Smart speaker channel adaptive recognition method and system

Abstract

The application provides a kind of smart speaker sound channel adaptive identification method, applied to sound system, method includes the following steps: each smart speaker in sound system is paired with each other, and the signal strength indication value of each pair of smart speaker is collected to generate signal strength indication cluster array not less than first threshold quantity;Characteristic parameter set is generated based on signal strength indication cluster array, and characteristic parameter includes mean value;According to characteristic parameter set, the first smart speaker sound channel is identified using characteristic parameter comparison method;According to characteristic parameter set and the first smart speaker sound channel identification result, the remaining sound channel smart speaker is identified using grouping identification method.The smart speaker sound channel adaptive identification method disclosed by the application avoids the tedious manual configuration operation of the user through intelligent sound channel adaptive identification, significantly improves the efficiency of the sound channel identification of the speaker, uses characteristic parameters and signal strength comparison to ensure the accuracy and consistency of the sound channel identification process of the speaker, and reduces the error identification caused by human interference.

Description

Technical Field

[0001] This invention relates to the field of smart speaker technology, and in particular to a method for adaptive channel recognition in smart speakers. Background Technology

[0002] The widespread adoption of wireless smart speakers has provided users with a completely new auditory experience, especially home theater audio systems composed of smart speakers, which further demonstrate the advantages of artificial intelligence in serving family life. However, current methods for configuring the channels of wireless speakers still rely on manual operation by the user, which presents many inconveniences.

[0003] Taking a 5.1 home theater audio system as an example, the channel information of each smart speaker is typically: center speaker channel, subwoofer channel, left front channel, right front channel, left rear channel, and right rear channel. Correspondingly, each smart speaker with different channel information plays audio signals from different channels. In existing technology, users need to manually identify the type of audio system, then match the audio source channels with the speakers one by one, and establish a wireless connection. This configuration method is not only time-consuming and laborious, but also places high demands on the user's operational skills, making it difficult to achieve true intelligence and automation.

[0004] As users increasingly demand higher levels of intelligence and convenience from audio equipment, the existing manual configuration methods are clearly insufficient to meet these needs. Summary of the Invention

[0005] To address the problems in existing technologies, the present invention aims to provide an adaptive channel recognition method for smart speakers, which solves the problems of complex manual operation, low efficiency, and insufficient user experience in existing smart speaker system channel configuration methods, and provides an intelligent and automated adaptive channel recognition method for smart speakers.

[0006] A first aspect of this invention provides a smart speaker channel adaptive recognition method, applied to an audio system, comprising the following steps:

[0007] The smart speakers in the audio system are paired with each other, and the signal strength indication values ​​of each pair of smart speakers are collected in a number not less than a first threshold number to generate a signal strength indication cluster array.

[0008] A set of feature parameters is generated based on the signal strength indicator cluster array, and the feature parameters include the mean.

[0009] Based on the feature parameter set, the feature parameter comparison method is used to identify the first smart speaker channel;

[0010] Based on the feature parameter set and the channel recognition results of the first smart speaker, the group recognition method is used to identify the smart speakers of the remaining channels.

[0011] In some embodiments of the first aspect, the first smart speaker is identified using a feature parameter comparison method based on a feature parameter set, including the following steps:

[0012] Sort the feature parameter set according to the mean value;

[0013] The feature parameter set with the two highest mean values ​​is selected as the first criterion.

[0014] Based on the comparison results of the first criterion and the signal strength indicator cluster array, the first smart speaker channel is identified.

[0015] In some embodiments of the first aspect, based on the feature parameter set and the channel recognition result of the first smart speaker, a group recognition method is used to identify the smart speakers of the remaining channels, including the following steps:

[0016] Remove the signal strength indicator data of the first smart speaker from the signal strength indicator cluster array, and group the remaining smart speakers according to their dimensions;

[0017] Within each group of smart speakers, they are sorted according to the signal strength indicator value, with two speakers forming a unit in the sorting order;

[0018] Based on the grouping and unit results, the sum of differences and squares of each smart speaker cluster array is calculated to identify the second smart speaker channel;

[0019] Based on the results of the units within the second smart speaker cluster array, the remaining smart speaker channel units are identified.

[0020] In some embodiments of the first aspect, based on the feature parameter set and the channel recognition result of the first smart speaker, a group recognition method is used to identify the smart speakers of the remaining channels, including the following steps:

[0021] Remove the signal strength indicator data of the first smart speaker from the signal strength indicator cluster array, and select the two smart speakers with the highest average signal strength indicator values ​​from the first smart speaker to group them separately according to their signal strength indicator values.

[0022] Within each group of smart speakers, they are sorted according to the signal strength indicator value, with two speakers forming a unit in the sorting order;

[0023] Based on the grouping and unit results, the sum of squares of the differences between the two smart speaker cluster arrays is calculated to identify the second smart speaker channel;

[0024] Based on the results of the units within the second smart speaker cluster array, the remaining smart speaker channel units are identified.

[0025] In some embodiments of the first aspect, the method further includes comparing the average characteristic parameters of the smart speaker in each unit with those of the first smart speaker to obtain the left and right channel recognition results of the smart speaker in each unit.

[0026] In some embodiments of the first aspect, the method further includes, when the smart speaker device has a microphone array, identifying the left and right channel results of the smart speaker in each unit through the microphone array.

[0027] In some embodiments of the first aspect, the method further includes the following steps:

[0028] Obtain the first speaker standard base;

[0029] The feature data of each smart speaker in the audio system are normalized to obtain the normalized dataset of each smart speaker.

[0030] Calculate the sum of squared differences between each normalized dataset and the first speaker standard basis;

[0031] If the minimum value of the sum of squared differences of all smart speakers is the sum of squared differences of the first smart speaker, then the channel verification of the first smart speaker is passed.

[0032] In some embodiments of the first aspect, the method further includes the following steps:

[0033] Obtain the standard base for the second speaker;

[0034] The absolute values ​​of the feature data of each smart speaker in the audio system after removing the signal strength indication data of the first smart speaker are normalized to obtain the normalized dataset of each smart speaker.

[0035] Calculate the sum of squared differences between each normalized dataset and the second speaker standard basis;

[0036] If the minimum sum of differences among all smart speakers is equal to the sum of differences among the second smart speaker, then the channel verification of the second smart speaker is passed.

[0037] In some embodiments of the first aspect, the method further includes the following steps:

[0038] Update the normalized datasets of each smart speaker to the current standard base;

[0039] The audio system calculates the normalized dataset of each smart speaker according to a preset mode, compares it with the current standard base, and if the preset conditions are met, the method is re-executed to identify the channels of each smart speaker.

[0040] A second aspect of this invention provides a smart speaker channel adaptive recognition system for implementing the above-described smart speaker channel adaptive recognition method.

[0041] The intelligent speaker channel adaptive recognition method of the present invention has the following beneficial effects:

[0042] Simplified operation and improved efficiency: Intelligent adaptive channel recognition avoids tedious manual configuration for users and significantly improves the efficiency of speaker channel recognition.

[0043] Automation and accuracy: By comparing characteristic parameters and signal strength, the accuracy and consistency of the speaker channel identification process are ensured, reducing erroneous identification caused by human interference.

[0044] Enhance user experience: Achieve efficient recognition of multi-channel smart speaker systems, providing users with a smarter and more convenient user experience. Attached Figure Description

[0045] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings.

[0046] Figure 1 This is a schematic diagram of the channel layout of each smart speaker in a five-channel audio system;

[0047] Figure 2 This is a flowchart illustrating the implementation of an embodiment of the intelligent speaker channel adaptive recognition method of the present invention.

[0048] Figure 3 This is a schematic diagram of the implementation process of identifying a first smart speaker using a feature comparison method according to an embodiment of the present invention;

[0049] Figure 4 This is a schematic diagram of the first implementation process of identifying the remaining channels of a smart speaker using a group recognition method according to an embodiment of the present invention;

[0050] Figure 5 This is a schematic diagram of the second implementation process of the present invention, which uses a group recognition method to identify the remaining channels of the smart speaker.

[0051] Figure 6 This is a schematic diagram of the implementation process of the first intelligent speaker channel verification method according to an embodiment of the present invention;

[0052] Figure 7 This is a schematic diagram of the implementation process of the second intelligent speaker channel verification method according to an embodiment of the present invention;

[0053] Figure 8 This is a schematic diagram illustrating the implementation process of a speaker system self-testing method according to an embodiment of the present invention;

[0054] Figure 9 This is a schematic diagram of the structure of an intelligent speaker channel adaptive recognition system according to an embodiment of the present invention. Detailed Implementation

[0055] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0056] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0057] The flowchart shown in the attached diagram is merely an illustrative example and does not necessarily include all steps. For example, some steps may be broken down, while others may be combined or partially combined. Therefore, the actual execution order may change depending on the specific circumstances.

[0058] Figure 1The diagram illustrates the channel layout of each smart speaker in the audio system according to various exemplary embodiments of this application. Taking a typical 5.1 channel audio system as an example, the audio system includes a center channel speaker, two front channel speakers (left front channel speaker and right front channel speaker), two rear channel speakers (left rear left channel speaker and rear right channel speaker), and a subwoofer. The center channel speaker is typically positioned directly in front of the user and is used to primarily present dialogue or main sound. The front left and right channel speakers are positioned on the left and right sides in front of the user, respectively, to present sound effects in the left and right directions. The rear left and right channel speakers are positioned on the left and right sides behind the user to provide surround sound effects. The subwoofer is positioned between the center channel speaker and the front right channel speaker, responsible for playing low-frequency sounds to enhance overall immersion and sound quality. In this embodiment of the invention, a channel refers to an independent audio signal collected or played back from different spatial locations during recording or playback. Therefore, the number of channels is the number of audio sources during recording or the corresponding number of speakers during playback. Of course, besides a 5.1-channel audio system, there are other multi-channel audio systems, such as a 7.1-channel audio system, which includes two center channel speakers (left center channel speaker and right center channel speaker) between the two front channel speakers and two rear channel speakers of a 5.1-channel system. In specific implementation, the smart speakers of the audio system are paired with the audio source device via wireless connection. The channels of each smart speaker are identified by the adaptive recognition method of this invention, eliminating the need for manual settings or adjustments by the user, thus achieving fully automatic channel matching and layout optimization. This layout not only meets the needs of multi-channel sound effects but also simplifies the configuration process of the audio system and improves the user experience.

[0059] Based on the aforementioned application scenarios and audio system architecture, this application provides a smart speaker channel adaptive recognition method applied to an audio system. For multiple independent smart speakers without pre-configured smart speaker channels, this application allows these independent smart speakers to be freely arranged according to the conventional layout of a stereo system, without being limited by smart speaker channel information. By collecting signal strength indication values ​​between each pair of smart speakers to generate a signal strength indication cluster array, calculating its characteristic parameters and comparing them, the channel of the first smart speaker is identified. Then, based on the channel recognition result of the first smart speaker, a group recognition method is used to gradually identify the channel information of the remaining smart speakers. In this way, these multiple independent smart speakers can be combined into an audio system, producing a stereo effect when audio signals are played through these multiple smart speakers. This allows for flexible combination of these independent smart speakers without speaker channel information, configuring the channels of each smart speaker to form a stereo speaker system. Therefore, this application can automatically and efficiently complete the identification and matching of speaker channels, simplifying user operation and improving the intelligence level and user experience of the audio system.

[0060] It should be noted that the executing entity of the smart speaker channel adaptive recognition method provided in this application embodiment can be a smart speaker (e.g., a smart speaker with control functions or other smart speakers), a functional module and / or functional entity in the smart speaker that can implement the method, a terminal device connected to the smart speaker (e.g., a mobile phone, tablet computer, etc.), or a functional module and / or functional entity in the terminal device that can implement the method. Furthermore, the execution of this method can also be completed collaboratively by multiple smart speakers in a distributed manner, or through cooperation between the smart speaker and the terminal device. The specific implementation can be determined according to actual usage requirements, and this application embodiment does not limit this.

[0061] like Figure 2 As shown, this embodiment of the invention provides a smart speaker channel adaptive recognition method, applied to an audio system, comprising the following steps:

[0062] S100: Pair the smart speakers in the audio system with each other, and collect signal strength indication values ​​of no less than a first threshold number from each pair of smart speakers to generate a signal strength indication cluster array.

[0063] In this embodiment, smart speakers in an audio system are paired up in pairs using wireless communication technology, and a signal strength indicator cluster array is established based on the received signal strength indicator (RSSI) values ​​between the paired speakers. The smart speakers pair up in pairs via a wireless communication protocol, taking Wi-Fi as an example, operating at a frequency such as 5.6 GHz, to collect signal strength. Between each pair of paired smart speakers, at least a first threshold number of signal strength indicator values ​​are collected, and these signal strength data generate the signal strength indicator cluster array. The signal strength indicator cluster array is the result of analyzing the relative position and signal quality between the speakers based on the collected signal strength values. This data helps in subsequent adaptive channel identification of the smart speakers. For example, a 5.1 channel audio system involves six smart speakers, and pairing them in the system generates 30 cluster arrays. Each cluster array contains a dataset of collected signal strength values, representing the signal strength information between each pair of smart speakers. It should be noted that, in order to improve stability and accuracy, the number of signal acquisitions for each pair of speakers should typically reach a first threshold. In this invention, the first threshold is 40 to ensure that the acquired data has sufficient representativeness and statistical significance. This embodiment of the invention uses an initial threshold of 200 acquisitions as an example to acquire RSSI signal strength values.

[0064] It should be noted that the wireless protocol and sampling frequency (i.e., the number of first thresholds) during RSSI acquisition can be adjusted and optimized according to actual needs. In this scheme, Wi-Fi is used for speaker pairing and signal acquisition. Besides Wi-Fi, Bluetooth, ZigBee, or other wireless protocols can also be used. Signal strength values ​​are typically represented by RSSI (Received Signal Strength Indicator). A higher RSSI value indicates better signal quality between speakers and a closer distance between devices. In addition, other signal quality indicators (such as SNR, signal-to-noise ratio, and time difference of arrival) can also be used for acquisition.

[0065] In some embodiments, based on the channel reciprocity principle, the number of measurements for the signal strength indicator cluster arrays of each pair of smart speakers is halved. The channel reciprocity principle states that under ideal symmetrical channel conditions, the transmission quality of signals (such as signal strength, noise, etc.) is symmetrical between two communicating devices; that is, the signal transmission quality from device A to device B is the same as the signal transmission quality from device B to device A. In other words, the channel from A to B has the same performance as the channel from B to A under ideal conditions. In this embodiment of the invention, the application of the channel reciprocity principle means that the relative position between speakers can be deduced from the signal strength values, and the data is symmetrical between paired speakers. By collecting the signal strength values ​​between each pair of speakers and utilizing the channel reciprocity principle, redundant data can be reduced and computational efficiency improved. For example, in a 5.1 channel audio system, the monitoring of paired smart speakers yields 30 cluster arrays of data. With the support of the channel reciprocity principle, these cluster arrays can be halved to 15 cluster arrays, further reducing the system's computational burden and storage pressure, while also ensuring the validity and accuracy of the data. S200. Generate a set of feature parameters based on the signal strength indicator cluster array. The feature parameters include the mean.

[0066] In this embodiment, statistical analysis of the signal strength indicator cluster array extracts parameters describing the relative positions and signal strength characteristics of each smart speaker, thus aiding in the identification of smart speaker channels in an audio system. Specifically, this embodiment processes the signal strength indicator cluster array through data analysis, calculating the mean of each cluster as its characteristic parameter. The mean signal strength of each cluster represents the average signal quality level between the speakers. The calculation of the mean reflects the stability and reliability of the signal strength between the speakers. Therefore, even without pre-defined speaker channel information, the mean signal strength can be used as a feature to identify the channel configuration of smart speakers, improving the accuracy and efficiency of adaptive channel configuration.

[0067] It should be noted that although the mean is used as the feature parameter in this step of the invention, it can be extended to other statistical features as needed, such as variance, standard deviation, or, for example, median, skewness, and kurtosis. These features can provide richer information on the signal distribution between speakers, further improving the accuracy of identification and enhancing the robustness of data analysis. For example, variance can be used to measure the volatility of signal strength, while standard deviation can reflect the stability of signal strength data. Furthermore, in practical applications, signal strength acquisition is affected by environmental factors (such as obstacles like walls and furniture) and signal interference. More complex signal processing methods (such as noise filtering and signal smoothing) can be selected according to the actual situation to further improve signal quality and accuracy.

[0068] S300: Identify the first smart speaker channel using the feature parameter comparison method based on the feature parameter set;

[0069] In this embodiment of the application, the feature parameters of each smart speaker are compared according to the feature parameter set to classify and identify them. Since the feature parameters can represent information such as the physical location of the smart speaker and the signal characteristics of the sound source, the difference between the feature parameters can be effectively distinguished by comparing the magnitude of the feature parameters or comparing them with preset thresholds or reference values. This allows the smart speaker that best matches the standard of a specific channel (e.g., front, center, rear, etc.) to be identified.

[0070] In some optional embodiments, S300, based on the feature parameter set, a feature comparison method is used to identify the first smart speaker. The purpose is to sort the speakers according to specific statistical characteristics (such as the mean) based on the feature parameter set collected from the smart speakers, in order to further identify the channels of the smart speakers. Figure 3 As shown in the figure, this embodiment of the invention provides a schematic diagram of an implementation process for identifying a first smart speaker using a feature comparison method, including the following steps:

[0071] S310. Sort the feature parameter set according to the mean value;

[0072] In this embodiment of the invention, the mean represents the average signal strength indication value within a cluster array. Each pair of smart speakers in the audio system generates a cluster array, and the signal strength of each cluster array is statistically analyzed to obtain a mean. These means reflect the signal strength level, and thus reflect the distance between a smart speaker and other smart speakers.

[0073] Taking a typical 5.1 speaker system as an example, consisting of 6 smart speakers, the above steps can yield 30 clusters of smart speakers paired with each other. If the reciprocity principle is adopted, the clusters can be halved to 15 (e.g., μ1, μ2, ..., μ15). The feature parameters are judged by using the mean as an example. The mean values ​​of the feature parameter set are sorted, and these mean values ​​represent the concentrated values ​​of signal strength between the speakers.

[0074] The sorting operation in this embodiment can use common sorting algorithms (such as quicksort, bubble sort, etc.) to arrange these averages in order of size. The pair with the largest average in the sorted result represents the pair of smart speakers that receive the most power, which also means that the pair of smart speakers is closest to each other.

[0075] It's important to note that besides using the mean for sorting, other statistical features can also be used to sort cluster arrays. For example: median sorting: For each cluster array, the median of its signal strength can be calculated, and the clusters can be sorted by the median. The median is more robust than the mean when dealing with extreme values. Standard deviation sorting: If signal volatility is a concern, cluster arrays can be sorted based on their standard deviation. Clusters with smaller standard deviations represent more stable signals, while clusters with larger standard deviations represent more volatile signals. Signal-to-noise ratio (SNR) sorting: If there is significant interference or noise in the system, the SNR can also be considered as a sorting criterion. These alternatives can be selected based on specific application scenarios and requirements to further improve the system's recognition accuracy and flexibility.

[0076] S320. Select the feature parameter set with the highest and second-highest mean as the first criterion;

[0077] The aim is to identify and determine the channels of smart speakers by further filtering the signal characteristics of an audio system. In a multi-channel audio system, by sorting the average signal strength of each speaker and selecting the top two, the smart speaker most likely to meet the matching criteria can be identified based on the characteristic value of the signal strength and designated as the first smart speaker.

[0078] The top two values ​​in the sorted mean, namely mean15[0] and mean15[1], are selected as the criteria. The selection of the characteristic parameters of the first criterion represents the two sets of smart speakers with the strongest signal strength between them. Usually, the relative position of the smart speakers that meet the first criterion can determine the specific position conditions.

[0079] S330. Based on the comparison results of the first criterion and the signal strength indicator cluster array, the first smart speaker channel is identified.

[0080] Since the mean represents the main characteristic of the signal strength between speakers, and the signal strength indicator cluster array is generated by collecting signal strength values ​​between multiple pairs of smart speakers, each element in the cluster array represents the signal strength characteristics between a smart speaker and another smart speaker. By comparing the mean of these signals, it is possible to determine which smart speakers' positions match the first criterion signal strength distribution, thereby accurately identifying the first smart speaker.

[0081] by Figure 1 In the 5.1 audio system shown, the subwoofer channel usually has a unique signal characteristic, and its signal strength value satisfies the order of the first two average values. Therefore, the subwoofer channel is determined to be the first smart speaker channel, and the smart speaker located at the first smart speaker channel is the subwoofer in the 5.1 audio system.

[0082] It should be noted that the method of this embodiment is not only applicable to 5.1 audio systems, but can also be effectively applied to more complex audio systems, such as 7.1 audio systems and 9.1 audio systems. The subwoofer channels of these systems all conform to the signal characteristics of the feature parameter set with the mean value ranking first two as the first criterion. Thus, the first intelligent speaker channel, i.e. the subwoofer channel, can be determined by the method of this embodiment, and the subwoofer of the audio system can be uniquely identified.

[0083] S400: Based on the feature parameter set and the channel recognition results of the first smart speaker, the group recognition method is used to identify the remaining channel smart speakers.

[0084] In this embodiment of the invention, the group identification method is a technique for grouping the remaining smart speakers in an audio system according to their signal characteristics. Each group represents a class of smart speakers with similar characteristics. By grouping the clusters, the characteristics of each group of smart speakers are further analyzed. The grouping is based on the differences between smart speakers in signal strength, relative position, and other characteristic parameters. For example, in a 5.1 audio system, front speakers, surround speakers, subwoofers, and speakers located on the left or right side will exhibit different characteristic parameters, and grouping them can classify them into different categories. Based on the grouping, the channel role of each speaker is identified by referring to the channel identification result of the first smart speaker. Specifically, the group identification method utilizes the characteristics between feature parameter sets to divide smart speakers into different groups, and combines the characteristic parameter relationship between each group and the first smart speaker to identify the channels of other smart speakers. The channel identification result of the first smart speaker refers to the channel information of the first smart speaker, such as the subwoofer channel, which has been identified in the above steps by comparing and analyzing the channel information. The identification result of the first smart speaker provides a basis for identifying the channels of other smart speakers.

[0085] In some optional embodiments, S400, based on the feature parameter set and the first smart speaker channel recognition result, a group recognition method is used to identify the remaining channel smart speakers. For example... Figure 4 As shown in the diagram, this embodiment of the invention provides a first implementation process for identifying smart speakers with other channels using a group recognition method, including the following steps:

[0086] S411. Remove the signal strength indicator value data of the first smart speaker from the signal strength indicator cluster array, and group the remaining smart speakers according to their dimensions.

[0087] In this embodiment, the signal strength indication data corresponding to the first smart speaker is removed from all signal strength indication clusters in the audio system to avoid interference from the first smart speaker in the subsequent smart speaker grouping process. The remaining smart speakers will be classified and grouped based on their signal characteristics or after statistical analysis of the clusters.

[0088] Taking a 5.1 speaker system as an example, there are six smart speakers, including two front speakers, two surround speakers, one subwoofer, and one center speaker. After removing the data from the first smart speaker (such as the subwoofer identified by the method described above), the signal strength indicator clusters of the remaining five smart speakers are grouped into groups based on the mean of their characteristic parameters, with each smart speaker as a unit. This results in a grouping scheme based on five smart speaker dimensions. This grouping method can better determine the channel of each smart speaker by the relative differences in the smart speaker signals.

[0089] S412. Within each group of smart speakers, they are sorted according to the signal strength indication value, with each pair forming a unit in the sorting order;

[0090] In this embodiment of the invention, the signal strength indicator (RSSI) values ​​within the grouping scheme of each smart speaker are further divided into units. In actual implementation, each smart speaker can first be sorted according to a certain characteristic parameter, such as the RSSI value. The RSSI value (e.g., RSSI value) is typically used to represent the strength of a wireless signal; the stronger the signal, the larger the value. Therefore, the purpose of the sorting process is to separate the stronger and weaker signals in the coarse array of smart speakers currently grouped, providing a clear distinction for subsequent identification.

[0091] Taking a 5.1 speaker system as an example, the sorted signal strength values ​​are divided into two units for more detailed classification and recognition. First, the four cluster arrays (e.g., mean[0], mean[1], mean[2], mean[3]) in each smart speaker are sorted according to the sorting rule of mean size. After sorting, each pair of clusters is grouped into a unit. For example, the two clusters with larger sorted signal strength values ​​(e.g., mean[0], mean[1]) are grouped into one unit, and the two clusters with smaller signal strength values ​​(e.g., mean[2], mean[3]) are grouped into another unit. This helps to further refine the signal data of each smart speaker and provides more stable data support for subsequent grouping and matching recognition.

[0092] It should be noted that the grouping and sorting methods and criteria can be adjusted according to different application scenarios and audio system requirements. For example, the sorting rules can be adaptively adjusted based on the specifications of different audio systems or the actual signal strength in different environments. In other application scenarios, in addition to signal strength values, other characteristic parameters (such as signal stability, transmission delay, etc.) can also be used for sorting and grouping. Therefore, this invention does not impose specific restrictions on the sorting rules and grouping strategies, and they can be adjusted according to actual needs.

[0093] S413. Based on the grouping results and unit results, calculate the sum of differences and squares of each smart speaker cluster array, and identify the second smart speaker channel;

[0094] This invention employs a sum-of-squares (SFS) analysis method (i.e., calculating the sum of squares of the differences between two numbers) to quantify the differences between signal strengths or characteristic parameters, and to identify the channel roles of a smart speaker based on these differences. Using this method, the invention can accurately identify the speaker group with the smallest difference from other speakers within a given group, and can determine the channel information of a given group.

[0095] The sum of squares (J) is a method for calculating the differences within grouped units across different dimensions of smart speakers. In this step, the formula for calculating the sum of squares (J) is:

[0096] J = (mean[0] - mean[1]) 2 +(mean[2]-mean[3]) 2 ])

[0097] Where mean[0], mean[1], mean[2], and mean[3] are the signal mean values ​​in the cluster array of each smart speaker. For example, mean[0] and mean[1] represent a smart speaker unit with a larger mean, and mean[2] and mean[3] represent a smart speaker unit with a smaller mean. This formula quantifies the differences between different smart speaker cluster arrays by calculating the sum of squares of the differences between each pair of clusters within each smart speaker group. In this way, a sum of squares (J value) can be calculated for each smart speaker, and these J values ​​represent the relative degree of difference of the smart speaker cluster array. The J values ​​of each smart speaker (such as J1, J2, J3, J4, J5) are obtained. Finally, the smart speaker cluster array with the smallest J value is selected as the second smart speaker channel. In this embodiment of the invention, the second smart speaker channel is the center speaker channel.

[0098] In audio systems, such as Figure 1 As shown, the center speaker channel is typically positioned in the center of the front speakers in an audio system, and in a 5.1 or 7.1 audio system, it usually exhibits a significant difference in signal position compared to other smart speakers (such as front and rear speakers). Due to its unique position, the center speaker channel experiences less signal variation in its speaker unit compared to other speakers (such as front or surround speakers), resulting in the smallest J-value, which aligns with the characteristics of a smart speaker serving as a center speaker channel.

[0099] It should be noted that the method for calculating the J value in this embodiment of the invention is only one implementation scheme. In different application scenarios, the calculation method of the sum of differences and squares can be adjusted according to actual needs. For example, other statistics (such as variance, standard deviation, etc.) can be chosen to replace the sum of differences and squares, or weighting factors can be introduced on the basis of the sum of differences and squares to assign different weights to the signal characteristics of different speakers.

[0100] S414. Based on the results of the units within the second smart speaker cluster array, identify the remaining smart speaker channel units.

[0101] In this embodiment of the invention, by further analyzing the signal characteristics of the second smart speaker channel (such as the center speaker channel), the channel unit information of other speakers can be determined based on the unit division in the cluster array. This step combines the sorting and differences of the smart speaker units in the cluster array to further divide the position and channel unit information of other smart speakers, thereby accurately completing the channel identification.

[0102] After identifying the second smart speaker channel, i.e., the center speaker channel, the units within the cluster corresponding to the center speaker are determined based on their characteristics. The units containing the two smart speakers with higher average values ​​(e.g., characteristic parameters) represent its front-speaker characteristics, while the units containing the two smart speakers with lower average values ​​represent its rear-speaker characteristics. In a 7.1 speaker system, the units containing the two smart speakers with the middle average values ​​(e.g., characteristic parameters) represent its center-speaker characteristics.

[0103] In some optional embodiments, S400, based on the feature parameter set and the first smart speaker channel recognition result, a group recognition method is used to identify the remaining channel smart speakers. For example... Figure 5 As shown in the diagram, this embodiment of the invention provides a second implementation process for identifying smart speakers with other channels using a group recognition method, including the following steps:

[0104] S421. Remove the signal strength indication value data of the first smart speaker from the signal strength indication cluster array, and select the two smart speakers with the highest average value of the first smart speaker to be grouped according to their signal strength indication values.

[0105] In this embodiment of the invention, similar to step S411, the data of the first smart speaker channel cluster array is removed, and the smart speakers with the highest and second-highest channel average of the first smart speaker are selected, such as... Figure 1As shown, in an audio system, the first two speakers closest to the subwoofer channel must include the center speaker channel. Therefore, only the two smart speaker clusters closest to the subwoofer channel need to be selected for subsequent identification. This method significantly reduces the number of clusters that need to be processed, thereby improving the efficiency of the identification process. This method is not only applicable to 5.1 or 7.1 systems, but can also be extended to audio systems with more channels, such as 9.1 or 11.1 systems. In different audio systems, selecting the first two clusters closest to the first smart speaker for identification can effectively reduce computational complexity.

[0106] S422. Within each group of smart speakers, they are sorted according to the signal strength indication value, with each pair forming a unit in the sorting order;

[0107] In this embodiment of the invention, similar to S421, it is only necessary to sort the two smart speaker cluster arrays that have already been grouped, and further divide the sorted results into speaker units.

[0108] S423. Based on the grouping results and unit results, calculate the sum of squares of the differences between the two smart speaker cluster arrays respectively, and identify the second smart speaker channel;

[0109] In this embodiment of the invention, similar to S421, the second smart speaker channel, i.e. the center speaker channel, can be identified simply by calculating the sum of squares of the differences between the two smart speaker cluster arrays.

[0110] S424. Based on the results of the units within the second smart speaker cluster array, identify the remaining smart speaker channel units.

[0111] In this embodiment of the invention, similar to S424, by analyzing the units within the second smart speaker cluster array, the front speaker unit, the rear speaker unit, and other channels (such as the center speaker unit) can be accurately identified.

[0112] In some embodiments, the smart speaker channel adaptive recognition method further includes comparing the average feature parameters of the smart speaker in each unit with those of the first smart speaker to obtain the left and right channel recognition results of the smart speaker in each unit.

[0113] In this embodiment of the invention, the channel type (i.e., left channel or right channel) of the smart speaker is determined by comparing the characteristic parameters of the smart speaker in each smart speaker channel unit with those of the first smart speaker channel. Specifically, in each smart speaker channel unit, the characteristic parameters of the two smart speakers are compared with those of the first smart speaker channel (e.g., the subwoofer channel). The one with the smaller mean value is the left speaker channel, and the one with the larger mean value is the right speaker channel. By comparing the mean values ​​of the smart speakers in each smart speaker unit with those of the first smart speaker channel, two front channel speakers (left front channel speaker and right front channel speaker) and two rear channel speakers (left rear left channel speaker and rear right channel speaker) can be identified. For a 7.1 audio system, two center channel speakers (left center channel speaker and right center channel speaker) can also be identified.

[0114] In some embodiments, when the smart speaker device has a microphone array, the left and right channels of the smart speaker in each unit are identified through the microphone array. In this embodiment of the invention, a microphone array refers to an array composed of multiple microphone elements, which receive sound source signals at different positions and angles. Each microphone independently collects sound data, and the system can determine the direction, distance, and other spatial information of the sound source based on the time difference of arrival (TDOA) between the multiple microphones. Using an array of multiple microphones can more accurately locate the direction of the sound source than a single microphone and can identify and process complex sound field information. For example, when sound comes from the left, the microphone closer to the left in the microphone array will receive the sound first, while the microphone closer to the right will receive the sound later. By calculating the time difference, the angle of the sound source can be determined, thereby identifying the left and right channels of the smart speaker.

[0115] It should be noted that the microphone array in the embodiments of the present invention is not limited to a specific number or type of microphones; any microphone arrangement that can provide time or spatial differences can be applied to the present invention. For example, the number of microphones, their arrangement, and the signal processing method can be flexibly adjusted according to specific applications. The method for identifying the left and right channels is not limited to the time difference of the microphone array; it can also be combined with other acoustic characteristics, such as signal amplitude and frequency response, for comprehensive analysis. Furthermore, for some devices that do not support complex microphone arrays, directional microphones can be used. These microphones can receive sound signals from a specific direction, and by analyzing the direction of the received audio signals, the speaker channels can be further determined.

[0116] In some embodiments, the smart speaker channel adaptive recognition method further includes a smart speaker channel verification method. This method verifies the channel type by calculating the sum of squared differences between the feature data of each smart speaker channel and a standard base, ensuring accurate channel recognition for each smart speaker in the audio system. In this invention, the standard base is a preset set of reference data representing the characteristic parameters of smart speaker channels (e.g., subwoofer channels, center speaker channels, etc.). The standard base is typically obtained by collecting and analyzing various performance parameters (such as signal strength, frequency response, and latency) of the smart speaker under normal operating conditions. The verification method uses normalization to eliminate physical differences between different speakers, making the comparison of feature data more accurate and consistent. By determining the minimum value of the sum of squared differences, the speaker closest to the standard base can be effectively identified, and channel verification can be performed using that speaker.

[0117] In some embodiments, such as Figure 6 As shown in the diagram, this embodiment of the invention provides a schematic flowchart of a first smart speaker channel verification method. The smart speaker channel adaptive recognition method further includes the first smart speaker channel verification method, comprising the following steps:

[0118] S510, Obtain the first speaker standard base;

[0119] In this embodiment of the invention, the standard basis of the first speaker is a preset set of characteristic parameters of a subwoofer and other speakers. If the characteristic parameters use the mean, then the standard set of the first speaker is:

[0120] mean 低音标准基 ={μ1, μ2, ..., μ5}

[0121] Here, μ represents the average signal strength indication value measured when the first speaker is paired with other speakers.

[0122] S520. Normalize the feature data of each smart speaker in the audio system to obtain the normalized dataset of each smart speaker.

[0123] In this embodiment of the invention, normalization processing is a preprocessing step on the feature data of each smart speaker, used to convert the signal characteristics of different smart speakers in the audio system into the same scale or range. Since the signal characteristics of each smart speaker may differ, normalization processing can unify these signal characteristics to the same standard scale, thereby eliminating differences between devices and facilitating comparison and analysis.

[0124] The specific method of normalization can be to subtract the mean of the feature data of each smart speaker and divide it by its standard deviation, or to directly normalize by the maximum and minimum values, etc. The embodiments of the present invention do not make specific limitations.

[0125] The result of normalizing the feature data of a certain smart speaker is

[0126] mean1 归一 ={μ1, μ2, ..., μ5}

[0127] Similarly, the mean2 result obtained by normalizing the feature data of other smart speakers can be obtained. 归一 mean3 归一 mean4 归一 mean5 归一 and mean6 归一 .

[0128] S530. Calculate the sum of squared differences between each normalized dataset and the first speaker standard basis;

[0129] In this embodiment of the invention, the sum of squared differences (SSD) is used to measure the difference between two datasets (or features). Specifically, the SSD between the normalized dataset of each smart speaker and the first speaker standard basis is calculated using the following formula:

[0130]

[0131] Where, mean 1归一 [i] represents each item in a normalized dataset of a smart speaker, with mean... 第一音箱标准基 [i] represents each item in the first speaker standard base. In this embodiment, the first speaker standard set is the subwoofer standard base, and 4 represents the number of data items.

[0132] Similarly, the sum of differences J2, J3, J4, J5, and J6 of other smart speakers can be calculated.

[0133] S540. If the minimum value of the sum of squares of differences among all smart speakers is the sum of squares of differences among the first smart speaker, then the channel verification of the first smart speaker is passed.

[0134] In this embodiment of the invention, by comparing the sum of squared differences between all smart speakers and the standard basis of the first speaker, the speaker with the smallest difference is selected, and its feature data is considered to be closest to that of the first smart speaker, thus determining that the speaker is the first smart speaker. Specifically, if the J value of the subwoofer obtained by the above method in the audio system is the smallest, then the first smart speaker determined in the smart speaker channel adaptive recognition method is confirmed, and the channel verification of the first smart speaker is passed.

[0135] In some embodiments, such as Figure 7 As shown in the diagram, this embodiment of the invention provides a schematic flowchart of a second smart speaker channel verification method. The smart speaker channel adaptive recognition method further includes the second smart speaker channel verification method, comprising the following steps:

[0136] S610, Obtain the second speaker standard base;

[0137] In this embodiment of the invention, the standard base of the second speaker is a preset set of characteristic parameters of the center speaker and other speakers. If the characteristic parameters use the mean, then the standard set of the second speaker is:

[0138] mean 中置标准基 ={μ1, μ2, ..., μ5}

[0139] Here, μ represents the average signal strength indication value measured when the second speaker is paired with other speakers.

[0140] S620. Normalize the absolute value of the feature data of each smart speaker in the audio system after removing the signal strength indication value data of the first smart speaker to obtain the normalized dataset of each smart speaker.

[0141] In this embodiment of the invention, similar to S520, the feature data of each smart speaker in the audio system after removing the signal strength indication value data of the first smart speaker is preprocessed by normalization.

[0142] S630. Calculate the sum of squared differences between each normalized dataset and the second speaker standard basis;

[0143] In this embodiment of the invention, the sum of squared differences (SSD) is used to measure the difference between two datasets (or features). Specifically, the SSD between the normalized dataset of each smart speaker and the second speaker standard basis is calculated using the following formula:

[0144]

[0145] Where, mean 1归一 [i] represents each item in a normalized dataset of a smart speaker, with mean... 第二音箱标准基 [i] represents each item in the second speaker standard base. In this embodiment, the first speaker standard set is the subwoofer standard base, and 3 represents the number of data items after removing the first smart speaker.

[0146] Similarly, the sum of differences J2, J3, J4, and J5 of other smart speakers can be calculated.

[0147] S640. If the minimum value of the sum of squares of differences among all smart speakers is the sum of squares of differences among the second smart speaker, then the channel verification of the second smart speaker is passed.

[0148] In this embodiment of the invention, by comparing the sum of squared differences between all smart speakers and the standard basis of the second speaker, the speaker with the smallest difference is selected, and its feature data is considered to be closest to that of the second smart speaker, thus determining that the speaker is the second smart speaker. Specifically, if the J value of the center speaker obtained by the above method in the audio system is the smallest, then the second smart speaker identified in the smart speaker channel adaptive recognition method is confirmed, and the second smart speaker channel verification is passed.

[0149] In some embodiments, the smart speaker channel adaptive recognition method further includes a speaker system self-testing method. The self-testing function of the speaker system is primarily used to automatically adapt and update the system configuration when the smart speaker's location is changed. By updating the normalized dataset to the current standard base, the system can determine the current standard base and thus detect changes in device status based on it, responding promptly and re-identifying the smart speaker's channels. The self-testing method ensures that the system can still efficiently and accurately identify channel information during changes.

[0150] like Figure 8 As shown in the diagram, this embodiment of the invention provides a schematic flowchart of a speaker system self-test method. The speaker system self-test method includes the following steps:

[0151] S710, Update the normalized dataset of each smart speaker to the current standard base;

[0152] In this embodiment of the invention, the normalized dataset of each smart speaker is compared and updated with the standard base of the current audio system. The standard base typically represents the normal operating state and characteristic data of the audio system, including the aforementioned first speaker standard base and second speaker standard base, or the standard base of all smart speaker channels. The standard base data may include features such as the speaker's channel layout, signal strength indication, and frequency response.

[0153] S720: The audio system calculates the normalized dataset of each smart speaker according to a preset mode, compares it with the current standard base, and if the preset conditions are met, the method is re-executed to identify the channels of each smart speaker.

[0154] In this embodiment of the invention, the preset mode refers to the system updating the speaker feature data according to certain rules or cycles during adaptive recognition. The preset mode can include the following methods: calculating the normalized dataset of each smart speaker upon startup: recalculating speaker data and performing recognition each time the system is powered on; periodically or regularly checking the normalized dataset of each smart speaker: the system performs recognition and update operations according to a set time interval (e.g., daily, weekly); frequency mode for the normalized dataset of each smart speaker: periodically updating data and performing channel recognition based on the usage frequency of the system. The preset mode ensures that the system always maintains the latest smart speaker channel configuration. The preset condition refers to a difference threshold set when comparing the normalized dataset of each smart speaker channel in the system with the current standard base. If the difference between the normalized dataset and the current standard base exceeds a certain preset threshold, the channel recognition method needs to be re-executed. Specifically, if the difference exceeds a second threshold (e.g., 0.1), it indicates a significant change in the system's configuration or characteristics, requiring re-execution of the smart speaker channel adaptive recognition method for re-recognition.

[0155] like Figure 9 As shown, this embodiment of the invention provides a smart speaker channel adaptive recognition system for implementing the above-mentioned smart speaker channel adaptive recognition method. The system includes a signal acquisition module M100 and a control module M200.

[0156] The acquisition module M100 is configured to acquire the signal strength indication values ​​of each smart speaker in the audio system and transmit the data to the control module.

[0157] The control module M200 is configured to schedule the operation of various functional modules and units, and execute processing tasks based on the acquired signal strength indication data. The control module can contain multiple functional units to control the execution of various steps in the speaker channel recognition method.

[0158] Optionally, the control module M200 also includes different types of databases, such as relational databases and NoSQL databases, to expand its applicability and functionality. Relational databases can be used to store structured data, while NoSQL databases can be used to store unstructured data, supporting more diverse data storage needs. This design provides the smart speaker channel adaptive recognition system with more flexible and comprehensive data management and query functions, thereby improving system performance and efficiency.

[0159] In the intelligent speaker channel adaptive recognition system of the present invention, the functions of each module can be implemented using the specific implementation methods of the intelligent customer service method described above, which will not be elaborated here.

[0160] This invention also provides a smart speaker channel adaptive recognition system product, which includes computer-executable instructions. When the computer program is executed by a processor, it implements the above-described smart speaker channel adaptive recognition method.

[0161] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of this application and should not be construed as limiting the specific implementation of this application to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of this application, and all such modifications or substitutions should be considered within the scope of protection of this application.

Claims

1. A smart speaker channel adaptive recognition method, applied to an audio system, characterized in that, Includes the following steps: The smart speakers in the audio system are paired with each other, and the signal strength indication values ​​of each pair of smart speakers are collected in a number not less than a first threshold number to generate a signal strength indication cluster array. A set of feature parameters is generated based on the signal strength indicator cluster array, and the feature parameters include the mean. Based on the feature parameter set, the feature parameter comparison method is used to identify the first smart speaker channel; Based on the feature parameter set and the channel recognition results of the first smart speaker, the group recognition method is used to identify the smart speakers of the remaining channels.

2. The intelligent speaker channel adaptive recognition method according to claim 1, characterized in that, The step of identifying the first smart speaker using a feature parameter comparison method based on a feature parameter set includes the following steps: Sort the feature parameter set according to the mean value; The feature parameter set with the two highest mean values ​​is selected as the first criterion. Based on the comparison results of the first criterion and the signal strength indicator cluster array, the first smart speaker channel is identified.

3. The intelligent speaker channel adaptive recognition method according to claim 2, characterized in that, The step of identifying the remaining smart speakers using a grouping recognition method based on the feature parameter set and the first smart speaker channel recognition result includes the following steps: Remove the signal strength indicator data of the first smart speaker from the signal strength indicator cluster array, and group the remaining smart speakers according to their dimensions; Within each group of smart speakers, they are sorted according to the signal strength indicator value, with two speakers forming a unit in the sorting order; Based on the grouping and unit results, the sum of differences and squares of each smart speaker cluster array is calculated to identify the second smart speaker channel; Based on the results of the units within the second smart speaker cluster array, the remaining smart speaker channel units are identified.

4. The intelligent speaker channel adaptive recognition method according to claim 2, characterized in that, The step of identifying the remaining smart speakers using a grouping recognition method based on the feature parameter set and the first smart speaker channel recognition result includes the following steps: Remove the signal strength indicator data of the first smart speaker from the signal strength indicator cluster array, and select the two smart speakers with the highest average signal strength indicator values ​​from the first smart speaker to group them separately according to their signal strength indicator values. Within each group of smart speakers, they are sorted according to the signal strength indicator value, with two speakers forming a unit in the sorting order; Based on the grouping and unit results, the sum of squares of the differences between the two smart speaker cluster arrays is calculated to identify the second smart speaker channel; Based on the results of the units within the second smart speaker cluster array, the remaining smart speaker channel units are identified.

5. The intelligent speaker channel adaptive recognition method according to any one of claims 3 to 4, characterized in that, The method also includes, By comparing the average feature parameters of the smart speakers in each unit with those of the first smart speaker, the left and right channel recognition results of the smart speakers in each unit are obtained.

6. The intelligent speaker channel adaptive recognition method according to any one of claims 3 to 4, characterized in that, The method further includes: When a smart speaker device has a microphone array, the left and right channels of the smart speaker in each unit are identified through the microphone array.

7. The intelligent speaker channel adaptive recognition method according to claim 1, characterized in that, The method further includes the following steps: Obtain the first speaker standard base; The feature data of each smart speaker in the audio system are normalized to obtain the normalized dataset of each smart speaker. Calculate the sum of squared differences between each normalized dataset and the first speaker standard basis; If the minimum value of the sum of squared differences of all smart speakers is the sum of squared differences of the first smart speaker, then the channel verification of the first smart speaker is passed.

8. The intelligent speaker channel adaptive recognition method according to claim 1, characterized in that, The method further includes the following steps: Obtain the standard base for the second speaker; The absolute values ​​of the feature data of each smart speaker in the audio system after removing the signal strength indication data of the first smart speaker are normalized to obtain the normalized dataset of each smart speaker. Calculate the sum of squared differences between each normalized dataset and the second speaker standard basis; If the minimum sum of differences among all smart speakers is equal to the sum of differences among the second smart speaker, then the channel verification of the second smart speaker is passed.

9. The intelligent speaker channel adaptive recognition method according to any one of claims 7 to 8, characterized in that, The method further includes the following steps: Update the normalized datasets of each smart speaker to the current standard base; The audio system calculates the normalized dataset of each smart speaker according to a preset mode, compares it with the current standard base, and if the preset conditions are met, the method is re-executed to identify the channels of each smart speaker.

10. A smart speaker channel adaptive recognition system, characterized in that, This method is used to implement the smart speaker channel adaptive recognition method according to any one of claims 1 to 9.

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