CN113115176A

CN113115176A - Sound parameter determination method and system

Info

Publication number: CN113115176A
Application number: CN202110505751.9A
Authority: CN
Inventors: 陈玮; 张鲲鹏
Original assignee: Hansong Nanjing Technology Co ltd
Current assignee: Hansong Nanjing Technology Co ltd
Priority date: 2020-12-28
Filing date: 2020-12-28
Publication date: 2021-07-13
Anticipated expiration: 2040-12-28
Also published as: CN113207059A; CN112312278B; CN113207061A; CN113207059B; CN113115176B; CN113207062A; CN113115177A; CN113207060A; CN113207060B; CN112312278A; CN113194384A

Abstract

The embodiment of the specification discloses a sound position determining method, which comprises the following steps: acquiring a panoramic image of a target space for placing a target sound, wherein the target sound is provided with an LED lamp, and the panoramic image is obtained by shooting under the condition that the LED lamp is turned on; analyzing the panoramic image to determine the position of the target sound; wherein the analyzing the panoramic image comprises: and identifying the position of the target sound by identifying the shape or the position of the LED lamp of the target sound in the panoramic image.

Description

Sound parameter determination method and system

Description of the cases

The application is a divisional application of Chinese patent application CN 202011572703.3 entitled "a sound parameter determination method and system" filed on 28.12.2020.

Technical Field

The present disclosure relates to the field of audio, and in particular, to a method and a system for determining audio parameters.

Background

With the rapid development of the offline service industry, the application of the sound equipment is more and more extensive. When the device is used, a user can adjust the sound parameters of the sound to obtain better listening experience. However, most audio systems have only basic adjustment functions, and various influencing factors, such as environmental factors, position and angle factors and the like of the audio system, are not considered when determining audio parameters. In addition, most users do not have the speciality of self-adjusting sound parameters and the specialized listening environment for accurately determining the sound parameters. The sound parameters are determined insufficiently comprehensively and accurately, and a better sound effect is difficult to obtain, so that the listening experience of a user is influenced.

Therefore, it is desirable to provide a method and a system for determining sound parameters, which improve comprehensiveness and accuracy of sound parameter determination, improve sound effect of a sound, and provide a high-quality listening experience for a user.

Disclosure of Invention

One of the embodiments of the present specification provides a sound parameter determination method, including: acquiring a panoramic image of a target space for placing a target sound, wherein the target sound is provided with an LED lamp, and the panoramic image is obtained by shooting under the condition that the LED lamp is turned on; acquiring a listening position of the target space; analyzing the panoramic image, including: identifying the position of the target sound by identifying the shape or position of the LED lamp of the target sound in the panoramic image; identifying a first distance and a first angle between the listening location and the target sound based on the listening location and the location of the target sound; identifying the position of a placed object in the target space, the size of the target space and the wall material of the target space; determining a gain of the target sound based on an analysis result of the panoramic image, including: and inputting the position of the target sound, the first distance and the first angle between the listening position and the target sound, the position of the placed object, the size of the target space and the wall material of the target space into a trained sound parameter configuration model, and outputting to obtain the gain of the target sound.

One of the embodiments of the present specification provides a sound parameter determination system, including: the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a panoramic image of a target space for placing a target sound, the target sound is provided with an LED lamp, and the panoramic image is obtained by shooting under the condition that the LED lamp is turned on; the second acquisition module is used for acquiring the listening position of the target space; an analysis module for analyzing the panoramic image, the analysis module comprising: the first identification module is used for identifying the position of the target sound by identifying the shape or the position of an LED lamp of the target sound in the panoramic image; a second identification module for identifying a first distance and a first angle between the listening location and the target sound based on the listening location and the location of the target sound; the third identification module is used for identifying the position of a placed object in the target space, the size of the target space and the wall material of the target space; a first determination module, configured to determine a gain of the target sound based on an analysis result of the panoramic image, including: and inputting the position of the target sound, the first distance and the first angle between the listening position and the target sound, the position of the placed object, the size of the target space and the wall material of the target space into a trained sound parameter configuration model, and outputting to obtain the gain of the target sound.

One of the embodiments of the present specification provides a computer-readable storage medium storing computer instructions which, when executed by a processor, implement the method as above.

Drawings

The present description will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:

fig. 1 is a diagram of an application scenario of a sound adjustment system according to some embodiments of the present description;

FIG. 2 is an exemplary flow diagram of a sound parameter determination method shown in accordance with some embodiments of the present description;

FIG. 3 is an exemplary flow diagram of a method for determining sound parameters according to further embodiments of the present description;

fig. 4 is an exemplary flow diagram illustrating the determination of the gain of a target sound according to some embodiments of the present description;

fig. 5 is a block diagram of an acoustic parameter determination system according to some embodiments of the present description.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.

It should be understood that "system", "apparatus", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.

As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.

Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

Fig. 1 is a diagram of an application scenario of a sound adjustment system according to some embodiments of the present description. In an exemplary application scenario, the sound conditioning system 100 may include a server 110, a processor 120, a storage device 130, a user terminal 140, and a network 150.

In some embodiments, the sound conditioning system 100 may be used to determine sound parameters. The sound adjustment system 100 can be applied to various offline scenes using sound. Such as residential homes, restaurants, cafes, malls, performance stages, movie theaters, and the like. The sound conditioning system 100 may determine the optimal sound parameters of the sound by implementing the methods and/or processes disclosed in this specification, so as to provide the user with the best listening effect and improve the listening experience of the user.

In some embodiments, the optimal sound parameters of the sound may be determined by the user terminal 140 acquiring multiple images of the scene and/or the listening position input by the user, and processing the images by the server 110. Server 110 may retrieve data from storage device 130 or save data to storage device 130 during processing, or may read data from other sources and output data to other target objects via network 150. In some embodiments, at least part of the processing operations to determine the acoustic parameters may be performed at the user terminal 140. Operations in this specification may be performed by processor 120 executing program instructions. The above-described method is merely for convenience of understanding, and the present system may also be implemented in other possible operation modes.

In some embodiments, storage 130 may be included in server 110, user terminal 140, and possibly other system components.

In some embodiments, the processor 120 may be included in the server 110, the user terminal 140, and possibly other system components.

The server 110 may be used to manage resources and process data and/or information from at least one component of the present system or an external data source (e.g., a cloud data center). In some embodiments, the server 110 may be a single server or a group of servers. The set of servers can be centralized or distributed (e.g., the servers 110 can be a distributed system), can be dedicated, or can be serviced by other devices or systems at the same time. In some embodiments, the server 110 may be regional or remote. In some embodiments, the server 110 may be implemented on a cloud platform, or provided in a virtual manner. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-tiered cloud, and the like, or any combination thereof.

Processor 120 may process data and/or information obtained from other devices or system components. Processor 120 may execute program instructions based on such data, information, and/or processing results to perform one or more of the functions described herein. In some embodiments, processor 120 may include one or more sub-processing devices (e.g., single core processing devices or multi-core processing devices). Merely by way of example, the processor 120 may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a programmable logic circuit (PLD), a controller, a micro-controller unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like or any combination thereof.

Storage device 130 may be used to store data and/or instructions. Storage device 130 may include one or more storage components, each of which may be a separate device or part of another device. In some embodiments, storage 130 may include Random Access Memory (RAM), Read Only Memory (ROM), mass storage, removable storage, volatile read and write memory, and the like, or any combination thereof. Illustratively, mass storage may include magnetic disks, optical disks, solid state disks, and the like. In some embodiments, the storage device 130 may be implemented on a cloud platform.

Data refers to a digitized representation of information and may include various types, such as sound data, binary data, text data, image data, video data, and so forth. Instructions refer to programs that may control a device or apparatus to perform a particular function.

User terminal 140 refers to one or more terminal devices or software used by a user. In some embodiments, one or more users may be using user terminal 140, which may include users who directly use the audio listening service, as well as other associated users. In some embodiments, the user terminal 140 may be one or any combination of mobile device 140-1, tablet computer 140-2, laptop computer 140-3, desktop computer 140-4, or other device having input and/or output capabilities. In some embodiments, mobile device 140-1 may include a wearable device, a smart mobile device, and the like, or any combination thereof. In some embodiments, the smart mobile device may include a smart phone, a Personal Digital Assistant (PDA), a gaming device, a navigation device, a hand-held terminal (POS), and the like, or any combination thereof. In some embodiments, desktop computer 140-4 may be a small computer, a television, or the like.

In some embodiments, other mobile devices 140-1 having input and/or output capabilities may include a voice control terminal located in a public or home environment. In some embodiments, the user may refer to a home owner, a stereo user, or other service requester.

The above examples are intended only to illustrate the broad scope of the user terminal 140 device and not to limit its scope.

The network 150 may connect the various components of the system and/or connect the system with external resource components. The network 150 allows communication between the various components and with other components outside of the sound conditioning system 100 to facilitate the exchange of data and/or information. In some embodiments, the network 150 may be any one or more of a wired network or a wireless network. For example, network 150 may include a cable network, a fiber optic network, a telecommunications network, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Public Switched Telephone Network (PSTN), a bluetooth network, a ZigBee network (ZigBee), Near Field Communication (NFC), an in-device bus, an in-device line, a cable connection, and the like, or any combination thereof. The network connection between the parts can be in one way or in multiple ways. In some embodiments, the network may be a point-to-point, shared, centralized, etc. variety of topologies or a combination of topologies. In some embodiments, the network 150 may include one or more network access points. For example, the network 150 may include wired or wireless network access points, such as base stations and/or network switching points 150-1, 150-2, …, through which one or more components entering and exiting the sound conditioning system 100 may connect to the network 150 to exchange data and/or information.

Fig. 2 is an exemplary flow diagram of a sound parameter determination method shown in accordance with some embodiments of the present description. As shown in fig. 2, the process 200 may be performed by the processor 120.

Step 210, obtaining an image of a target space where the target sound is placed. In some embodiments, step 210 may be performed by the first obtaining module 510.

The target sound refers to a sound device of which a parameter is to be determined. In some embodiments, the target sound may include one or more speakers, or sound devices arranged in various combinations. In some embodiments, the target sound may be a combination of one or more sounds in a home theater. For example, the target sound may include one or more of 2 front speakers, 2 rear speakers, 1 center surround speaker, and 1 subwoofer speaker. As another example, 5.1 stereo.

The target space is a space where a scene in which the target sound is placed is located. Such as a residential home, a waiting area of a restaurant, a theater, etc.

In some embodiments, the images may be multiple images of the target space described above. At least two images of the plurality of images may include one or more same elements. E.g. a reference line containing the same object or part thereof and/or the same target space, etc.

The panoramic image refers to a wide-angle image in the form of a photograph. The panoramic image may display information of the target space and/or objects therein. For example, the panoramic image may reflect information such as the placement position of the target sound in the target space, the positions of other objects in the target space, and the size of the target space. In some embodiments, a processing device (e.g., processor 120) may analyze the above-described information of the panoramic image and determine parameters of the target sound according to the analysis result. For more details about determining the parameters of the target sound according to the analysis result of the panoramic image, refer to fig. 3 and the related description thereof, which are not repeated herein.

The first obtaining module 510 may obtain an image of a target space where the target speaker is placed in various ways. In some embodiments, the first obtaining module 510 may obtain an image of a target space in which the target speaker is placed from the user terminal 140 through the network 150. For example, the user terminal 140 may capture an image through an image capture device (e.g., a camera) and provide the image for the first obtaining module 510 to obtain in a network transmission manner. In some embodiments, the user may capture a plurality of images of the target space in which the target stereo is placed through the user terminal 140. At least two of the plurality of captured images may include at least one same element, so as to calculate the size of the target space from the plurality of captured images.

In some embodiments, the first obtaining module 510 may obtain an image of a target space in which the target speaker is placed from the server 110. For example, the user may upload the captured image to the server 110 for acquisition by the first acquisition module 510. In some embodiments, the first obtaining module 510 may obtain an image of a target space in which the target speaker is placed from the storage device 130. For example, the user may store the captured image in the storage device 130 for acquisition by the first acquisition module 510. In some embodiments, the first obtaining module 510 may obtain an image pre-stored in a storage space of the network 150. For example, the first obtaining module 510 may obtain an image in a cloud storage space.

Step 220, obtaining a listening position of the target space. In some embodiments, step 220 may be performed by the second acquisition module 520.

The listening position refers to a position where the user listens to the target sound. The listening position may be one or more. In some embodiments, the listening position may be a real-time location of the user in the target space. The real-time location may be a coordinate location where the user is located in the spatial coordinate system at the current time. For example only, if the coordinate position of the user in the target space at the first time is a and the coordinate position of the user in the target space at the second time is B, then both the real-time position a and the real-time position B are the listening positions of the user.

In some embodiments, the listening locations may be common locations of the user in the target space. The common location may be a location in the target space where the user frequently appears. Taking a home theater scenario as an example, the common location may be a sitting location of the user. Taking a restaurant service scene as an example, the common position may be a position of the user in a restaurant waiting area, or a position of the user in a dining area of the restaurant. It should be noted that the position of the object used by the user in the above-mentioned scene may also be determined as the listening position. For example, the coordinate position at a sofa in a home theater, a waiting area in a restaurant, or a chair in a dining area in a restaurant is determined as the listening position to improve the accuracy of the determination of the listening position. In some embodiments, the second obtaining module 520 may obtain the listening position of the target space in a variety of ways. In some embodiments, processor 120 may retrieve the listening position for the target space from an image (e.g., a panoramic image) stored by storage device 130. For example, the processor 120 may determine one or more common locations of the user in the target space from the panoramic image and determine the common locations as the listening locations for the target space. As another example, processor 120 may automatically identify the listening location by analyzing layout information of the panoramic image, or analyzing the location of certain particular items (e.g., tables, seats, etc.) in the panoramic image. For more details on the layout information of the target space, reference may be made to step 230 and its related description, which are not repeated herein.

In some embodiments, the second obtaining module 520 may also obtain the listening position of the target space through the user terminal 140. For example, the second obtaining module 520 may obtain the real-time location of the user terminal 140 in the target space from the server 110 through the network 150, and determine the real-time location as the listening position of the target space. As another example, the second obtaining module 520 may obtain the listening position input by the user terminal 140 or clicked in the application program interface from the server 110 through the network 150. Taking a home theater scene as an example, a user may obtain a prompt to shoot an image through an application program of the user terminal 140, shoot one or more images of a current scene through a camera of the user terminal 140 and upload the images to the application program, and the user may input or click at least one listening position in the application program according to the images for the second obtaining module 520 to obtain.

Step 230, identifying layout information of the target space based on the image. In some embodiments, step 230 may be performed by analysis module 530.

The layout information refers to information on objects laid out in the target space. In some embodiments, the layout information may include, but is not limited to, a location of the target sound, a distance between the listening location and the target sound, a location of a placement in the target space, a material of the placement, a shape of the placement, a size of the target space, a standing wave of the target space, reverberation of the target space, a sensitivity of the target sound, a gain formula of the target sound, and the like.

In some embodiments, the analysis module 530 may identify the layout information of the target space in a variety of ways. In some embodiments, the analysis module 530 may identify layout information of the target space through a machine learning model and/or in conjunction with a correlation algorithm. The machine learning model may include, but is not limited to, Convolutional Neural Networks (CNNs), Long-Short-Term Memory (LSTM) models, and the like. Correlation algorithms may include, but are not limited to, Layout net, Flat2Layout, E2P, geometric algorithms, depth information algorithms, and the like. For more details on the layout information for identifying the target space, refer to fig. 3 and the related description thereof, which are not repeated herein.

The position of the target sound refers to a placement position of the target sound in the target space. In some embodiments, the analysis module 530 may identify the location of the target sound through image recognition techniques. In some embodiments, the analysis module 530 may extract, from the image, a feature of the target sound through a Convolutional Neural Network (CNN), where a position of the feature in the image is a position of the target sound. For more details on identifying the position of the target sound, reference may be made to fig. 3 and its related description, which are not repeated herein.

In some embodiments, analysis module 530 may identify the distance between the listening location and the target sound in a variety of ways. In some embodiments, analysis module 530 may identify the distance between the listening location and the target sound through image recognition techniques. In some embodiments, the analysis module 530 may extract features of the target sound and the listening position from the scene image through a Convolutional Neural Network (CNN), and determine a baseline therebetween according to the features, and determine an actual length of the baseline in the real scene according to a ratio of the image, and determine the actual length as a distance between the listening position and the target sound.

In some embodiments, the processing device (e.g., processor 120) may calculate a difference in coordinates of the two in the target space, resulting in a distance between the listening location and the target sound. For more details on identifying the distance between the listening position and the target sound, reference may be made to fig. 3 and its related description, which are not repeated herein.

The object to be placed is an object to be placed in the target space, for example, furniture such as a sofa, a table and chair, a curtain, a wall picture, or a decoration. In some embodiments, the analysis module 530 may identify the location of the placed object through image recognition techniques. In some embodiments, the analysis module 530 may extract a feature of the placing object from the image through a Convolutional Neural Network (CNN), where a position of the feature in the image is a position of the placing object. In some embodiments, the convolutional neural network may extract features of the placement through a preset algorithm. For example, SSD algorithm (Single Shot multitox Detector).

The material, shape and size of the object placed may affect the parameters of the target sound. For example, the placement object made of the sound absorbing material absorbs sound waves of the target sound, and reduces acoustic parameters such as sound quality and volume of the target sound. For example, the placement object having an irregular shape or a large size may block the sound wave transmission of the target sound, and may reduce the sound parameters such as the surround sound effect and the stereo sound effect of the target sound.

The standing wave is a composite wave formed by two sine waves having the same wavelength, period, frequency and wave velocity and traveling and interfering in opposite directions. The standing waves in the target space attenuate some of the sound waves emitted by the target sound, thereby reducing the listening experience for the user. The standing wave of the target space is related to the size of the target space. For example, the larger the size of the target space, the smaller the critical frequency of the standing wave in the target space, and the smaller the influence of the standing wave on the target sound.

Reverberation is an acoustic phenomenon in which sound continues to exist after the sound source has stopped sounding. The reverberation of the target space affects the sound quality of the target sound. The reverberation of the target space is related to the size of the target space and the material of the target space.

The sensitivity of the target sound equipment means the signal voltage at the input end when the power amplifier of the sound equipment reaches the full power output. The larger the signal voltage, the lower the sensitivity. The sensitivity of sound is typically used to reflect the magnitude of sound that the human ear subjectively perceives. The sensitivity of the sound is high, the larger the sound of the sound is felt by a user, but the tone quality of the sound is damaged by the too high sensitivity, so that the sensitivity of the sound is controlled within a reasonable range to bring a good listening experience to the user.

The gain formula of the target sound is a formula used in the process of determining the gain of the target sound. In some embodiments, the gain formula of the target sound may be a formula of determining a maximum voltage capacity of the target sound. The target sound can be protected by determining the maximum voltage capacity of the target sound.

Step 240, determining parameters of the target sound based on the layout information, wherein the parameters include the gain of the target sound. In some embodiments, step 240 may be performed by the first determining module 540.

The gain of the target sound is a signal amplification factor of the target sound. For example, a smaller output voltage is amplified by an amplifier to become the amplification ratio of a larger output voltage.

In some embodiments, the first determining module 540 may determine a plurality of candidate gains of the target sound according to the layout information, and determine the gain of the target sound from the plurality of candidate gains. The gain of the target sound can determine that the target sound can obtain better sound quality improvement under the same volume. In some embodiments, the gain of the target sound may be determined and adjusted by a corresponding Equalizer (EQ).

In some embodiments, the parameters of the target sound may include, but are not limited to, a gain of the target sound, an output power of the target sound, a delay of the target sound, and the like. In some embodiments, the first determining module 540 may determine the parameters of the target sound in a variety of ways. In some embodiments, the first determination module 540 may determine the parameters of the target sound based on a machine learning model. In some embodiments, the first determining module 540 may determine the parameters of the target sound by the initial parameters of the target sound. In some embodiments, the initial parameters of the target sound may include, but are not limited to, one or more of a gain of the target sound, an output power of the target sound, and a delay time of the target sound. In some embodiments, the first determination module 540 may determine the parameters of the target sound based on the initial parameters of the target sound and/or the analysis result of the panoramic image.

For more details on determining the parameters of the target sound, reference may be made to fig. 3 and fig. 4 and the related description thereof, which are not described herein again.

The layout information of the target space influences the parameters of the target sound. For example, if a large number of objects are placed around the target sound, the sound wave transmission of the target sound is blocked, which affects the listening effect of the user. Therefore, the parameters of the target sound can be determined more comprehensively and accurately by identifying the layout information of the target space, so that better listening effect is provided for the user.

Fig. 3 is an exemplary flow diagram of a method for determining acoustic parameters according to further embodiments of the present description. As shown in fig. 3, the process 300 may be performed by the processor 120.

Step 310, a panoramic image of a target space where the target sound is placed is obtained. In some embodiments, step 310 may be performed by the first obtaining module 510.

In some embodiments, the target audio has one or more LED lights. The LED lamp can be a function indicator lamp of the target sound and can also be an appearance decorative lamp of the target sound.

In some embodiments, the panoramic image is captured with the LED light on. Because the LED lamp in the lighting state can not be influenced by the brightness of light rays in a target space, the display effect of high resolution and clear outline in the panoramic image is achieved. Therefore, it may help the processing device (e.g., the processor 120) to better identify the location of the target sound, facilitating subsequent comprehensive and accurate determination of the parameters of the target sound.

In some embodiments, the first acquiring module 510 may acquire a plurality of images of the target space from a mobile terminal. The mobile terminal may be a user terminal 140. In some embodiments, the mobile terminal may capture multiple images of the target space through an image capture device (e.g., a camera). In some embodiments, the multiple images may be from different locations of the target space. For example, the user may capture a plurality of images containing the same element of the target space at different positions in the target space, or select one or more images with the best capturing quality from the images for the first capturing module 510 to capture. In some embodiments, multiple images may be taken from the same location in the target space. For example, a user may take multiple images at different angles at the same location in the target space for acquisition by the first acquisition module 510.

In some embodiments, the first obtaining module 510 may obtain the plurality of images stored by the mobile terminal from the storage device 130 or from the cloud storage space through the network 150. In some embodiments, the first obtaining module 510 may obtain the panoramic image based on a plurality of images. In some embodiments, the first obtaining module 510 may perform permutation and combination on a plurality of images of the target space through an image processing device (e.g., the processor 120), and concatenate a plurality of images meeting the conditions in the permutation and combination to obtain a panoramic image of the target space.

In some embodiments, the first obtaining module 510 may obtain the panoramic image shot or stored by the mobile terminal from the storage device 130 or from the cloud storage space through the network 150.

For more details on acquiring the panoramic image, reference may be made to fig. 2 and the related description thereof, which are not repeated herein.

And step 320, acquiring a listening position of the target space. In some embodiments, step 320 may be performed by the second acquisition module 520.

In some embodiments, the second obtaining module 520 may obtain the listening position input by the user from the mobile terminal. In some embodiments, the listening location input by the user to the mobile terminal may include multiple types. For example, the user's common listening position clicks on the listening position or likes the listening position, etc. In some embodiments, the listening position input by the user to the mobile terminal may be one or more. The types of the plurality of listening positions may be the same or multiple.

In some embodiments, the second obtaining module 520 may obtain the listening position input by the user from the storage device 130 or from the cloud storage space through the network 150. In some embodiments, the processing device (e.g., processor 120) may record the listening position input by the user to the mobile terminal and store the recorded listening position in the storage device 130 in the form of historical data, from which the second obtaining module 520 may obtain the listening position input by the user. For more details on obtaining the listening position input by the user from the mobile terminal, reference may be made to fig. 2 and the related description thereof, which are not further described herein.

As can be seen from the above description, the second obtaining module 520 may obtain multiple listening positions input to the mobile terminal by the user more quickly without identifying the panoramic image, so as to determine the sound parameters more comprehensively and accurately through the listening positions, and provide a better listening experience for the user.

Step 330, analyzing the panoramic image. In some embodiments, step 330 may be performed by analysis module 530. In some embodiments, step 330 may also include the following steps.

Step 331, identifying the position of the target sound by identifying the shape or position of the LED lamp of the target sound in the panoramic image. In some embodiments, step 331 may be performed by the first identification module 531.

The LED lamp of the target sound can help the first recognition module 531 accurately recognize the target sound from the panoramic image of the target space. In some embodiments, the first recognition module 531 may recognize the shape of the LED lamp of the target sound. In some embodiments, the first identification module 531 may identify the shape of the LED lamp according to preset parameters of the LED lamp. The preset parameters may include the size of the pattern LED lamp of the LED lamp, the color of the LED lamp, and the like. In some embodiments, the processing device (e.g., the processor 120) may extract features of the LED lamp from the panoramic image based on preset parameters of the LED lamp, and the first recognition module 531 may recognize the shape of the LED lamp according to the features. In some embodiments, a processing device (e.g., processor 120) may identify the shape of the LED lamp by a machine learning model based on the panoramic image of the target space. In some embodiments, the first identification module 531 may identify the location of the LED lamp of the target stereo. In some embodiments, the processing device (e.g., the processor 120) may extract features of a plurality of LED lights from the panoramic image, and the first identification module 531 may obtain a baseline formed by a connection of the features and determine the positions of the LED lights in the panoramic image according to the baseline.

In some embodiments, the first identification module 531 may identify the second distance between the target sound and the photographer of the panoramic image by identifying a shape or a position of an LED lamp of the target sound in the panoramic image. The second distance is the relative distance between the photographer and the target sound. In some embodiments, a processing device (e.g., processor 120) may determine the location of the photographer from the panoramic image and determine a baseline formed by the location in connection with the location of the LED lights. In some embodiments, the first identification module 531 may determine the length of the second distance from the baseline in conjunction with the scale of the panoramic image. For example only, the scale of the panoramic image is 1: 20, the length of the base line is 5 cm. Thus, the length of the second distance is 100 cm.

In some embodiments, the first recognition module 531 may determine the location of the target sound based on the second distance. For example only, assume that the position coordinates of the photographer are the origin of coordinates and the second distance is 100 cm. In this way, the relative distance between the target sound and the position of the photographer is 100cm, and the first recognition module 531 may calculate the coordinates of the target sound according to the relative distance, and may further obtain the position of the target sound.

Because the shape and the size of the LED lamp are simpler than those of the target sound, and the outline of the LED lamp in the panoramic image in the lighting state is clearer and the display resolution is higher according to the above description, the position of the target sound can be identified more quickly and accurately by identifying the LED lamp of the target sound, so that the efficiency and the accuracy of subsequent target sound parameter determination can be improved.

Step 332 of identifying a first distance and a first angle between the listening position and the target sound based on the listening position and the position of the target sound. In some embodiments, step 332 may be performed by the second identification module 532.

In some embodiments, the second identifying module 532 may determine a baseline connecting the listening position and the target sound in the panoramic image according to the listening position and the position of the target sound, and determine the first distance according to the length of the baseline in combination with the scale of the panoramic image. It should be noted that, when there are a plurality of target sounds, the second identifying module 532 may determine a plurality of first distances between the listening position and the target sounds according to the base lines of the listening position and the plurality of target sounds connected in the panoramic image.

For more details on the identification of the first distance, reference may be made to the related description of the identification of the second distance in step 331, which is not described herein again.

In some embodiments, the second identification module 532 may identify a first angle between the listening position and the target sound by an off-axis curve. The off-axis curve refers to the frequency response curve when the sound is off-axis. Wherein, the axis refers to a connecting line when the sound and the listening position are at 0 degrees. The off-axis curves may reflect the quantized behavior of the sound. In some embodiments, the off-axis curve may be preset. Specifically, data can be obtained by performing pre-measurement on the target sound, and an off-axis curve is drawn based on the obtained data. For example, the off-axis curve for sound output in the 20-22kHz range is plotted, with the output sound pressure level and frequency as the axis.

It is understood that the second identifying module 532 may identify the sound pressure level output by the target sound device at a specific frequency (e.g., 20-22kHz) for the listening location, and then compare the output sound pressure level according to the predetermined off-axis curve to identify the first angle between the listening location and the target sound device.

It should be noted that, due to the directivity of the high frequency signal, when the angle between the target sound and the listening position is 0 ° (referred to as on-axis), the high pitch heard by the user is clearer and more accurate relative to other angles (off-axis). That is, the larger the off-axis angle, the less the high volume feeling the sound emits. The equalizer for off-axis, i.e., the off-axis EQ, can thus be preset based on the above properties.

In some embodiments, the processing device (e.g., processor 120) may determine the gain of the target sound through the off-axis EQ. Specifically, the off-axis EQ can adjust the high pitch emitted by the target sound at different off-axis angles to ensure that the sound quality of the off-axis target sound is as close as possible to the on-axis state.

Step 333, identifying the position of the placed object in the target space, the size of the target space, and the wall material of the target space. In some embodiments, step 333 may be performed by the third identifying module 533.

In some embodiments, the third identifying module 533 may identify the location of the item from the characteristics of the item extracted by the processing device (e.g., the processor 120). For more details on identifying the position of the placing object, reference may be made to fig. 2 and the related description thereof, which are not repeated herein.

In some embodiments, the third identifying module 533 may further identify the material, shape and size of the placing object according to preset parameters of the placing object. The preset parameters of the placing object can comprise material parameters, shape parameters and size parameters of the placing object. In some embodiments, the processing device (e.g., the processor 120) may extract a feature of the placing from the panoramic image, and the third identifying module 533 may compare the feature with preset parameters and determine the material, shape and size of the placing according to the comparison result. For example only, the third identifying module 533 may compare the characteristics of the placing object with the material parameter of the placing object, assuming that the material parameter is wood, and if the comparison result is yes, it indicates that the material of the placing object is wood.

In some embodiments, the third identifying module 533 may identify the size of the target space. The size of the target space may be the volume of the target space, or the surface area of all walls and floors in the target space. Specifically, the third identifying module 533 may identify a plurality of base lines of the panoramic image, calculate an actual length of each base line according to a ratio of the panoramic image, and further calculate a size of the target space according to the actual length of each base line. For more details of the baseline, reference may be made to the following description regarding the baseline and the target spatial dimensions, which are not repeated here.

In some embodiments, the third identifying module 533 may identify the wall covering of the target space. Taking the target space as a single room, the wall covering of the target space may be the surface material of six walls in the target space, such as paint, coating, etc. Specifically, the third identifying module 533 may compare the elements corresponding to the wall materials in the panoramic image with the features of the panoramic image, and identify the wall materials in the target space according to the obtained comparison result. The panoramic image feature may be a feature of a wall covering pre-extracted from the panoramic image, for example, a pixel, a feature vector, and the like of different wall coverings.

It should be noted that both the size of the target space and the wall material of the target space may affect the reverberation of the target space, and for more details about the reverberation of the target space, reference may be made to the following embodiments and related descriptions thereof, which are not described herein again.

Through the position, material, shape and the size of discernment placing object to and the size of target space, the wall material of target space, can combine its a plurality of influence factor to target sound parameter of above-mentioned information analysis to can promote sound parameter determination's comprehensiveness and accuracy.

In some embodiments, analyzing the panoramic image may further include: identifying at least one of a size of the target space, a standing wave of the target space, reverberation of the target space, sensitivity of the target sound, a gain formula of the target sound. In some embodiments, the identification operation of the size of the target space, the standing wave of the target space, the reverberation of the target space, the sensitivity of the target sound, the gain formula of the target sound, and the like may be performed by the third identification module 533. For example, the placement object made of the sound absorbing material absorbs sound waves of the target sound, and reduces acoustic parameters such as sound quality and volume of the target sound. For example, the placement object having an irregular shape or a large size may block the sound wave transmission of the target sound, and may reduce the sound parameters such as the surround sound effect and the stereo sound effect of the target sound.

In some embodiments, the third identifying module 533 may identify a size of the target space. In some embodiments, a processing device (e.g., processor 120) may extract a plurality of keypoints of a target space from a panoramic image of the target space. Wherein the key point may be a connection point of an interface line of a wall surface of the target space and the ground. In some embodiments, the third identifying module 533 may identify a baseline formed by connecting a plurality of key points according to the plurality of key points, and calculate the size of the target space according to the size of the baseline and the ratio of the panoramic image. For example only, assuming that one of the plurality of base lines has a length of 20cm, the panoramic image has a scale of 1: 5, the baseline corresponds to a target space of 100cm in size.

In some embodiments, the third identifying module 533 may identify a standing wave of the target space. In some embodiments, the third identifying module 533 may identify the standing wave of the target space according to the size of the target space. In some embodiments, the formula for the calculation of the standing wave frequency may be expressed as:

wherein, F₁The first order standing wave frequency, F, in the direction of the length of the target space₂First order standing wave frequency, F, in the width direction of the target space₃First order standing wave frequency, L, in the height direction of the target space₁Is the length of the target space, L₂Is the width of the target space, L₃V is the propagation speed of the sound emitted by the sound source in the target space, i.e., the sound velocity, which is the height of the target space.

It should be noted that, since the formula (1) requires to calculate the corresponding standing wave frequency from the sound velocity V. Therefore, when it occurs that the sound of the sound source is completely blocked by the placing object, L in the formula (1) needs to be set₁Defined as the distance of the sound source from the object placed.

The reverberation of the target space can be represented by T60 reverberation. T60 reverberation refers to the time required for a sound to decay by 60dB when it suddenly stops after reaching steady state. In some embodiments, the measurement formula for T60 reverberation of the target space may be expressed as:

where V denotes the size (volume) of the target space, m denotes the air attenuation coefficient, S denotes the surface area of the target space, and α denotes the average sound absorption coefficient.

In some embodiments, the third identifying module 533 may identify reverberation of the target space. According to the formula (2), it can be known that the reverberation of the target space is related to the volume, surface area and material of the target space. In some embodiments, the processing device (e.g., the processor 120) may obtain the size and texture of the target space according to the foregoing steps, and the third identifying module 533 may estimate the reverberation of the target space according to the information of the target space, so as to determine the gain of the target sound through the bass equalizer subsequently.

In some embodiments, the third identifying module 533 may identify the sensitivity of the target sound. In some embodiments, the third identifying module 533 may identify the sensitivity of the target sound according to a preset sensitivity parameter. The preset sensitivity parameters may include a maximum voltage capacity and a voltage gain of the target sound. In some embodiments, the processing device (e.g., the processor 120) may extract a feature of the target sound, for example, a model of the target sound, and the third identifying module 533 may determine a preset sensitivity parameter for the target sound according to the feature. For example, a preset sensitivity parameter may be selected that has an equal or closest maximum voltage capability. In some embodiments, the third identifying module 533 may obtain the sensitivity of the target sound according to a preset sensitivity parameter and a sensitivity calculation formula. In some embodiments, the sensitivity calculation formula may be expressed as:

where V' represents the sensitivity of the target sound, V represents the maximum voltage capacity of the target sound, and a represents the voltage gain. Generally, the sensitivity of the target sound is in the range of 0.775V to 1.5V.

The gain formula of the target sound is a formula used in the process of determining the gain of the target sound. In some embodiments, the gain of the target sound may be a maximum voltage capacity formula of the target sound. The target sound can be protected by determining the maximum voltage capacity of the target sound. The maximum voltage capacity of the target sound may be obtained according to the maximum power and the load impedance of the target sound, and the corresponding gain formula may be expressed as:

V＝W×Ω (4)

where V denotes the maximum voltage capacity of the target sound, W denotes the maximum power of the target sound, and Ω denotes the load impedance of the target sound.

In some embodiments, the third identifying module 533 may identify a gain formula of the target sound. In some embodiments, the third identifying module 533 may obtain the gain formula of the target sound from the storage device 130 or from the server 110 through the network 150.

By identifying the various information related to the target space and the target sound, the information can be combined to analyze a plurality of factors influencing the target sound parameters, so that the comprehensiveness and accuracy of sound parameter determination can be improved.

Step 340, determining the gain of the target sound based on the analysis result of the panoramic image. In some embodiments, step 340 may be performed by the first determining module 540.

In some embodiments, the parameters of the target sound may include, but are not limited to, a gain of the target sound, an output power of the target sound, a delay of the target sound, and the like.

In some embodiments, the first determination module 540 may determine the parameters of the target sound through a machine learning model. In some embodiments, multiple images or panoramic images of the target space may be input to the machine learning model, and parameters of the target sound with better quality may be output. In some embodiments, the processing device (e.g., processor 120) may obtain the original parameters of the target sound stored in the storage device 130, and determine the optimized parameters of the target sound by evaluating the sound quality of the target sound under the original parameters through an auxiliary device or program (e.g., a sound quality evaluation application).

In some embodiments, the parameters of the target sound may also be determined by preset parameters of the target sound. For more details about determining the parameters of the target sound according to the preset parameters of the target sound, reference may be made to fig. 4 and the related description thereof, which are not described herein again.

In some embodiments, the first determination module 540 may determine the gain of the target sound through a machine learning model. In some embodiments, the machine learning model may be a trained acoustic parameter configuration model. In some embodiments, the acoustic parameter configuration model may include, but is not limited to, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) models, and the like. In some embodiments, the input of the sound parameter configuration model may be one or more of a location of the target sound, a first distance between the listening location and the target sound, a first angle, a location of the placed object, a type of the placed object, a shape of the placed object, a material of the placed object, and the like, and the output may be a parameter (e.g., gain) of the target sound. In some embodiments, the input to the sound parameter configuration model may also be an image containing the above information. The target sound information may be obtained by a processing device (e.g., the processor 120) through an image recognition method.

In some embodiments, the sound parameter configuration model may be trained based on a plurality of labeled training samples. Specifically, a training sample with a label is input into the sound parameter configuration model, and parameters of the sound parameter configuration model are updated through training.

In some embodiments, the training sample may include a location of the target sound, a first distance between the listening location and the target sound, a first angle, a location of the placement, a material of the placement, a shape of the placement, a parameter of the target sound, an image containing the above information, or the like, or a combination thereof.

In some embodiments, the training sample may be obtained by an auxiliary device. In some embodiments, the auxiliary device may be an automated device. Such as a robotic arm, automated cart, etc. In some embodiments, the accessory may acquire training samples in a variety of ways. For example, the auxiliary device may change the position of the target sound, the first distance between the listening position and the target sound, the first angle, the position of the placed object by moving the target sound, the listening device, or each placed object. For another example, the auxiliary device may replace the type, shape and/or material of each item placed. For another example, the auxiliary device may automatically set or adjust parameters of the target sound according to the data to obtain parameters of a plurality of target sounds. So that the auxiliary device can acquire a large number of training samples through the above operation.

In some embodiments, the tag may be a sound quality of the target sound or a parameter of the target sound. In some embodiments, the sound quality of the target sound may be represented by a corresponding score. Wherein, the higher the score, the better the tone quality of the corresponding target sound. In some embodiments, the parameter of the target sound may be a plurality of parameter values of the target sound that are automatically set. Wherein, the parameter values can be automatically set according to the training sample.

In some embodiments, the tags may be acquired by a listening device (e.g., an analog user system) having a scoring function. In some embodiments, the listening device may automatically score the sound quality of the target sound by receiving the sound emitted by the target sound. In some embodiments, the listening device may filter the above labels, and use the sound quality of the target sound or the parameters of the target sound meeting the preset conditions as the label corresponding to the training sample. In some embodiments, the preset condition may be a sound quality of the target sound, or a sound quality corresponding to a parameter of the target sound is greater than a preset threshold. In some embodiments, the listening device may obtain, as a tag, the sound quality of the target sound that meets a preset condition. For example, if the sound quality of the target sound in the current training sample obtained by the listening device is 95 minutes and the preset threshold is 90 minutes, it indicates that the sound quality of the target sound meets the preset condition, and the sound quality can be used as a label corresponding to the training sample. In some embodiments, the listening device may obtain parameters of the target sound meeting preset conditions as a tag. For example, the auxiliary device may obtain a plurality of sets of training samples, automatically set a plurality of different parameters of the target sound for each set of training samples, obtain the sound quality scores of the target sound under the plurality of different parameters, and use the parameters of the target sound with the sound quality scores exceeding a preset threshold as the labels of the corresponding training samples.

In some embodiments, the sound parameter configuration model may be trained by various methods to update the model parameters based on the above samples. For example, the training may be based on a gradient descent method.

In some embodiments, the training is ended when the trained sound parameter configuration model satisfies a preset condition. The preset condition may be that the loss function result converges or is smaller than a preset threshold, etc.

According to the above description, the position of the target sound, the first distance between the listening position and the target sound, the first angle, the position of the placed object, the size of the target space, the wall material of the target space and the like are considered when the parameters of the target sound are determined, so that the accuracy and the comprehensiveness of the determination of the parameters of the target sound are improved, and the listening experience of a user is improved.

In some embodiments, the first determination module 540 may determine the output power of the target sound and/or the delay time of the target sound based on the analysis result of the panoramic image.

The output power of the target sound is the rated power of the target sound when it is used. The output power of the target sound can determine the maximum sound intensity of the target sound. In some embodiments, the first determination module 540 may determine the output power of the target sound based on the analysis result of the panoramic image. In some embodiments, the analysis result of the panoramic image may include a size of the target space. The first determining module 540 may determine the optimal output power of the target sound according to the size of the target space and the gain formula of the target sound. In some embodiments, the gain formula of the target sound may be an optimal correspondence of the volume of the target space and the output power of the target sound. For example, when the volume of the target space is 20m³Then, the first determining module 540 may determine the optimal output power of the target sound to be 60W according to the optimal corresponding relationship.

The delay of the target sound is the delay of the user receiving the sound emitted by each sound. Proper time delay can improve the stereo tone quality of the target sound. For example, when the delay amounts of two sound sources are 5ms to 35ms, the human ear can only feel the presence of one sound source ahead; when the delay amount of the sound source is 30ms to 50ms, the human ear can roughly distinguish the existence of the two sound sources; when the delay amount of the sound source is more than 50ms, the human ear can feel that two sound sources exist simultaneously. The smaller the delay of the target sound is, the softer the tone quality of the target sound is; the larger the delay of the target sound is, the stronger the tone quality stereo surround of the target sound is. In some embodiments, the first determining module 540 may determine the time delay of the target sound according to the listening effect required by the user. For example, when the user requests a sound quality with a strong stereoscopic feeling, the first determining module 540 may determine the delay time of the target sound with a large value within a reasonable range.

By determining the output power of the target sound and the time delay of the target sound, the parameters of the target sound can be reasonably adjusted, so that the comprehensiveness of sound parameter determination is improved, and the listening experience of a user is improved.

In some embodiments, the first determination module 540 may determine the gain of the target sound according to the first distance or the second distance. Taking the first distance between the listening position and the 5.1 sound as an example, the first distances from the listening position to the five sound boxes of the 5.1 sound are a₁、a₂、a₃、a₄、a₅. In some embodiments, the average of the first distances may be expressed as:

in some embodiments, the gains of the above five speakers can be expressed as:

wherein a may be a₁To a₅Any one of the above.

In some embodiments, the gain may be adjusted by an equalizer or dynamic equalizer of the target sound to determine parameters of the target sound.

And step 350, acquiring preset parameters of at least one sound in the target sound. In some embodiments, step 350 may be performed by the third obtaining module 550.

The preset parameters refer to preset parameters of the target sound. In some embodiments, the preset parameter may be a preferred sound parameter. For example, the delay time of the target sound may be in the range of 5ms to 50ms, and if the sound quality stereo surround of the target sound is made strong, the delay time of the target sound may be in the range of 30ms to 50ms, which is preferable. In some embodiments, the preset parameter may be a termination value of a parameter adjustable range of the target sound. For example, when the delay time of the target sound is adjustable between 30ms and 50ms, the preset parameter may be that the delay time of the target sound is adjustable between 30ms and 50 ms.

In some embodiments, the third obtaining module 550 may obtain the preset parameters of the target space from the storage device 130 or from the cloud storage space through the network 150. In some embodiments, the third obtaining module 550 may obtain the preset parameters of the target sound manually input by the user from the user terminal 140.

In some embodiments, the preset parameter may include a preset gain of the at least one speaker. For example, the sensitivity gain of the target sound, the output power gain of the target sound, and the like.

And step 360, determining the target position of the at least one sound based on the listening position and the preset parameters. In some embodiments, step 360 may be performed by the second determination module 560.

In some embodiments, the preset parameter may be a delay time of the target sound. In some embodiments, the processing device (e.g., the processor 120) may obtain a time delay of a target sound at a certain listening position, and the second determining module 560 may determine the target position or the selectable range of the target position of at least one sound according to the time delay of the target sound. For example, when the delay time of the target sound is greater than 50ms, which indicates that the delay time of the target sound is too large, an echo effect affecting the sound quality of the target sound may be generated, and the second determining module 560 may decrease the distance between at least one sound and other sounds to reduce the overall delay time of the target sound.

In some embodiments, the preset parameter may be a gain of the target sound. In some embodiments, the processing device (e.g., the processor 120) may obtain a gain of the target sound at a certain listening position, and the second determining module 560 may determine the target position or the selectable range of target positions of at least one sound according to the gain of the target sound. For example, when the gain of the target sound is a thousand times amplification gain of the output voltage of the target sound by 3 amplifiers of 20db, the user may estimate the sound quality of the target sound through an auxiliary device or system (e.g., a sound quality estimation application), and the second determining module 560 may increase the distance of at least one sound from other sounds to further increase the surround sound quality of the target sound.

According to the above description, when the parameter of the target sound reaches a better value, an optimal value or an end value of an adjustable range, the listening experience of the user can be further improved by changing the position of the target sound, so that the accuracy and diversity of sound parameter determination are improved.

Fig. 4 is an exemplary flow diagram illustrating determining a gain of a target sound according to some embodiments of the present description. The process 400 may be performed by the processor 120.

Step 410, obtaining initial parameters of the target sound.

The initial parameters may be default parameters of the target sound. In some embodiments, the initial parameters may be set empirically. In some embodiments, the initial parameters may include initial parameters of the target sound. For example, the initial gain of the target sound, the initial output power of the target sound, the initial delay of the target sound, and the like.

In some embodiments, the processor 120 may retrieve the initial parameters from the storage device 130 or the user terminal 140.

Step 420, determining at least one optimum listening position from the plurality of listening positions based on the analysis result of the panoramic image and the initial parameters.

In some embodiments, the analysis results of the panoramic image may include the location of the target sound and the listening location. In some embodiments, the processing device (e.g., processor 120) may obtain parameters of a plurality of target sounds corresponding to the plurality of listening locations, and determine at least one optimum listening location based on a comparison of the parameters of the plurality of target sounds with the initial parameters. For example only, assuming that the initial parameter is the delay of the target sound, the delay of the target sound corresponding to the first listening position is 40ms, and the delay of the target sound corresponding to the second listening position is 60ms, the comparison result is that the first listening position is better than the second listening position. The above steps are repeated until at least one optimum listening position is determined.

And step 430, adjusting the gain of the target sound based on the optimal listening position.

In some embodiments, the processing device (e.g., processor 120) may adjust the gain of the target sound based on the sweet spot. For example only, assuming the user's desired listening effect is a soft tone quality, the processor 120 may decrease the gain of the target sound. For example, by reducing the amplification factor of the target sound.

According to the description, the optimal parameters of the sound can be further determined under the optimal listening position, so that the comprehensiveness and accuracy of sound parameter determination are improved, and the listening experience of a user is further improved.

In some embodiments, the system 500 may include a first acquisition module 510, a second acquisition module 520, an analysis module 530, a first determination module 540, a third acquisition module 550, and a second determination module 560.

The first acquisition module 510 may be used to acquire a panoramic image. In some embodiments, the first obtaining module 510 may be configured to obtain a panoramic image of a target space in which a target stereo is placed, the target stereo having an LED lamp, and the panoramic image being captured when the LED lamp is turned on. In some embodiments, the first obtaining module 510 may be further configured to obtain a plurality of images of the target space from a mobile terminal; obtaining the panoramic image based on the plurality of images.

The second acquisition module 520 may be used to acquire a listening position. In some embodiments, the second obtaining module 520 may be configured to obtain a listening position of the target space. In some embodiments, the second obtaining module 520 may also be configured to obtain the listening position input by the user from the mobile terminal.

The analysis module 530 may be used to analyze the panoramic image. In some embodiments, the analysis module 530 may be used to identify the material, shape, and size of the placement in the target space.

In some embodiments, the analysis module 530 may further include a first recognition module 531, a second recognition module 532, and a third recognition module 533.

The first recognition module 531 may be used to recognize a location of the target sound. In some embodiments, the first identification module 531 may be configured to identify the location of the target sound by identifying a shape or a location of an LED lamp of the target sound in the panoramic image. In some embodiments, the first identification module 531 may be further configured to identify a second distance between the target sound and the photographer of the panoramic image by identifying a shape or a position of an LED lamp of the target sound in the panoramic image; and determining the position of the target sound based on the second distance.

The second identification module 532 may be used to identify the relative distance of the listening location from the target sound. In some embodiments, the second identification module 532 may be configured to identify a first distance and a first angle between the listening position and the target sound based on the locations of the listening position and the target sound.

The third identifying module 533 can be used to identify the placing object and the information of the target space. In some embodiments, the third identifying module 533 may be configured to identify a location of a placement in the target space. In some embodiments, the third identifying module 533 can also be used to identify the material, shape and size of the placement in the target space. In some embodiments, the third identifying module 533 may be further configured to identify at least one of a size of the target space, a standing wave of the target space, reverberation of the target space, a sensitivity of the target sound, and a gain formula of the target sound.

The first determining module 540 may be configured to determine a gain of the target sound based on an analysis result of the panoramic image. In some embodiments, the first determining module 540 may be further configured to input the location of the target sound, the first distance and the first angle between the listening location and the target sound, and the location of the placement, the size of the target space, and the wall material of the target space to the trained sound parameter configuration model, and output the gain of the target sound. In some embodiments, the first determining module 540 may be further configured to obtain an initial parameter of the target sound, where the initial parameter includes an initial gain of the target sound; determining at least one optimal listening position from the plurality of listening positions based on the analysis result of the panoramic image and the initial parameters; and adjusting the gain of the target sound based on the optimum listening position.

The third obtaining module 550 may be configured to obtain preset parameters of the target sound. In some embodiments, the third obtaining module 550 may be configured to obtain a preset parameter of at least one sound of the target sound, where the preset parameter includes a preset gain of the at least one sound.

The second determination module 560 may be used to determine the location of the target speaker. In some embodiments, the second determining module 560 may be configured to determine the target location of the at least one sound based on the listening position and the preset parameters.

It should be understood that the system and its modules shown in FIG. 5 may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules in this specification may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).

It should be noted that the above description of the sound parameter determination system 500 and the modules thereof is merely for convenience of description, and is not intended to limit the present disclosure within the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of modules or sub-system configurations may be used to connect to other modules without departing from such teachings. For example, in some embodiments, the first obtaining module 510, the second obtaining module 520, the analyzing module 530, the first determining module 540, the third obtaining module 550, and the second determining module 560 disclosed in fig. 5 may be different modules in a system, or may be a module that implements the functions of two or more of the above modules. For another example, the first obtaining module 510 and the second obtaining module 520 may be two modules, or one module may have both obtaining and data processing functions. For another example, the first and

second recognition modules

531 and 532 may be two modules, and when the listening position coincides with the position where the panoramic image is obtained, the first and

second recognition modules

531 and 532 may be one module and have a function of recognizing the distance between the listening position and the target sound. Such variations are within the scope of the present disclosure.

The beneficial effects that may be brought by the embodiments of the present description include, but are not limited to: (1) the panoramic image of the target space where the sound equipment is located is identified to obtain a plurality of factors influencing the sound equipment parameters, so that the parameters of the sound equipment can be determined more conveniently, more quickly, more comprehensively and more accurately according to the influencing factors; (2) by changing the position of the sound, the gain of the sound can be further determined under the optimal parameters or the optimal listening position of the sound, so that the listening experience of a user can be improved.

It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.

Also, the description uses specific words to describe embodiments of the description. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.

Additionally, the order in which the elements and sequences of the process are recited in the specification, the use of alphanumeric characters, or other designations, is not intended to limit the order in which the processes and methods of the specification occur, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.

For each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this specification, the entire contents of each are hereby incorporated by reference into this specification. Except where the application history document does not conform to or conflict with the contents of the present specification, it is to be understood that the application history document, as used herein in the present specification or appended claims, is intended to define the broadest scope of the present specification (whether presently or later in the specification) rather than the broadest scope of the present specification. It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of the present description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. A method of acoustic position determination, comprising:

acquiring a panoramic image of a target space for placing a target sound, wherein the target sound is provided with an LED lamp, and the panoramic image is obtained by shooting under the condition that the LED lamp is turned on; and

analyzing the panoramic image to determine the position of a target sound box;

wherein the analyzing the panoramic image comprises:

and identifying the position of the target sound by identifying the shape or the position of the LED lamp of the target sound in the panoramic image.

2. The sound location determination method of claim 1, the identifying the location of the target sound by identifying a shape or location of an LED light of the target sound in the panoramic image comprising:

identifying a second distance between the target stereo and a photographer of the panoramic image by identifying a shape or position of an LED lamp of the target stereo in the panoramic image; and

and determining the position of the target sound based on the second distance.

3. The sound location determination method of claim 2, said identifying a second distance between the target sound and a photographer of the panoramic image by identifying a shape or location of an LED light of the target sound in the panoramic image, comprising:

determining a location of the photographer based on the panoramic image;

determining a base line formed by connecting the position of the photographer and the position of the LED lamp; and

determining a length of the second distance from the baseline in conjunction with a scale of the panoramic image.

4. The sound position determination method of claim 1, wherein the target sound has one or more LED lights, and the LED lights comprise at least one of a function indicator light of the target sound and an appearance decorative light of the target sound.

5. The sound location determination method of claim 1, the identifying a shape of an LED light of the target sound in the panoramic image, comprising:

recognizing the shape of the LED lamp based on preset parameters of the LED lamp, wherein the preset parameters of the LED lamp at least comprise at least one of the pattern of the LED lamp, the size of the LED lamp and the color of the LED lamp.

6. The method of claim 5, wherein the identifying the shape of the LED lamp according to the preset parameters of the LED lamp comprises:

extracting the characteristics of the LED lamp from the panoramic image based on the preset parameters of the LED lamp; and

identifying a shape of the LED lamp based on the characteristics of the LED lamp.

7. The sound location determination method of claim 1, the identifying a shape of an LED light of the target sound in the panoramic image, comprising:

identifying a shape of the LED lamp through a machine learning model based on the panoramic image of the target space.

8. The method of claim 7, the machine learning model comprising at least one of a convolutional neural network, long-short term memory.

9. The acoustic position determination method of claim 5, further comprising:

identifying a location of the LED lamp based on the characteristics of the LED lamp.

10. The method of claim 9, the identifying the location of the LED light based on the characteristics of the LED light, comprising:

acquiring a baseline formed by connecting the characteristics of the plurality of LED lamps;

determining a position of the LED lamp in the panoramic image based on the baseline.