A Bluetooth headset pairing system with automatic noise reduction recognition and pairing

Abstract

This invention relates to the field of Bluetooth headset pairing technology, specifically to an automatic noise reduction and identification pairing system for Bluetooth headsets. The system includes a signal strength evaluation module, a noise level evaluation module, and a comprehensive analysis and optimization module. Specifically, the signal strength evaluation module uses a Bluetooth transceiver to periodically collect Bluetooth signals from different Bluetooth devices. The Bluetooth signals include the device's unique identifier and signal strength. The noise level evaluation module establishes a bidirectional communication link with each Bluetooth device based on its unique identifier to receive audio signals. It then converts the audio signal data into frequency domain data using a Fast Fourier Transform to determine the noise level. The comprehensive analysis and optimization module uses the KNN algorithm to prioritize the signal strength and noise levels of different devices to determine the target pairing device. Finally, it determines the final pairing device and performs optimization processing based on the signal strength and noise level of the target pairing device.

Description

Technical Field

[0001] The present invention relates to the technical field of Bluetooth headset pairing, and in particular to a Bluetooth headset pairing system for automatic noise reduction recognition and pairing. Background Art

[0002] Traditional Bluetooth headset pairing systems rely primarily on simple broadcast and connect mechanisms to identify and connect to nearby devices. In this case, the headset searches for available Bluetooth signals within a certain range and then pairs with the device with the highest signal strength. This approach typically does not take into account other factors that may affect communication quality, such as ambient noise levels, relative motion between devices, and interfering signals.

[0003] In this simplified pairing process, once the headset detects multiple devices, it will choose the one with the strongest signal to connect. Although simple to operate, this method usually does not take into account that the signal strength may change due to obstacles or device movement, resulting in unstable connection. Similarly, this method cannot distinguish the quality of the signal - although the signal strength is high, the actual communication quality may be poor due to surrounding noise or interference.

[0004] In addition, the traditional pairing process does not have a dynamic adaptation mechanism. This means that once the pairing is completed, if the user encounters connection problems, such as noise or disconnection during the call, manual intervention is often required to disconnect the current connection and re-search for a better paired device. This not only consumes the user's time, but also affects the experience. Moreover, in a multi-device environment, this method does not take into account the communication priority between devices, or adjust the connection strategy according to different scenarios.

[0005] In addition, traditional methods do not use advanced algorithms to further analyze signal quality and environmental noise, and they may lack the ability to adapt to more complex or changing environments, thus performing poorly in noisy environments or when devices are dynamically changing. Therefore, compared with the newly proposed automatic noise reduction recognition pairing Bluetooth headset pairing system, traditional methods have obvious disadvantages in terms of pairing intelligence, communication quality assurance, connection stability, and user convenience. Summary of the invention

[0006] The object of the present invention is to provide a Bluetooth headset pairing system with automatic noise reduction recognition and pairing to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: a Bluetooth headset pairing system with automatic noise reduction recognition and pairing, which includes a signal strength evaluation module, a noise level evaluation module, and a comprehensive analysis and optimization module, wherein:

[0008] The signal strength evaluation module uses a Bluetooth wireless transceiver to periodically collect Bluetooth signals of different Bluetooth devices, where the Bluetooth signals include a unique identifier of the device and signal strength;

[0009] The noise level assessment module establishes a two-way communication link with different Bluetooth devices according to the unique identifier of the device, for receiving audio signals, and converts the audio signal data into frequency domain data form through fast Fourier transform for determining the noise level;

[0010] The comprehensive analysis and optimization module uses the KNN algorithm to prioritize the signal strength and noise level of different devices to determine the target paired device, and determines the final paired device based on the signal strength and noise level of the target paired device, including:

[0011] Calculate the average signal strength of the remaining devices, where the remaining devices are all devices except the target device, and the average signal strength is the sum of the signal strengths of the remaining devices divided by the number of the remaining devices;

[0012] When the signal strength of the target paired device is greater than or equal to the average signal strength of the remaining devices, and the noise level of the target paired device is less than or equal to the noise level of the remaining devices, the target paired device is determined to be the final paired device;

[0013] When the signal strength of the target paired device is less than the average signal strength of the remaining devices, a gain coefficient is added to the signal strength of the target paired device in the signal strength evaluation module for amplification;

[0014] When the noise level of the target paired device is greater than the noise levels of the remaining devices, the noise level of the target paired device in the noise level evaluation module is optimized by removing the DC offset and performing denoising using a low-pass filter;

[0015] After the optimization process, the previous comparison and optimization logic is used to make judgments again, and multiple iterations are performed until the final result is determined.

[0016] As a further improvement of the technical solution, the noise level assessment module converts the audio signal data into frequency domain data form by fast Fourier transform, and the conversion process specifically includes:

[0017] The continuous audio signal data is sampled into a set of discrete audio data by using an analog-to-digital converter;

[0018] Use the discrete Fourier transform formula to convert a set of discrete audio data into another set of discrete audio data. The other set of discrete audio data represents complex values ​​in the frequency domain. For a set of sample sequences of length N, the discrete Fourier transform formula of each discrete frequency component X[k] is as follows:

[0019]

[0020] Where x[n] is the nth sample in the time domain, X[k] is the kth component in the frequency domain, i is the imaginary unit, e is the base of the natural logarithm, and N is the total number of samples;

[0021] Peak location is performed on the frequency domain data. The left and right neighbors are found for each data point and compared. When a data point is larger than its neighbors on both sides, it is determined to be a peak and used as the noise level.

[0022] As a further improvement of the present technical solution, the comprehensive analysis and optimization module includes an analysis unit and an optimization unit. The analysis unit uses a KNN algorithm to prioritize the signal strengths and noise levels of different devices to determine the target pairing device, and determines the final pairing device based on the signal strength and noise level in the target pairing device. When the signal strength of the target pairing device is less than the average signal strength of the remaining devices, the optimization unit adds a gain coefficient to the signal strength of the target pairing device in the signal strength evaluation module for amplification. When the noise level of the target pairing device is greater than the noise level of the remaining devices, the noise level of the target pairing device in the noise level evaluation module is optimized, and denoising is performed by removing the DC bias and using a low-pass filter.

[0023] As a further improvement of the technical solution, the analysis unit uses the KNN algorithm to prioritize the signal strength and noise level of different devices, specifically including:

[0024] Using a public database to collect training data, the training data includes the same data features as the signal strength and noise level of different devices sent by the signal strength assessment module and the noise level assessment module, as well as the priority score, and integrating the data features in the training data into a feature vector, wherein the priority score is used as a target variable;

[0025] For the device to be scored, it is compared with all the feature vectors in the training data. The comparison method is Chebyshev distance. For each dimension of the feature vectors of two points, the absolute value of the difference between the coordinate values ​​is calculated, and the maximum value of the coordinate difference is found as the Chebyshev distance of the two points. For each device to be scored, the K nearest neighbors are found. These neighbors are the K data points in the training data that are closest to the device to be scored in the feature space. The k value determines the number of neighboring samples considered and has an impact on the classification result. The k value is set to 3. The nearest k neighbors are found, and the classification decision will be made through "majority voting". Each neighbor casts a vote for its category, and the query point will be classified into the category with the most votes. The predicted classification is output as the corresponding priority score.

[0026] As a further improvement of the present technical solution, the optimization unit adds a gain coefficient to the signal strength of the target paired device in the signal strength evaluation module for amplification, and adds the gain coefficient through an internal signal amplifier to amplify the signal strength of the target device, and the gain coefficient = the average signal strength of the remaining devices - the signal strength of the target paired device.

[0027] As a further improvement of the present technical solution, the optimization unit optimizes the noise level of the target paired device in the noise level assessment module by removing the DC bias, eliminating the average voltage level in the signal, and subtracting the average value of the signal from each sample point in the signal so that the signal is centered on a zero value in time, and uses a low-pass filter to eliminate the frequency range that limits the analysis, retaining useful frequency components while removing non-target frequencies that are considered to be noise.

[0028] As a further improvement of the present technical solution, the noise level assessment module establishes a two-way communication link with different Bluetooth devices according to wireless scanning, identifies the wireless signals sent by different Bluetooth devices by scanning the wireless frequency band and discovers that the broadcasting device sends a connection request, and the connection request includes device information and handshake protocol data; after the Bluetooth device accepts the connection request, a two-way communication link between the noise level assessment module and the Bluetooth device is formed through digital certificate verification. After the connection is established, the Bluetooth device will transmit audio signal data, and the audio signal data includes prepared sound samples for testing and analysis.

[0029] As a further improvement of the present technical solution, the optimization unit adds a gain coefficient to the signal strength of the target pairing device in the signal strength evaluation module to perform amplification processing, and after optimizing the noise level of the target pairing device in the noise level evaluation module, the signal strength evaluation module sends the signal strength of the target pairing device after optimization processing to the analysis unit, and the noise level evaluation module sends the noise level of the target pairing device after optimization processing to the analysis unit, and the analysis unit makes a judgment according to the previous comparison and optimization logic, and iterates multiple times until the final result is determined.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] The Bluetooth headset pairing system with automatic noise reduction and identification pairing integrates two core modules, signal strength and noise level, and establishes a scientific and efficient selection mechanism to determine the best pairing device. The signal strength evaluation module ensures that the collected Bluetooth signal is accurate and provides reliable basic data for the pairing process; the noise level evaluation module accurately analyzes the audio signal and intelligently renders the environmental noise conditions; this method effectively improves the communication quality, especially in a noisy environment, so that users can get a clearer and more stable listening experience; the application of the comprehensive analysis and optimization module further scores and sorts the signals and noises between devices through the KNN algorithm, and can automatically identify and select the best pairing device, reduce the user's operating burden, and distinguish the connection object with the least interference in the environment;

[0032] In the system's working mechanism, the continuously iteratively optimized program design ensures the high stability and long-term reliability of the connection. For those devices with weak signals or high noise levels, the system will automatically adjust the gain factor or perform noise reduction processing, effectively improving the effectiveness of signal transmission and the clarity of calls. This series of optimization measures not only enhances the performance of paired devices, but also greatly optimizes power consumption, helps extend the life of the headset, and reduces energy waste. As the environment changes and the signal strength between devices fluctuates, the system can dynamically adapt and adjust, ensuring the quality of the Bluetooth connection in real time, providing users with a convenient and intelligent daily use experience, and significantly improving user satisfaction with Bluetooth headsets. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the overall module of the present invention;

[0034] Figure 2 It is a schematic diagram of each module unit of the present invention;

[0035] Figure 3 It is a process flow chart of the KNN algorithm in the comprehensive analysis and optimization module of the present invention.

[0036] In the figure: 100, signal strength evaluation module; 200, noise level evaluation module; 300, comprehensive analysis and optimization module; 301, analysis unit; 302, optimization unit. DETAILED DESCRIPTION

[0037] The following will be combined with the accompanying drawings in the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] See also Figure 1-3 The present invention provides a technical solution: a Bluetooth headset pairing system with automatic noise reduction recognition and pairing, including a signal strength evaluation module 100, a noise level evaluation module 200 and a comprehensive analysis and optimization module 300.

[0039] The signal strength evaluation module 100 collects Bluetooth signals from different Bluetooth devices. The Bluetooth signals include the unique identifier and signal strength of the device. During the collection process, the signal strength evaluation module 100 uses the Bluetooth wireless transceiver to monitor the Bluetooth signals sent by different Bluetooth devices at a certain time interval. The value of the time interval can be set according to user needs. The common broadcast interval is set to 20 milliseconds to 10.24 seconds. According to the Bluetooth core specification, the shorter the interval time is recommended to be set, the greater the probability of being discovered, but it will also increase power consumption.

[0040] The noise level assessment module 200 establishes a two-way communication link with different Bluetooth devices based on wireless scanning, identifies the wireless signals sent by different Bluetooth devices by scanning the wireless frequency band and finds that the broadcasting device sends a connection request, which includes device information and handshake protocol data; after the Bluetooth device accepts the connection request, a two-way communication link between the noise level assessment module 200 and the Bluetooth device is formed through digital certificate verification. After the connection is established, the Bluetooth device will transmit audio signal data, and the audio signal data includes prepared sound samples for testing and analysis.

[0041] The noise level assessment module 200 performs spectrum analysis on the audio signal data and converts the audio signal data into frequency domain data form by fast Fourier transform. The conversion process is as follows:

[0042] The continuous audio signal data is sampled into a set of discrete audio data by using an analog-to-digital converter;

[0043] The discrete Fourier transform formula is used to convert a set of discrete audio data (in time domain representation) into another set of discrete audio data (in frequency domain representation). The other set of discrete audio data represents complex values ​​in the frequency domain. For a set of sample sequences of length N, the discrete Fourier transform formula of each discrete frequency component X[k] is as follows:

[0044]

[0045] Where x[n] is the nth sample in the time domain, X[k] is the kth component in the frequency domain, i is the imaginary unit, e is the base of the natural logarithm, and N is the total number of samples. In the time domain, a "sample" is the signal strength at a specific time rate; in the frequency domain, a "component" is the complex expression of a specific frequency component, which contains the amplitude (complex modulus) and phase information (complex argument) of the corresponding frequency component.

[0046] Peak location is performed on the frequency domain data. The left and right neighbors are found for each data point and compared. When a data point is larger than its neighbors on both sides, it is determined to be a peak and used as the noise level.

[0047] For example, suppose we have a continuous audio signal (such as a simple sine wave), and the analog-to-digital converter samples it, resulting in the following signal values ​​at four discrete time points: time domain signal x[n] = {1, 0, -1, 0}. This simple sequence is regarded as four equally spaced samples within one period of a periodic signal.

[0048] Using the discrete Fourier transform formula, this set of discrete time domain data is converted into a complex representation in the frequency domain, and each frequency component X[k] is calculated as follows:

[0049] X[0]=(1*e^0)+(0*e^0)+(-1*e^0)+(0*e^0)=0;;

[0050] X[1]=(1*e^0)+(0*e^(-iπ / 2))+(-1*e^(-iπ))+(0*e^(-i3π / 2))=1-(-1)=2;

[0051] X[2]=(1*e^0)+(0*e^(-iπ))+(-1*e^(-i2π))+(0*e^(-i3π))=0;

[0052] X[3]=(1*e^0)+(0*e^(-i3π / 2))+(-1*e^(-i3π))+(0*e^(-i9π / 2))=1-(-1)=2.

[0053] According to the peak location, the frequency components X[1] and X[3] are both 2, while the components X[0] and X[2] are both 0. This sequence contains two peaks, at k=1 and k=3 respectively.

[0054] The analysis unit 301 in the comprehensive analysis and optimization module 300 receives the signal strength and noise level of different devices sent by the signal strength evaluation module 100 and the noise level evaluation module 200, and uses the KNN algorithm to prioritize the signal strength and noise level of different devices to determine the target pairing device, wherein the KNN algorithm is a K-nearest neighbor algorithm, and the algorithm process specifically includes:

[0055] Training data collection: using a public database to collect training data, the training data includes the same data features as the signal strength and noise level of different devices sent by the signal strength evaluation module 100 and the noise level evaluation module 200, as well as the priority score, and the data features in the training data are integrated into a feature vector, wherein the priority score is used as the target variable;

[0056] Device priority scoring: For the device to be scored, it is compared with all the feature vectors in the training data. The comparison method is Chebyshev distance. For each dimension of the feature vector in two points, the absolute value of the difference between the coordinate values ​​is calculated, and the maximum value of the coordinate difference is found as the Chebyshev distance of the two points. For each device to be scored, the K nearest neighbors are found. These neighbors are the K data points in the training data that are closest to the device to be scored in the feature space. The k value determines the number of neighboring samples considered and has an impact on the classification result. If the k value is set too small, the model may be too sensitive to random noise; if it is set too large, the model may not be accurate enough in grasping the classification boundary; set the k value to 3; find the nearest k neighbors, and the classification decision will be made through "majority voting". Each neighbor votes for its category, and the query point will be classified into the category with the most votes; output the predicted classification, that is, the corresponding priority score (target variable).

[0057] The analysis unit 301 selects the device with the highest priority score as the target paired device, and sends the signal strength and noise level of the device to the optimization unit 302. The optimization unit 302 performs optimization processing according to the signal strength and noise level of the target paired device, specifically including:

[0058] Calculate the average signal strength of the remaining devices, where the remaining devices are all devices except the target device, and the average signal strength is the sum of the signal strengths of the remaining devices divided by the number of the remaining devices;

[0059] When the signal strength of the target paired device is greater than or equal to the average signal strength of the remaining devices, and the noise level of the target paired device is less than or equal to the noise level of the remaining devices, the target paired device is determined to be the final paired device;

[0060] When the signal strength of the target paired device is less than the average signal strength of the remaining devices, the optimization unit 302 is started to amplify the signal strength of the target paired device in the signal strength evaluation module 100, and a gain factor is added through the internal signal amplifier to amplify the signal strength of the target device, where the gain factor = the average signal strength of the remaining devices - the signal strength of the target paired device;

[0061] When the noise level of the target paired device is greater than the noise levels of the remaining devices, the optimization unit 302 is started to perform denoising on the noise level of the target paired device in the noise level assessment module 200 by removing the DC bias, eliminating the average voltage level in the signal, subtracting the average value of the signal from each sample point in the signal so that the signal is centered on a zero value in time, and using a low-pass filter to remove the frequency range that limits the analysis, retaining useful frequency components while removing non-target frequencies that are considered to be noise;

[0062] After the optimization process, the previous comparison and optimization logic is used to make judgments again, and multiple iterations are performed until the final result is determined.

[0063] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A Bluetooth headset pairing system with automatic noise reduction recognition and pairing, characterized by: It comprises a signal strength evaluation module (100), a noise level evaluation module (200), and a comprehensive analysis and optimization module (300), wherein: The signal strength evaluation module (100) uses a Bluetooth wireless transceiver to periodically collect Bluetooth signals of different Bluetooth devices, where the Bluetooth signals include a unique identifier of the device and a signal strength; The noise level assessment module (200) establishes a two-way communication link with different Bluetooth devices according to wireless scanning, for receiving audio signals, and converts the audio signal data into frequency domain data form by fast Fourier transform, for determining the noise level; The comprehensive analysis and optimization module (300) uses the KNN algorithm to prioritize the signal strength and noise level of different devices to determine the target paired device, and determines the final paired device according to the signal strength and noise level of the target paired device, specifically including: Calculate the average signal strength of the remaining devices, where the remaining devices are all devices except the target paired device, and the average signal strength is the sum of the signal strengths of the remaining devices divided by the number of the remaining devices; When the signal strength of the target paired device is greater than or equal to the average signal strength of the remaining devices, and the noise level of the target paired device is less than or equal to the noise level of the remaining devices, the target paired device is determined to be the final paired device; When the signal strength of the target paired device is less than the average signal strength of the remaining devices, a gain coefficient is added to the signal strength of the target paired device in the signal strength evaluation module (100) to perform amplification processing; When the noise level of the target paired device is greater than the noise levels of the remaining devices, the noise level of the target paired device in the noise level assessment module (200) is optimized by removing the DC bias and performing denoising processing using a low-pass filter; After the optimization process, the previous comparison and optimization logic is used again for judgment, and multiple iterations are performed until the final result is determined; The comprehensive analysis and optimization module (300) comprises an analysis unit (301) and an optimization unit (302); the analysis unit (301) uses a KNN algorithm to prioritize the signal strengths and noise levels of different devices to determine a target paired device, and determines a final paired device according to the signal strength and noise level of the target paired device; the optimization unit (302) adds a gain coefficient to the signal strength of the target paired device in the signal strength evaluation module (100) for amplification when the signal strength of the target paired device is less than the average signal strength of the remaining devices; and optimizes the noise level of the target paired device in the noise level evaluation module (200) by removing a DC offset and performing a denoising process using a low-pass filter when the noise level of the target paired device is greater than the noise level of the remaining devices; The analysis unit (301) uses the KNN algorithm to prioritize the signal strength and noise level of different devices, specifically including: Using a public database to collect training data, the training data includes the same data features as the signal strength and noise level of different devices sent by the signal strength assessment module (100) and the noise level assessment module (200), as well as priority scores, and integrating the data features in the training data into a feature vector, wherein the priority score is used as a target variable; For the device to be scored, it is compared with all the feature vectors in the training data. The comparison method is Chebyshev distance. For each dimension of the feature vectors of two points, the absolute value of the difference between the coordinate values ​​is calculated, and the maximum value of the coordinate difference is found as the Chebyshev distance of the two points. For each device to be scored, the K nearest neighbors are found. These neighbors are the K data points in the training data that are closest to the device to be scored in the feature space. The K value determines the number of neighboring samples considered and has an impact on the classification result. The K value is set to 3. The nearest K neighbors are found, and the classification decision will be made through "majority voting". Each neighbor casts a vote for its category, and the query point will be classified into the category with the most votes. The predicted classification is output as the corresponding priority score.

2. The Bluetooth headset pairing system with automatic noise reduction recognition pairing according to claim 1, characterized in that: The noise level assessment module (200) converts the audio signal data into frequency domain data form by fast Fourier transform, and the conversion process specifically includes: The continuous audio signal data is sampled into a set of discrete audio data by using an analog-to-digital converter; Use the discrete Fourier transform formula to convert a set of discrete audio data into another set of discrete audio data. The other set of discrete audio data represents complex values ​​in the frequency domain. For a set of sample sequences of length N, the discrete Fourier transform formula of each discrete frequency component X[k] is as follows: Where x[n] is the nth sample in the time domain, X[k] is the kth component in the frequency domain, i is the imaginary unit, e is the base of the natural logarithm, and N is the total number of samples; Peak location is performed on the frequency domain data. The left and right neighbors are found for each data point and compared. When a data point is larger than its neighbors on both sides, it is determined to be a peak and used as the noise level.

3. The Bluetooth headset pairing system with automatic noise reduction recognition pairing according to claim 2, characterized in that: The optimization unit (302) adds a gain coefficient to the signal strength of the target paired device in the signal strength evaluation module (100) to perform amplification processing, and adds the gain coefficient through an internal signal amplifier to amplify the signal strength of the target device, where the gain coefficient = the average signal strength of the remaining devices - the signal strength of the target paired device.

4. The Bluetooth headset pairing system with automatic noise reduction recognition pairing according to claim 3, characterized in that: The optimization unit (302) optimizes the noise level of the target paired device in the noise level assessment module (200) by removing the DC bias, eliminating the average voltage level in the signal, subtracting the average value of the signal from each sample point in the signal so that the signal is centered on a zero value in time, and using a low-pass filter to remove the frequency range that limits the analysis, retaining useful frequency components while removing non-target frequencies that are considered to be noise.

5. The Bluetooth headset pairing system with automatic noise reduction recognition pairing according to claim 4, characterized in that: The noise level assessment module (200) establishes a two-way communication link with different Bluetooth devices according to wireless scanning, identifies wireless signals sent by different Bluetooth devices and discovers Bluetooth devices by scanning wireless frequency bands, and sends a connection request to the Bluetooth device, wherein the connection request includes device information and handshake protocol data; after the Bluetooth device accepts the connection request, a two-way communication link between the noise level assessment module (200) and the Bluetooth device is formed through digital certificate verification, and after the connection is established, the Bluetooth device transmits audio signal data, wherein the audio signal data includes sound samples for testing and analysis.

6. The Bluetooth headset pairing system with automatic noise reduction recognition pairing according to claim 5, characterized in that: The optimization unit (302) adds a gain coefficient to the signal strength of the target paired device in the signal strength evaluation module (100) to perform amplification processing, and optimizes the noise level of the target paired device in the noise level evaluation module (200). The signal strength evaluation module (100) sends the signal strength of the target paired device after the optimization processing to the analysis unit (301), and the noise level evaluation module (200) sends the noise level of the target paired device after the optimization processing to the analysis unit (301). The analysis unit (301) makes a judgment according to the previous comparison and optimization logic, and iterates multiple times until a final result is determined.

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