or 4, wherein, The adaptive learning module further includes a user habit learning unit, which is configured to: record sound field adjustment operations of the user on the sound device through the sound control device; generate and update a user sound quality preference model according to the recorded sound field adjustment operations; wherein, the user habit learning unit is configured to provide the user sound quality preference model to the adaptive learning module for incorporating user preferences when establishing or learning the sound field calibration model.

6. The apparatus of claim 5, wherein, The user habit learning unit is further configured to: distinguish and identify different use habit modes based on the pose data acquired by the pose acquisition module; associate the sound field adjustment operation with the use habit mode to establish the user sound quality preference model corresponding to different users; The adaptive learning module is configured to determine a target user based on a currently identified usage habit pattern, and provide a user timbre preference model corresponding to the target user to the adaptive learning module for incorporating the preference of the target user when establishing or learning the sound field calibration model.

7. The apparatus of any one of claims 1, 4-6, wherein, The adaptive learning module further comprises an environment change learning unit, which is configured to: monitor at least one environmental parameter in the listening environment; adjust the sound field calibration model dynamically according to the change of the monitored environmental parameter; The environment change learning unit is configured to trigger the adaptive learning module to start updating the sound field calibration model to generate sound field calibration parameters adapted to the change of the environment when the change of the environmental parameter exceeds a preset threshold.

8. The apparatus of claim 5, wherein, Further comprising a user interaction module, which is in communication connection with the adaptive learning module, and is configured to: receive feedback information of the user on the sound field calibration result; send the feedback information to the adaptive learning module; The adaptive learning module is configured to optimize the sound field calibration model or the user timbre preference model according to the feedback information.

9. A remote controller, characterized by comprising: The device comprises any one of claims 1-8.

10. A sound system, characterized by It comprises: The remote controller of claim 9; at least one sound equipment, which is in communication connection with the communication module, and is configured to receive the sound field calibration parameters and adjust audio output according to the sound field calibration parameters.

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