Loudspeaker array passive acoustic configuration process

Abstract

An example method of operation includes identifying a loudspeaker array profile stored in a memory, the loudspeaker array profile defining characteristics of a loudspeaker array; identifying three-dimensional venue geometry values stored in the memory; defining a virtual receiver to model acoustic characteristics within the venue geometry; defining a number of passive acoustic filter arrangements to perform within a range of passive acoustic filter settings, and each passive acoustic filter setting is unique and has one or more passive acoustic filters to apply to one or more loudspeakers in the loudspeaker array; selecting a performance criteria to apply to the loudspeaker array to represent sound coverage uniformity of the loudspeaker array at a given location throughout the venue geometry; performing a simulation by using the passive acoustic filter settings selected from one or more of the passive acoustic filter arrangements to calculate the performance criteria of the loudspeaker array via the passive acoustic filter settings; identifying an optimized passive acoustic filter setting from a particular arrangement that the loudspeaker array utilizes to achieve optimal uniform sound coverage in the venue geometry; and applying the optimized passive acoustic filter setting to the loudspeaker array.

Description

Background Technology

[0001] The installation of loudspeakers in ceilings, walls, and other locations, and how quickly they can be installed and secured, can be a concern. Furthermore, the acoustic fingerprint of a particular environment varies depending on its size and anatomy. Using feedback and sound to calibrate the environment can be helpful in adjusting aspects such as loudspeaker type, angle, position, output amplitude, and angle. However, feedback and calibration are not always easy to perform, especially for those without proper training. Performing pre-installation analysis of the environmental anatomy using modeling algorithms to identify fundamental design considerations can optimize the audio setup process.

[0002] Furthermore, the directivity function of a practical multi-speaker system is difficult to predict. A polar coordinate pattern extracted from the radiation pattern is used to measure the environment at different frequencies. The audience area of ​​a particular environment, or a specific portion of that area, is typically the target radiation pattern required for a uniform distribution of the audio signal. The intensity level in any direction must offset the distance between the audience and the speaker (the farther the seat, the stronger the signal required). As stated by the inverse square law (ISL), sound intensity is inversely proportional to the distance from the sound source, expressed in decibels (dB) as sound pressure level (SPL) [20 * log(distance / reference)].

[0003] Loudspeaker arrays possess numerous degrees of freedom, allowing manipulation and reshaping of the overall directivity function. In contrast, individual loudspeakers typically exhibit a less consistent directivity shape as a function of frequency compared to an array. Consequently, simple loudspeakers often fail to maintain a usable frequency response within the desired range. Certain degrees of freedom within an array can include the array's orientation / position, the number of elements, the type / directivity of each element, the spread angle between elements, and the amplitude and phase of each element at each frequency.

[0004] To perform calculations or analyses on the acoustic performance of an array, the array's mechanical structure, such as the position and orientation of each array element, must be determined. To model an ideal array, a large number of permutations will be needed to accommodate the possible number of degrees of freedom in the array configuration. Summary of the Invention

[0005] An example embodiment may provide: identifying the number of selected loudspeakers based on stored values; identifying the type of loudspeakers; identifying the spread angle associated with the loudspeakers; selecting one or more digital filters to apply to one or more loudspeakers; performing several filter permutations based on the number of loudspeakers; and sorting one or more results of the filter permutations based on the calculated permutations.

[0006] Another example embodiment may provide: identifying site geometry based on stored values; determining site polarity representation based on site geometry; selecting one or more speaker types to place in the site based on site geometry and one or more target sound pressure levels (SPL); selecting one or more locations in the site to place one or more speakers; determining one or more angles to position one or more speakers in the site at the selected locations; and generating a display model of a speaker arrangement including the selected number of speakers and one or more filters for application to the one or more speakers.

[0007] Another example embodiment may include an apparatus comprising a processor configured to: identify site geometry based on stored values; determine a site polarity representation based on the site geometry; select one or more speaker types for placement in the site based on the site geometry and one or more target sound pressure levels (SPLs); select one or more locations in the site for placement of the one or more speakers; determine one or more angles to position the one or more speakers in the site at the selected locations; and generate a display model of a speaker arrangement including the selected number of speakers and one or more filters for application to the one or more speakers.

[0008] Another example embodiment may include a non-transitory computer-readable storage medium configured to store instructions that, when executed, cause a processor to perform the following operations: identify site geometry based on stored values; determine a site polarity representation based on the site geometry; select one or more speaker types for placement in the site based on the site geometry and one or more target sound pressure levels (SPLs); select one or more locations in the site for placement of one or more speakers; determine one or more angles to position one or more speakers in the site at the selected locations; and generate a display model including a speaker arrangement comprising the selected number of speakers and one or more filters for application to the one or more speakers.

[0009] Another example embodiment may include a method comprising: identifying a loudspeaker array profile; the profile defining characteristics of the loudspeaker array stored in memory; identifying three-dimensional site geometry values ​​stored in memory; defining a plurality of virtual receivers to simulate acoustic characteristics within the site geometry; defining a plurality of passive acoustic filter arrangements to perform within a range of passive acoustic filter settings, wherein each passive acoustic filter setting is unique and includes one or more passive acoustic filters to be applied to one or more loudspeakers in the loudspeaker array; selecting a performance criterion to be applied to the loudspeaker array to represent the uniformity of sound coverage of the loudspeaker array at a given location throughout the site geometry; performing a simulation using a passive acoustic filter setting selected from one or more passive acoustic filter arrangements, through which the performance criterion of the loudspeaker array is calculated; identifying an optimized passive acoustic filter setting from a particular arrangement, using the optimized passive acoustic filter setting, the loudspeaker array achieving optimal uniform sound coverage within the site geometry; and applying the optimized passive acoustic filter setting to the loudspeaker array.

[0010] Another example embodiment may include an apparatus comprising a processor configured to: identify a loudspeaker array profile stored in memory, the loudspeaker array profile defining characteristics of the loudspeaker array; identify three-dimensional site geometry values ​​stored in memory; define a plurality of virtual receivers to simulate acoustic characteristics within the site geometry; define a plurality of passive acoustic filter arrangements to perform within a range of passive acoustic filter settings, wherein each passive acoustic filter setting is unique and includes one or more passive acoustic filters to be applied to one or more loudspeakers in the loudspeaker array; select a performance criterion to be applied to the loudspeaker array to represent its sound coverage uniformity at a given location throughout the site geometry; perform a simulation using a passive acoustic filter setting selected from one or more passive acoustic filter arrangements, through which the performance criterion of the loudspeaker array is calculated; identify an optimized passive acoustic filter setting from a particular arrangement, using which the loudspeaker array achieves optimal uniform sound coverage in the site geometry; and apply the optimized passive acoustic filter setting to the loudspeaker array.

[0011] Another example embodiment may include a non-transitory computer-readable storage medium configured to store instructions that, when executed, cause a processor to perform the following operations: identify a loudspeaker array profile stored in memory, the loudspeaker array profile defining the characteristics of the loudspeaker array; identify three-dimensional site geometry values ​​stored in memory; define a plurality of virtual receivers to simulate acoustic characteristics within the site geometry; define a plurality of passive acoustic filter arrangements to perform within a range of passive acoustic filter settings, each passive acoustic filter setting being unique and including one or more passive acoustic filters to be applied to one or more loudspeakers in the loudspeaker array; select a performance criterion to be applied to the loudspeaker array to represent the uniformity of sound coverage of the loudspeaker array at a given location throughout the site geometry; perform a simulation using a passive acoustic filter setting selected from one or more passive acoustic filter arrangements, through which the performance criterion of the loudspeaker array is calculated; identify an optimized passive acoustic filter setting from a particular arrangement, using which the loudspeaker array can achieve optimal uniform sound coverage within the site geometry; and apply the optimized passive acoustic filter setting to the loudspeaker array. Attached Figure Description

[0012] Figure 1A A user interface for configuring a venue loudspeaker according to an example embodiment is shown.

[0013] Figure 1B A site with complex geometry is shown according to an example embodiment.

[0014] Figure 1C The area for the venue loudspeaker configuration according to an example embodiment is shown.

[0015] Figure 1D The basic site loudspeaker distance area is shown according to an example embodiment.

[0016] Figure 1E Different site polarity configurations at various frequencies are shown according to example embodiments.

[0017] Figure 2A-2C Various loudspeaker enclosure arrays and different spread angles are shown according to example embodiments.

[0018] Figure 3A An example graphical user interface is shown, illustrating a speaker array configuration according to an example embodiment.

[0019] Figure 3B Details of the speaker array enclosure configuration according to an example embodiment are shown.

[0020] Figure 4 A user interface for speaker enclosure mounting configuration according to an exemplary embodiment is shown.

[0021] Figure 5 A user interface for configuring a speaker array according to an exemplary embodiment is shown.

[0022] Figure 6 Another speaker array configuration user interface is shown according to an exemplary embodiment.

[0023] Figure 7 A virtual receiver configuration user interface for identifying the audio characteristics of a site geometry, according to an exemplary embodiment, is shown.

[0024] Figure 8 A table-based user interface for identifying and calculating filter settings and reducing permutations, according to an exemplary embodiment, is shown.

[0025] Figure 9 Another table-based user interface for identifying and calculating filter settings and reducing permutations, according to an exemplary embodiment, is shown.

[0026] Figure 10 A flowchart illustrating a speaker array configuration according to an exemplary embodiment is shown.

[0027] Figure 11 A flowchart illustrating a speaker array configuration according to an exemplary embodiment is shown.

[0028] Figure 12 A system configuration of a computer-readable medium according to an exemplary embodiment is shown. Detailed Implementation

[0029] It is readily understood that the immediate components generally described and illustrated in the accompanying drawings can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of embodiments of at least one of the methods, apparatuses, non-transitory computer-readable media, and systems illustrated in the drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments.

[0030] In one or more embodiments, the immediate features, structures, or characteristics throughout this specification may be combined in any suitable manner. For example, throughout this specification, the phrases “example embodiment,” “some embodiments,” or other similar language are used to refer to the fact that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment. Therefore, the phrases “example embodiment,” “some embodiments,” “other embodiments,” or other similar language appearing throughout this specification do not necessarily refer to the same set of embodiments, and the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0031] Furthermore, although the term "message" may be used in the description of the embodiments, this application is applicable to various types of network data, such as packets, frames, datagrams, etc. The term "message" also includes packets, frames, datagrams, and their equivalents. Moreover, although certain types of messages and signaling may be depicted in the exemplary embodiments, they are not limited to a certain type of message, nor is this application limited to a certain type of signaling.

[0032] An exemplary embodiment provides a speaker configuration simulator that receives various objectives or target values ​​to establish initial speaker setup criteria, and also receives the geometry / anatomy of a specific environment as input. The software application simulator outputs the number of speakers, speaker types, speaker angles and positions, and an audio visualization of the possible propagation of audio throughout the environment. Various speaker / cabinet ratings can be determined using several considerations. These include, for example, the number of cabinets, speaker / driver types, inter-cabinet angles or spread angles, and speaker-related directivity angles, and various filter selections can then be applied. For the purposes of this discussion, internal speaker components will be referred to as drivers as part of the speaker assembly.

[0033] The example embodiments provide a speaker configuration and optimization simulation application that generates potential speaker configurations and ideal output standards. Furthermore, some graphical user interfaces provide a visual implementation of multi-driver speakers and / or speaker arrays. By applying a finite impulse response (FIR) filter to each driver or group of drivers driving one or more speakers, an ideal configuration and output can be achieved according to desired design goals. Additionally, according to the example embodiments, the filter type may include passive acoustic filters.

[0034] The required speaker configuration typically depends on the geometry of the specific venue. For example, venues with flat ground, such as traffic areas, cathedrals, and mosques, require narrower polarity patterns, while venues with tiered seating and balconies, such as theaters, lecture halls, and conference rooms, require wider speaker polarity patterns. Depending on the use case, the venue may also require different speaker polarity patterns. Speaker configurations and optimized use cases can be designed to meet the requirements of such venues. (See reference...) Figure 1C The loudspeaker system can be optimized for lower seat 162 only, but not upper seat 160, or for both lower seat 168 and upper seat 166 simultaneously. Electronic control of settings for different use cases is possible by applying specific FIR filters and / or passive acoustic filters to each driver in the loudspeaker enclosure.

[0035] Figure 1A A multi-speaker configuration for a specific venue, according to an example embodiment, is shown. (Refer to...) Figure 1A This example provides a combination of a single-speaker array (single enclosure) 112 and a dual-speaker array (dual enclosure) 114 as a potential loudspeaker implementation for a specific site with a particular site geometry. For example, the rated value of the dual enclosure could be 96 dB at 158 ​​meters, as indicated by the second line 118; while the rated value of the single enclosure could be 96 dB at 112 meters, as indicated by the first line 116. The modeling required to determine the site target can provide loudspeaker driver configurations to apply selective filters and generate polarity output patterns, which are ideal for certain geometries and desired angles that will be applied to the site audio.

[0036] Figure 1B A site geometry 150 is shown, which has various zones that need to be defined when configuring and optimizing speaker placement. One or more speakers 122 may be placed on a wall or an attachable surface, and the goal is to optimize the audio output potential in the site geometry 124. Figure 1D A simplified site geometry 170 is shown, which includes the following items: speaker position 172, distance D1 178 from the front row to the speaker 172, and distance D2 176 from the last row to the speaker 172. Angle 'θ' 174 represents the angle between distances D1 and D2.

[0037] Figure 1E An example polarity geometry 180 is shown. The low-frequency range of 100-200 Hz exhibits an almost perfect circular geometry 182, and the polarity pattern begins to shrink as the frequency increases. Convergence of the pattern is shown at frequencies of 250-500 Hz 184 and 630-1250 Hz 186. For frequencies of 1600-3750 Hz 188, 4000-8000 Hz 192, and finally the high-frequency range of 10000 Hz-16000 Hz 194, the pattern continues to shrink.

[0038] Figure 2A A dual-speaker array configuration according to an example embodiment is shown. (Refer to...) Figure 2A Example dual speaker array 212 is shown as having a specific splay plate that defines the angle between the speakers and their relative positions when mounted from a wall or ceiling (i.e., the splay angle). Figure 2B The spread angles in array 214 include a smaller spread angle 222 defined by a smaller spread angle and a larger spread angle 226 defined by a larger spread plate. In this example, the total number of speakers can include three. Figure 2CThe image shows a four-speaker array configuration 216 with three different spread angles.

[0039] Figure 3A An example loudspeaker array model according to an example embodiment is shown. (Refer to...) Figure 3A The modeling application displays cabinet dimensions, location, spread angle, and other information that can be set during the simulation process used to create the polarity patterned output signal. The desired coverage pattern can be achieved by selecting certain speaker models from a memory that records the speaker's output characteristics and possible mounting bracket array configuration data. Basic array information may include: cabinet type 314 (speaker model), aiming angle (angle relative to site geometry), and spread angle 312 (angle between one or more speakers). The use of multiple drivers (within one or more speakers) and amplifier channels for a set of speakers provides the ability to beamform a wide range of vertical patterns based on different cabinet configurations. Vertical patterns are generated by varying the phase and / or amplitude of the driver pairs using linear-phase finite-impulse-response (FIR) filters. The desired vertical coverage angle is mostly between 20 and 100 degrees, with most designs approaching 40 degrees.

[0040] Figure 3B A detailed loudspeaker array model with positional specifications and aiming angles according to an example embodiment is shown. (Refer to...) Figure 3B The dual-casing configuration 350 includes a housing 354 with a driver placement 356 based on a specific driver placement angle 358. A Y-axis position 366 is shown together with a specific aiming angle 362. An X-axis position 364 is also illustrated for accurate driver placement. The angle between the housings is the unfolding angle. The driver placement position is the basis for calculating the driver spacing.

[0041] Figure 4 An example graphical user interface for a single speaker model according to an example embodiment is shown. (Refer to...) Figure 4 The exemplary illustration includes a detailed schematic diagram 400 of a speaker enclosure 414 configured with a mounting bracket 412 at a specific spread angle 416. These values ​​can be varied to accommodate different polarity spectral targets.

[0042] Example implementations may include an array setup that includes the array's orientation / position, the number of loudspeaker elements, the type / directivity of each element, the spread angle between elements, and the amplitude and phase of each element at each frequency. System designers simply input the acoustic goals / characteristics they need to achieve for the audience area, and the software application automatically calculates the required number of cabinets, cabinet type, spread angle between each cabinet, aiming angle of the entire array, and finally automatically selects the passive acoustic filter settings for each cabinet to provide the most uniform response possible throughout the seating area.

[0043] The configuration application gathers room geometry, speaker placement, and user-specified design goals. The array configurator algorithm analyzes the room geometry and mechanically configures the line array system (number of cabinets, model, and deployment angle) to match the speaker's acoustic radiation pattern to the room geometry. This process provides multiple candidates, and after the line array is configured, passive acoustic filters can be used to optimize system performance. The algorithm selects one or more filters that achieve optimal performance (from millions of filter arrangements) based on predetermined criteria. Filter optimization is performed on each candidate, and the best candidate is selected.

[0044] According to an example embodiment, a process is provided for identifying a sound source definition using a site polarity definition (site polarity). Site polarity is the ISL variation described relative to the angular variation of the site area viewed from the sound source location. This provides a site ISL / angle function in dB / deg. Determining a mechanical array definition using site polar coordinates may include determining the acoustic sum of the array based on the superposition variables and directivity of each element in space. Site polar coordinates are used to generate targets with approximate general intensity.

[0045] The first operation may include assessing the field polarity by identifying the change in angle with respect to the ISL (the derivative of the field polarity or the slope of the curve), which is proportional to the acoustic interaction of the modules. When the derivative of the ISL is small, the array needs to be in a constant curvature state, using a wider spread angle (no module interaction). When the numerical derivative is high, the array needs higher interaction to form a high-intensity beam structure. The derivative function "F" of the ISL can be used to create the spread angle definition function, where: spread curve = K / (R*F), F = d / dθ(ISL), R = sqrt(r / min(r)), K = scaling constant, θ = angle, R = distance. The constant K allows the generated curve to be scaled for different array sizes and can be applied to arrays of any size. R is used to implement a weighting function for the reference actual distance. R is the normalized value of D1 (distance to the nearest seat in the venue). D2 is the distance to the farthest seat in the venue. F is the derivative of the ISL, in dB / deg. The K value, expressed in dB, allows for the measurement of the spread angle in degrees. In one example, if K = -6 dB, the spread curve can be determined. The loudspeaker pattern can be defined by -6 dB; for example, a 90-degree by 40-degree loudspeaker indicates that its directivity is -6 dB at + / -45 degrees horizontal polarity and + / -20 degrees vertical polarity. This information can be converted to dB / degree, for example, -6 / 20 = -0.3 for the vertical direction.

[0046] According to the example embodiment, an "unfolded curve" is used to generate the actual speaker selection. The 'F*R' function represents a shape defined by the distance and angle from the speaker to the audience, and is unrelated to the actual speaker. However, the unfolded curve (the inverse of 'F*R' multiplied by 'K' (a scaling constant)) provides a translation that directly tracks the speaker selection and azimuth angle. A local directivity of K = -6dB can serve as the basis for selecting actual speakers, such as those displayed in the GUI as optional and recommended, and demonstrating where certain speaker configurations in the audience area of ​​the venue can successfully operate for optimal sound distribution. Applications can select the number of speakers, speaker type, azimuth aiming of the entire array, and individual speaker angles selected in the form of a spreadsheet.

[0047] The application first determines the target array size using input variables. This size is determined through direct calculation based on the array's target sound pressure level (SPL) output, target low-frequency control, and whether the preferred number of components (e.g., budget, structure, etc.) is predetermined. This calculation sets reasonable maximum / minimum values ​​for the number of array components and determines the range of array size. After determining the minimum / maximum array size, the process iterates over the 'K' value. 'K' is in dB, so the unfolded curve has units of angle, representing the design angle for the speaker's vertical directivity. The speaker array must cover the entire room, requiring its total coverage angle to be equal to the total angle of the site polarity (room geometry). If the unfolded curve is plotted relative to the site polarity, the speaker design angles can begin to be matched with their positions in the array. The application of determining the unfolded plate angles is essentially the optimal design variable. The unfolded curve is implemented as possible unfolded plate values ​​and compared to the site polarity. This defines the value and number of each unfolded plate that matches its corresponding position in the array. Rules are established for the iterative process to achieve optimal matching with the product line (e.g., which plates match which speakers, and basic rules regarding these selections). For example, for a specific array type, choosing the transition between a five-degree box and a fifteen-degree box is a trigger point in the application. The process discretizes the K value for each iteration by unfolding the curve and collects each unique array solution within the minimum / maximum array size range and total array angle requirements.

[0048] The number of loudspeaker elements used in a loudspeaker array depends on various factors. Specific directivity features can be achieved through a variety of array sizes, types, and deployment angles. Deployment curve calculations can be performed by varying the 'K' value for a given venue polarity, resulting in a set of arrays of different sizes. The optimal loudspeaker array size can take into account factors such as the required SPL, the directivity frequency control range, and the complexity of the audience area. User-submitted input can serve as the basis for the target array size. The deployment curve is then adjusted based on the target size.

[0049] When selecting loudspeaker array characteristics, two types of modules are available for this example: a 5-degree waveguide and a 15-degree waveguide. When connecting a 5-degree cabinet, there are four expansion plates that allow for expansion angles of [0, 1, 25, 2, 5, 5]. When connecting a 15-degree cabinet, the same expansion plates produce expansion angles of [10, 11.25, 12.5, 15]. The plates can also be used to connect 5-degree and 15-degree cabinets, producing expansion angles of [5, 6.25, 7.5, 10]. The expansion curves must be quantified for the various angle options to best fit the target array size.

[0050] The unfolding curve is calculated based on information that provides accurate and useful results. Different "K" values ​​are evaluated to provide the array configuration best suited to the shape of the site's poles. By identifying the optimal 3-4 options, the range of potential solutions can be narrowed down. The target sound pressure level (SPL) can also be used as a basis for array selection. In a smaller auditorium, the SPL might be 90 dBA, while in a larger stadium, the SPL might be 105 dBA. Furthermore, the offset angle of the last row can be used as a positive value when it exceeds the range of the last row (overshoot), and as a negative value when it does not reach the range of the last row (undershoot). This can be useful when there are overflow areas, standing areas, etc., behind the last row, or in scenarios where the range is not reached, such as when there is a wall in the last row, which may cause reflections if the sound exceeds the last row.

[0051] Figure 5 A user interface for configuring a speaker array according to an example embodiment is shown. (Refer to...) Figure 5 The user interface 500 is used to report recommended speaker array configurations. Cabinet 512 is the type of speaker in the array, which may vary. Frame angle 516 is associated with each cabinet included in the array, and also with spread angle 514. Attenuation 522 / 524 is also expressed in dB.

[0052] Figure 6 Another speaker array configuration user interface according to an example embodiment is shown. (Refer to...) Figure 6 The user interface 600 includes one or more target audience areas 610 and specific array characteristics, such as minimum target sound pressure level (SPL) 612, optimized level in Hz 614, last row offset in degrees 616, and cabinet type and quantity options 618.

[0053] Once the speaker cabinet selection criteria, along with the deployment angle, aiming angle, and other standards, are determined, the passive acoustic filters to be applied must be identified and applied according to the optimization criteria. There may be a large number of choices; in this example, 55 passive acoustic filters are available. This provides an exponential number of calculations, such as 55 to the power of the number of cabinets. Therefore, four speakers would produce 55 to the power of 4, or 9,150,625 possible filter arrangements. The filter selection that provides the best performance is chosen, and second, third, and so on can also be displayed in the user interface.

[0054] Figure 7 A virtual receiver configuration user interface for identifying audio characteristics of a site geometry, according to an example embodiment, is shown. (Refer to...) Figure 7 The venue 700 is shown as a virtual environment with a set of virtual receivers / microphones 712 positioned at a predetermined distance (i.e., 1 meter apart) in the area of ​​interest, such as the geometry whose audio characteristics are being analyzed. The process of determining the ideal filter configuration requires a comparative basis to determine the coverage uniformity best suited to the geometry being analyzed. The virtual receivers 712 can be simulated with uniform placement for all areas of interest to the audience. In this example, the virtual receivers are positioned along a single axis with a spacing of 1 meter.

[0055] This process may include calculating the octave sound pressure level (SPL) for each virtual receiver 712, and then calculating the standard deviation (STD). For this example, the filter setting with the lowest STD at 4kHz-3 octaves can be considered the optimal filter setting. The STD can be calculated using one filter setting, and then summed using magnitude and phase balloons. To avoid iterative filter settings, a dynamic reduction in the permutation range must be performed for optimization. For each cabinet with 10 filters, "0" in the filter index (0-9) indicates no attenuation, and "9" indicates maximum attenuation. A linear increase in the number of cabinets (which increases linearly with the attenuation of the filter index) naturally results in 10 x 10 x 10 x 10 = 10,000 filter settings (i.e., 0000-9999). One approach is to remove acoustically redundant filter settings. In one example, this would include filter settings where the values ​​for all four cabinets (A, B, C, D) are identical: 0,0,0,0, 1,1,1,1, 2,2,2,2, 9,9,9,9, etc. In another example, settings can be considered redundant if the same value is added to or subtracted from each set of filter indices. For example, if the values ​​for the four cabinets are 4, 7, 9, 5, subtracting 1 gives 3, 6, 8, 4; subtracting another 1 gives 2, 5, 7, 3; and subtracting another 2 gives 0, 3, 5, 1. This uniform variation across all cabinets is redundant, and these values ​​can also be removed. Each cabinet value can be 0-9, and when one cabinet's value differs from another's, subsequent variations are also redundant if modified uniformly. In this example, at least one of the four numbers must be "0" to eliminate redundancy.

[0056] Continuing with the same example, the optimization process can begin with indices 0,0,0,0 and end with a set of values ​​A, B, C, D, provided that each of these values ​​is an acoustic critical point when compared graphically. In this example, at least one value is "0". This initial approach has already involved 10,000 potential options and reduced them to 4,000. However, this process can include other operations to reduce the total number of permutations required. To determine the acoustic critical point (ACP), the filter index on the enclosure is 1, which can increase the value of one enclosure from 0 to 9 while keeping the other enclosures unchanged.

[0057] In one example, S(i) is a function of STD as a function of the filter exponent (i). If a mathematical critical point exists in the range [0,9], then it can be inferred that there is one and only one critical point. Acoustic critical point (i) ac ) = mathematical critical point, and i ac The following condition must be met: S(i) ac ) <S(i ac -1) and S(i ac ) <S(i ac +1). If there is no mathematical critical point, if STD(0)≤STD(1), then the function is either increasing or flat in the range [0,9], such that i ac =0. If std(0) > std(1), then the function decreases in the range [0,9], making i = 0. ac =9. The Acoustic Critical Point (ACP) can be easily determined by plotting STD (dB) on the Y-axis and the filter index on the X-axis (0-9 in this example). As the value continues to increase linearly (positive slope), the first value will be the lowest STD. If the linear path is downward, the lowest STD will be the last calculated value (negative slope). If the graph is non-linear, the identified lowest point will be the most ideal value.

[0058] Identifying ACPs allows the process to dynamically reduce the permutation size. The definition of optimized filter settings provides filter settings (A)(B)(C)(D), where (A)(B)(C)(D) is the optimized filter setting if A, B, C, and D are all ACPs.

[0059] Therefore, S[(A)(B)(C)(D)] < the following:

[0060] S[(A+1)(B)(C)(D)]

[0061] S[(A-1)(B)(C)(D)]

[0062] S[(A)(B+1)(C)(D)]

[0063] S[(A)(B-1)(C)(D)]

[0064] S[(A)(B)(C+1)(D)]

[0065] S[(A)(B)(C-1)(D)]

[0066] S[(A)(B)(C)(D+1)]

[0067] S[(A)(B)(C)(D-1)]

[0068] Identifying an asymptotic path to an optimized filter setting can provide an iterative process from the bottom box to the top box. For example, for (A)(B)(C)(D), is D an ACP? If yes, go to C; otherwise, increment D by 1. Next, is C an ACP? If yes, go to B; otherwise, increment C by 1. Next, is B an ACP? If yes, go to A; otherwise, increment B by 1. Next, is A an ACP? If not, increment A by 1. This iteration is then repeated until an optimized filter setting is determined.

[0069] Figure 8 A table-based user interface for identifying and calculating filter settings and reducing permutations, according to an example embodiment, is shown. (Refer to...) Figure 8 Table 800 includes Example 812, which shows rows with a step size of "0" and all filter settings of 0. STD is 1.9 dB, and the sum is 0. A new filter setting is added to the sequence as any filter index increases by 1. The last row has an index of 7, STD of 0.5, and a filter index of 7. The last row represents the best value calculated so far. Based on the example of reducing permutations, the best case for the number of basic operations per step is "1", and the worst case is "4". Iteration is repeated until an optimized filter setting is found. The asymptotic path to determine the optimized filter setting can include a series of filter settings. It starts with (0)(0)(0)(0) and ends with the optimized filter setting. A new filter setting is added to the sequence as any filter index increases by 1. STD is smaller with each step than the previous step. The permutation size is dynamically reduced with each step, and at any step, the filter index = step index + Σ of the number of basic operations per step has a best result of "1" and a worst result of "4". When (0)(0)(0)(0)(0) is the optimized filter setting, the number of operations is minimized (best case 1). When (0)(9)(9)(9)(9) or (9)(0)(9)(9)(0)(9) or (9)(9)(9)(0)(0) are the optimized filter settings, the number of operations is maximized (worst case) to 27. The worst-case basic operation count is 27 x 4, or 108 basic operations. For four enclosures (see... Figure 9 In Table 900, the 108 basic operations for 10 filter values ​​(0-9) are far fewer than the traditional method that performs thousands of operations.

[0070] In the worst-case computational case, the expression for filter computation can provide the following: number of bins = n (2 ≤ n ≤ 24), number of filters = k, number of basic operations per step = n, number of steps = (k-1)*(n-1); T(n) = n*(k-1)*(n-1). Non-optimal methods do not reduce permutations and require T( n )=(k n ). Figure 9 Another tabular user interface for identifying and calculating filter settings and reducing permutations, according to an example embodiment, is shown. (Reference) Figure 9 The optimization method (912) for 4 boxes produces 108 operations. For 10 boxes, the number of operations is equal to 810 (914).

[0071] Figure 10 A flowchart illustrating a speaker array configuration according to an example embodiment is shown. (Reference) Figure 10Flowchart 1000 provides the following steps: identifying site geometry based on stored values ​​(1002); determining site polarity representation based on site geometry (1004); selecting one or more speaker types to place in the site based on spatial geometry and one or more target sound pressure levels (SPL) (1006); selecting one or more locations in the site to place one or more speakers (1008); identifying one or more angles to position one or more speakers in the site at the selected locations (1010); and generating a display model of the speaker arrangement (which includes a selected number of speakers and one or more filters applied to one or more speakers) (1012). Spatial geometry may include the sum of multiple volumes. Site polarity representation is based on the ratio of the distance from the speaker to the farthest seat in the site to the distance from the speaker to the nearest seat in the site. Determining one or more angles to position one or more speakers includes: selecting a frame angle based on targets to mount one or more speakers and a flat angle to position one or more speakers within the corresponding enclosures. The process may further include: determining the deployment angle applied to the loudspeakers based on the derivative of the scaling constant divided by the angle between the front and back rows; and determining multiple different target sound pressure levels (SPLs) to be achieved in the spatial geometry when the geometry produces multiple back row offset angles; identifying a first of one or more loudspeaker types as a first candidate loudspeaker type for the display model, and identifying a second of one or more loudspeaker types as a second candidate loudspeaker type for the display model, wherein the number of loudspeakers in the enclosure of the second loudspeaker type is greater than the number of loudspeakers in the enclosure of the first loudspeaker type, and the deployment angle of the first loudspeaker type is greater than the deployment angle of the second loudspeaker type. Selecting one or more locations in the site to place one or more loudspeakers includes: determining the optimal distance between the first and second loudspeaker types to achieve the target sound pressure level (SPL).

[0072] Figure 11 A flowchart illustrating a speaker array configuration according to an example embodiment is shown. (Refer to...) Figure 11The process 1100 includes: identifying a loudspeaker array profile stored in memory (1102), which defines the characteristics of the loudspeaker array; identifying three-dimensional site geometry values ​​stored in memory (1104); defining multiple virtual receivers to simulate acoustic characteristics within the site geometry (1106); defining a plurality of passive acoustic filter arrangements to perform within a range of passive acoustic filter settings, wherein each passive acoustic filter setting is unique and includes one or more passive acoustic filters to be applied to one or more loudspeakers in the loudspeaker array (1108); selecting the filters to be applied to the loudspeaker array. The performance criteria of the loudspeaker array are used to represent the uniformity of sound coverage of the loudspeaker array at a given location throughout the site geometry (1110); a simulation is performed using a passive acoustic filter setting selected from one or more passive acoustic filter arrangements, through which the performance criteria of the loudspeaker array are calculated (1112); an optimized passive acoustic filter setting is identified from a particular arrangement, using which the loudspeaker array achieves optimal uniform sound coverage in the site geometry (1114); and the optimized passive acoustic filter setting is applied to the loudspeaker array (1116).

[0073] The arrangement of passive acoustic filters depends on the number of loudspeakers in the array and the number of passive acoustic filters applied to each loudspeaker. The passive acoustic filter arrangement size is the number of passive acoustic filters raised to the power of the loudspeaker quantity of the array. The performance criterion is the standard deviation of the direct sound field sound pressure level calculated at multiple virtual receivers used in the acoustic characteristics simulation, and the loudspeaker array includes multiple loudspeakers, one or more loudspeaker models, loudspeaker array positions, one or more loudspeaker positions, one or more aiming angles, and one or more spread angles. The process may include: identifying acoustically redundant passive acoustic filter settings in the arrangement, and performing the arrangement without redundancy.

[0074] The process may also include: dynamically converging to the optimal passive acoustic filter settings, thereby achieving higher coverage uniformity in each simulation cycle compared to the previous simulation cycle under optimal performance criteria; establishing an optimized set of passive acoustic filter settings, where each filter setting provides a higher performance loudspeaker setting than the previously calculated settings; selecting the last setting from the determined set as the optimized passive acoustic filter setting; and defining and using acoustic critical points to determine the optimized passive acoustic filter setting. Furthermore, the types, manufacturers, and models of loudspeakers, enclosures, and array structures can vary, depending on site requirements and available options.

[0075] The operation of the methods or algorithms related to the embodiments disclosed herein may be embodied directly in hardware, in a computer program executed by a processor, or in a combination of both. The computer program may be embodied on a computer-readable medium, such as a storage medium. For example, the computer program may reside in random access memory (“RAM”), flash memory, read-only memory (“ROM”), erasable and programmable read-only memory (“EPROM”), electrically erasable and programmable read-only memory (“EEPROM”), registers, hard disks, removable disks, optical disc read-only memory (“CD-ROM”), or any other form of storage medium known in the art.

[0076] Figure 12 This is not intended to suggest any limitation on the use or scope of the embodiments described herein. In any case, compute node 1200 is capable of implementing and / or performing any of the functions described herein.

[0077] Computing node 1200 contains a computer system / server 1202, which can operate with many other general-purpose or special-purpose computing system environments or configurations. Examples of well-known computing systems, environments, and / or configurations that can be applied to computer system / server 1202 include, but are not limited to, personal computer systems, server computer systems, lightweight clients, rich clients, handheld or laptop devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, microcomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the aforementioned systems or devices.

[0078] The computer system / server 1202 can be described in the general context of computer system execution instructions (e.g., program modules) executed by the computer system. Generally, program modules can include routines, procedures, objects, components, logic, data structures, etc., that perform specific tasks or implement specific abstract data types. The computer system / server 1202 can be used in a distributed cloud computing environment where tasks are executed by remote processing devices connected via a communication network. In a distributed cloud computing environment, program modules can reside in local and remote computer system storage media, including memory storage devices.

[0079] like Figure 12 As shown, the computer system / server 1202 in the cloud computing node 1200 is displayed in the form of a general-purpose computing device. The components of the computer system / server 1202 may include, but are not limited to, one or more processors or processing units 1204, system memory 1206, and buses that couple various system components, including system memory 1206, to the processor 1204.

[0080] A bus represents one or more of several types of bus architectures, including memory buses or memory controllers, peripheral buses, accelerated graphics ports, and processor or local buses that use any of the various bus architectures. Examples are not limiting; such architectures include the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MCA) bus, the Enhanced ISA (EISA) bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0081] Computer system / server 1202 typically includes various computer system readable media. This media can be any usable media accessible to computer system / server 1202, and includes volatile and non-volatile media, removable media, and non-removable media. In one embodiment, system memory 1206 implements the flowcharts in other figures. System memory 1206 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 1210 and / or cache memory 1212. Computer system / server 1202 may also include other removable / non-removable, volatile / non-volatile computer system storage media. For example, storage system 1214 may be provided to read from and write to non-removable, non-volatile magnetic media (not shown, commonly referred to as a "hard disk drive"). Although not shown, disk drives for reading from and writing to removable, non-volatile disks (e.g., "floppy disks") and optical disc drives for reading from or writing to removable, non-volatile optical discs (e.g., CD-ROMs, DVD-ROMs, or other optical media) may also be provided. In this case, each drive may be connected to a bus via one or more data media interfaces. As will be further described and depicted below, memory 1206 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of various embodiments of this application.

[0082] A program / utility 1216 having at least one set of program modules 1218 may be stored in memory 1206 by way of example and not limitation. Memory 1206 may also store an operating system, one or more applications, other program modules, and program data. Each of the operating system, one or more applications, other program modules, and program data, or some combination thereof, may include an implementation of a network environment. Program modules 1218 generally perform the functions and / or methods of various embodiments of the claims described herein.

[0083] As those skilled in the art will understand, various aspects of this application may be embodied as a system, method, or computer program product. Therefore, various aspects of this application may take the form of a completely hardware embodiment, a completely software embodiment (including firmware, resident software, microcode, etc.), or an embodiment combining software and hardware aspects, all of which may generally be referred to herein as a “circuit,” “module,” or “system.” Furthermore, various aspects of this application may take the form of a computer program product embodied in one or more computer-readable media having computer-readable program code.

[0084] Computer system / server 1202 can also communicate with one or more external devices 1220, such as a keyboard, pointing device, display 1222, etc.; one or more devices that enable a user to interact with computer system / server 1202; and / or any device that enables computer system / server 1202 to communicate with one or more other computing devices (e.g., network interface card, modem, etc.). Such communication can be performed via I / O interface 1224. Furthermore, computer system / server 1202 can also communicate with one or more networks, such as a local area network (LAN), a general wide area network (WAN), and / or a public network (e.g., the Internet), via network adapter 1226. As shown, network adapter 1226 communicates with other components of computer system / server 1202 via a bus. It should be understood that, although not shown, other hardware and / or software components can also be used in conjunction with computer system / server 1202. Examples include, but are not limited to, microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archiving storage systems.

[0085] Those skilled in the art will understand that "system" can be embodied in a personal computer, server, console, personal digital assistant (PDA), mobile phone, tablet computing device, smartphone, or any other suitable computing device, or a combination of devices. Describing the above functions as being performed by a "system" is not intended to limit the scope of this application in any way, but rather to provide one example among many embodiments. In fact, the methods, systems, and apparatuses disclosed herein can be implemented in localized and distributed forms consistent with computing technologies.

[0086] It should be noted that some system features described in this specification are presented in the form of modules to emphasize their implementation independence. For example, modules can be implemented as hardware circuits, including custom-designed very large-scale integrated circuits (VLSI) or gate arrays, off-the-shelf semiconductors (such as logic chips, transistors, or other discrete components). Modules can also be implemented in programmable hardware devices, such as field-programmable gate arrays, programmable array logic, programmable logic devices, graphics processing units, or similar devices.

[0087] Modules can also be implemented, at least partially, in software so that they can be executed by various types of processors. For example, an identifiable unit capable of executing code may include one or more physical or logical blocks of computer instructions that can be organized into objects, procedures, or functions. However, the executable files of an identified module are not necessarily physically together; they can consist of different instructions stored in different locations. When these instructions are logically connected, they constitute a module and achieve the module's intended purpose. Furthermore, modules can be stored on computer-readable media, such as hard disk drives, flash memory devices, random access memory (RAM), magnetic tape, or any other medium used for storing data.

[0088] In fact, executable code modules can be a single instruction or multiple instructions, and can even be distributed across multiple different code segments, different programs, and multiple storage devices. Similarly, operational data can be identified and described within modules, and can be represented in any suitable form and organized in any suitable data structure type. Operational data can be collected as a single dataset or distributed across different locations (including across different storage devices), and can exist at least partially as electronic signals within a system or network.

[0089] It is readily understood that the application components described and illustrated in the figures herein can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments is not intended to limit the scope of this disclosure, but merely represents selected embodiments of this application.

[0090] It will be readily understood by those skilled in the art that the above-described contents can be implemented with steps in a different order and / or with hardware elements in a different configuration than those disclosed. Therefore, although this application has been described with reference to these preferred embodiments, certain modifications, variations, and alternative structures will be readily understood by those skilled in the art.

[0091] While preferred embodiments of this application have been described, it should be understood that the described embodiments are merely illustrative, and the scope of this application is limited only by the appended claims when taking into account the full scope of equivalents and modifications (e.g., protocols, hardware devices, software platforms, etc.).

Claims

1. A method for configuring a loudspeaker, comprising: Identify the speaker array profile stored in memory, which defines the characteristics of the speaker array; Identify the three-dimensional site geometry values ​​stored in the memory; Define multiple virtual receivers to simulate the acoustic characteristics within the site geometry; Define several passive acoustic filter arrangements to perform within a range of predefined passive acoustic filter settings, wherein each passive acoustic filter setting is unique and includes one or more passive acoustic filters to be applied to one or more speakers in the speaker array; Select a performance criterion to apply to the loudspeaker array to represent the uniformity of sound coverage of the loudspeaker array at a given location throughout the site geometry; The simulation is performed using a passive acoustic filter setting selected from one or more passive acoustic filter arrangements in the passive acoustic filter arrangement, and the performance criteria of the loudspeaker array are calculated via the passive acoustic filter setting. The speaker array identifies optimized passive acoustic filter settings from a specific arrangement to achieve optimal uniform sound coverage within the site geometry. The optimized passive acoustic filter settings are applied to the speaker array.

2. The method according to claim 1, wherein, The arrangement of the passive acoustic filters depends on the number of speakers in the speaker array and the number of passive acoustic filters applied to each speaker.

3. The method according to claim 2, wherein, The size of the passive acoustic filter array is a power of the number of speakers in the speaker array, which is the number of passive acoustic filters.

4. The method according to claim 1, wherein, The performance standard is the standard deviation of the direct field sound pressure level used in the acoustic characteristic simulation, which is calculated at multiple virtual receivers, and wherein the loudspeaker array includes multiple loudspeakers, one or more loudspeaker models, loudspeaker array positions, one or more loudspeaker positions, one or more aiming angles, and one or more deployment angles.

5. The method according to claim 1, comprising: Identify the acoustically redundant passive acoustic filter settings in the arrangement; as well as The permutation is performed without any redundant permutations.

6. The method according to claim 1, comprising: Dynamically converges to the optimal passive acoustic filter settings, resulting in higher coverage uniformity for each simulation cycle compared to the previous simulation cycle at the best performance criterion. An optimized set of passive acoustic filter settings is established, where each filter setting provides a higher performance speaker setting than previously calculated settings; and Select the last setting from the identified set as the optimized passive acoustic filter setting.

7. The method of claim 6, comprising: Define and use acoustic critical points to identify the optimized passive acoustic filter settings.

8. An apparatus for configuring a loudspeaker, comprising: The processor is configured as follows: Identify the speaker array profile stored in memory, which defines the characteristics of the speaker array; Identify the three-dimensional site geometry values ​​stored in the memory; Define multiple virtual receivers to simulate the acoustic characteristics within the site geometry; Define several passive acoustic filter arrangements to perform within a range of predefined passive acoustic filter settings, wherein each passive acoustic filter setting is unique and includes one or more passive acoustic filters to be applied to one or more speakers in the speaker array; Select a performance criterion to apply to the loudspeaker array to represent the uniformity of sound coverage of the loudspeaker array at a given location throughout the site geometry; The simulation is performed using a passive acoustic filter setting selected from one or more passive acoustic filter arrangements in the passive acoustic filter arrangement, and the performance criteria of the loudspeaker array are calculated via the passive acoustic filter setting. The speaker array identifies optimized passive acoustic filter settings from a specific arrangement to achieve optimal uniform sound coverage within the site geometry. The optimized passive acoustic filter settings are applied to the speaker array.

9. The apparatus according to claim 8, wherein, The arrangement of the passive acoustic filters depends on the number of speakers in the speaker array and the number of passive acoustic filters applied to each speaker.

10. The apparatus according to claim 9, wherein, The size of the passive acoustic filter array is a power of the number of speakers in the speaker array, which is the number of passive acoustic filters.

11. The apparatus of claim 8, wherein the performance criterion is the standard deviation of the direct field sound pressure level used in acoustic characteristic simulation, the direct field sound pressure level being calculated at a plurality of virtual receivers, and wherein, The loudspeaker array includes multiple loudspeakers, one or more loudspeaker models, loudspeaker array positions, one or more loudspeaker positions, one or more aiming angles, and one or more deployment angles.

12. The apparatus according to claim 8, wherein, The processor is also configured to: Identify passive acoustic filter settings with acoustic redundancy in the arrangement; and The permutation is performed without any redundant permutations.

13. The apparatus according to claim 8, wherein, The processor is also configured to: Dynamically converges to the optimal passive acoustic filter settings, resulting in higher coverage uniformity for each simulation cycle compared to the previous simulation cycle at the best performance criterion. An optimized set of passive acoustic filter settings is established, where each filter setting provides a higher performance speaker setting than previously calculated settings; and Select the last setting from the identified set as the optimized passive acoustic filter setting.

14. The apparatus of claim 13, wherein the processor is further configured to: define and use acoustic critical points to identify the optimized passive acoustic filter settings.

15. A non-transitory computer-readable storage medium configured to store instructions, which, when executed, cause a processor to perform the following operations: Identify the speaker array profile stored in memory, which defines the characteristics of the speaker array; Identify the three-dimensional site geometry values ​​stored in the memory; Define multiple virtual receivers to simulate the acoustic characteristics within the site geometry; Define several passive acoustic filter arrangements to perform within a range of predefined passive acoustic filter settings, wherein each passive acoustic filter setting is unique and includes one or more passive acoustic filters to be applied to one or more speakers in the speaker array; Select a performance criterion to apply to the loudspeaker array to represent the uniformity of sound coverage of the loudspeaker array at a given location throughout the site geometry; The simulation is performed using a passive acoustic filter setting selected from one or more passive acoustic filter arrangements in the passive acoustic filter arrangement, and the performance criteria of the loudspeaker array are calculated via the passive acoustic filter setting. The speaker array identifies optimized passive acoustic filter settings from a specific arrangement to achieve optimal uniform sound coverage within the site geometry. The optimized passive acoustic filter settings are applied to the speaker array.

16. The non-transitory computer-readable storage medium according to claim 15, wherein, The arrangement of the passive acoustic filters depends on the number of speakers in the speaker array and the number of passive acoustic filters applied to each speaker.

17. The non-transitory computer-readable storage medium configured to store instructions according to claim 16, wherein, when executed, the instructions cause a processor to perform an operation, wherein, The size of the passive acoustic filter array is a power of the number of speakers in the speaker array, which is the number of passive acoustic filters.

18. The non-transitory computer-readable storage medium according to claim 15, wherein, The performance standard is the standard deviation of the direct field sound pressure level used in the acoustic characteristic simulation, which is calculated at multiple virtual receivers, and wherein the loudspeaker array includes multiple loudspeakers, one or more loudspeaker models, loudspeaker array positions, one or more loudspeaker positions, one or more aiming angles, and one or more deployment angles.

19. The non-transitory computer-readable storage medium according to claim 15, wherein, The processor is also configured to perform the following operations: Identify passive acoustic filter settings with acoustic redundancy in the arrangement; and The permutation is performed without any redundant permutations.

20. The non-transitory computer-readable storage medium according to claim 15, wherein, The processor is also configured to perform the following operations: Dynamically converges to the optimal passive acoustic filter settings, resulting in higher coverage uniformity for each simulation cycle compared to the previous simulation cycle at the best performance criterion. An optimized set of passive acoustic filter settings is established, where each filter setting provides a higher performance speaker setting than previously calculated settings; and Select the last setting from the identified set as the optimized passive acoustic filter setting.

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