Buzzer driving system with signal compensation function and method thereof

Abstract

A buzzer driving system with a signal compensation function and a method thereof are used for a buzzer, and the buzzer driving system comprises a control module and a filtering module. The control module is provided with a neural network unit, and the neural network unit generates a filter coefficient corresponding to the frequency response of the buzzer according to frequency spectrum data of the buzzer; the filtering module receives an audio playing signal and filters the audio playing signal according to the filtering coefficient so as to generate an output signal; according to the buzzer driving system, the signal compensation of the buzzer is realized through the adjustment of the filter coefficient, and the distortion of the output audio is prevented.

Description

Technical Field

[0001] This invention relates to a buzzer driving system and method, and more particularly to a buzzer driving system and method with signal compensation function. Background Technology

[0002] Buzzers are widely used as sound-generating devices in products such as alarms, multimedia devices, automotive electronic equipment, and toys. They can be mainly divided into piezoelectric and electromagnetic types. When a buzzer is powered on, the internal metal plate vibrates in the resonant cavity to produce sound.

[0003] Existing buzzers are driven by digital signals. Since the equivalent capacitance of a buzzer can reach tens to hundreds of nF, depending on the frequency response of the buzzer itself, it may attenuate or amplify signals of different frequencies, while also filtering out some signals, thus causing signal distortion and affecting audio quality. For example, short pulses in digital signals are filtered out by the buzzer, resulting in the loss of sound details.

[0004] Signal distortion causes the output sound quality of a buzzer to be inferior to that of a speaker. However, compared to a speaker, a buzzer still has irreplaceable advantages such as small size, loud volume, low cost, and resistance to damage.

[0005] Therefore, how to solve the existing problems of buzzers is one of the important issues that those skilled in the art want to address. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide a buzzer driving system and method with signal compensation function, which can adjust the filtering coefficient of the filtering circuit according to the frequency response of different buzzers, so as to compensate the signal during filtering, prevent the buzzer from causing signal distortion and retain more sound details, improve the sound quality of the buzzer, and enable the buzzer to display the sound details in the audio signal.

[0007] To achieve the aforementioned objective, the present invention provides a buzzer driving system with signal compensation function for a buzzer. The buzzer driving system includes a control module and a filtering module. The control module has a neural network unit that generates a filtering coefficient corresponding to the frequency response of the buzzer based on a spectrum of the buzzer. The filtering module receives an audio playback signal and filters the audio playback signal according to the filtering coefficient to generate an output signal.

[0008] Based on the objectives of this invention, this invention further provides a buzzer driving method with signal compensation function, comprising: acquiring a complex test voltage generated by the buzzer receiving a complex test signal; performing a fast Fourier transform analysis on the complex test voltage to generate a spectrum data of the buzzer; generating a filter coefficient corresponding to the frequency response of the buzzer by calculating the spectrum data through a neural network; and filtering an audio playback signal according to the filter coefficient to generate an output signal to drive the buzzer.

[0009] In the buzzer driving system and method with signal compensation function of the present invention, the trained neural network unit calculates and generates the filtering coefficient corresponding to the spectrum data, and controls the filtering module to adjust its own filtering coefficient accordingly, so that the filtering module can process the signal to conform to the frequency response of various buzzers, so as to compensate the signal during filtering and present sound quality and sound details comparable to ordinary loudspeakers. Attached Figure Description

[0010] Figure 1 This is a block diagram illustrating an embodiment of the buzzer drive system with signal compensation function of the present invention.

[0011] Figure 2 This is another block diagram of an embodiment of the buzzer drive system with signal compensation function of the present invention.

[0012] Figure 3 This is a flowchart of the buzzer driving method with signal compensation function according to the present invention.

[0013] Figure 4 This is another flowchart of the buzzer driving method with signal compensation function of the present invention.

[0014] 1. Buzzer drive system; 2. Buzzer; 10. Filtering module; 20. Control module; 21. Neural network unit; 30. Analog-to-digital conversion module; 40. Fast Fourier transform module; S10-S60 steps. Detailed Implementation

[0015] Please refer to Figure 1 As shown, the main objective of this invention is to provide a buzzer drive system 1 with signal compensation function, which can adjust the filtering coefficient of the filtering circuit according to the frequency response of different buzzers, so as to compensate the signal during filtering, prevent the buzzer from causing signal distortion and retain more sound details. The following describes possible embodiments of the invention in detail with reference to the drawings. However, it should be noted that the following implementation details are not intended to limit the scope of the claims made by this invention, but are merely for the convenience of those skilled in the art to understand.

[0016] The present invention provides a buzzer drive system 1 with signal compensation function, which is electrically connected to a buzzer 2 and includes a filter module 10 and a control module 20. The buzzer drive system 1 is independent of the buzzer 2 and is separately configured, therefore it can be directly used with existing buzzers 2 without requiring adjustments to the buzzer 2's architecture.

[0017] The filtering module 10 is electrically connected to the buzzer 2. The filtering module 10 receives an audio playback signal corresponding to the buzzer 2 from the outside and filters the audio playback signal according to a filtering coefficient to generate an output signal to the buzzer 2.

[0018] In a preferred embodiment, the filtering module 10 may be an active filter or a passive filter, and the filtering coefficient is an adjustable parameter, for example, the filtering coefficient can be adjusted by adjusting the number of inductors, resistors and / or capacitors and other components that are turned on in the filtering module 10.

[0019] In a preferred embodiment, the filtering module 10 includes an Nth-order finite impulse response (FIR) filter, and the filter coefficients are Nth-order filter coefficients.

[0020] The control module 20 is electrically connected to the filter module 10 and includes a neural network unit 21. The neural network unit 21 calculates and generates a filter coefficient corresponding to the frequency response of the buzzer 2 based on the spectrum data of the buzzer 2. The control module 20 can generate a control signal to the filter module 10 based on the filter coefficient and adjust the filter coefficient of the filter module 10 through the control signal. The control module 20 can be a central processing unit (CPU), graphics processing unit (GPU), or other computing device. The neural network unit 21 can include a deep neural network (DNN) model and is pre-trained using complex sample spectrum data and complex sample filter coefficients corresponding to the complex sample spectrum data, so that the neural network unit 21 can calculate the filter coefficient corresponding to the frequency response of different buzzers based on the spectrum data of different buzzers.

[0021] In some embodiments, the neural network unit 21 may be trained using one or more well-known artificial intelligence (AI) learning algorithms or machine learning algorithms, which may include neural networks (e.g., artificial neural networks, deep neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, reinforcement learning, etc.), fuzzy logic, artificial intelligence (AI), deep learning algorithms, deep structured learning hierarchical learning algorithms, support vector machines (SVMs) (e.g., linear SVMs, nonlinear SVMs, SVM regression), decision tree learning (e.g., classification and regression trees (CART)), dimensionality reduction algorithms (e.g., projection, manifold learning, principal component analysis, etc.) and / or deep machine learning algorithms.

[0022] The implementation of neural network unit 21 may include at least two phases: a training phase (also known as a learning phase) and an inference phase (also known as a generation phase). Neural network unit 21 generates filter coefficients during the inference phase.

[0023] In the training of the neural network unit 21, a sample spectrum data corresponds to a sample filter coefficient. The associated sample spectrum data and sample filter coefficient correspond to the same sample buzzer. The sample spectrum data is related to the equivalent circuit and frequency response information of the corresponding sample buzzer, while the sample filter coefficient is the filter coefficient value required to adjust the signal to match the frequency response of the corresponding sample buzzer during filtering.

[0024] Each sample spectrum data is generated based on the complex sample voltages produced when a sample buzzer receives complex test signals, and these complex sample voltages are analyzed using a Fast Fourier Transform (FFT). The signal frequencies of each test signal increase sequentially according to a default frequency difference. For example, with a default frequency difference of 200Hz, the signal frequencies of the complex test signals could be 200Hz, 400Hz, and 600Hz sequentially. Two consecutive test signals are spaced 200Hz apart, and these complex test signals are superimposed to form a test signal with a wide frequency coverage. Preferably, each test signal has the same signal strength and they are superimposed.

[0025] The complex sample spectrum data serves as the input data for the deep learning model, while the complex sample filter coefficients serve as the output data for the deep learning model. During the training phase, the neural network unit 21 is trained using sample spectrum data and corresponding sample filter coefficients from multiple sample buzzers, so that the neural network unit 21 can establish the operational relationship between the spectrum data and filter coefficients of different buzzers.

[0026] By setting the filtering coefficient of the filtering module 10 according to the spectrum data of the buzzer 2, the signal processing of the filtering module 10 can complement the influence of the buzzer 2 on the signal. For example, in a frequency band that is weakened by the buzzer 2, the filtering module 10 pre-amplifies the signal in that frequency band, so that the signal in that frequency band is weakened by the buzzer 2 but still meets the original signal strength, thereby preventing signal distortion.

[0027] Please refer to Figure 2 As shown, in order to obtain the spectrum data of the buzzer 2, the buzzer driving system 1 with signal compensation function of the present invention further includes an analog-to-digital conversion module 30 and a fast Fourier transform module 40. The buzzer 2 receives complex test signals, which can be output to the buzzer 2 through an external device. The complex test signals are the same as the complex test signals received by each sample buzzer when training the neural network unit 21.

[0028] The analog-to-digital conversion module 30 is connected to the buzzer 2 and receives the complex test voltage generated by the buzzer 2 receiving the complex test signal. The analog-to-digital conversion module 30 may include an analog-to-digital converter (ADC).

[0029] The Fast Fourier Transform (FFT) module 40 is connected to the analog-to-digital converter (ADC) module 30 and the neural network unit 21. It receives the complex test voltage from the ADC module 30, performs FFT analysis on the complex test voltage to generate the spectral data of the buzzer 2, and transmits this spectral data to the neural network unit 21 as its input value. The FFT analysis may include presenting the time-domain signal in the frequency domain. In this embodiment, the FFT module 40 can be separate from the control module 20, or it can be integrated into the control module 20 along with the neural network module.

[0030] Preferably, a signal processing module can be connected between the filter module 10 and the buzzer 2. The signal processing module amplifies and filters the output signal generated by the filter module 10, and then outputs the processed output signal to the buzzer.

[0031] The buzzer 2 can be a piezoelectric buzzer 2, the main structure of which includes a piezoelectric component, a metal sheet and a housing. The piezoelectric component can be made of piezoelectric ceramic material. When subjected to voltage, the piezoelectric component deforms due to the piezoelectric effect, which drives the metal sheet to vibrate and produce sound. In addition to covering the piezoelectric component and the metal sheet, the housing also constitutes the resonant cavity of the piezoelectric component and the metal sheet.

[0032] Compared to ordinary speakers, this buzzer has the advantages of low power consumption, loud sound, small size, low cost, and resistance to damage. Moreover, the structure and material characteristics of this buzzer enable it to maintain high durability and stability even in extreme environments such as high temperature or humidity. Compared to speakers that are easily damaged and have a high unit price, this buzzer has irreplaceable advantages.

[0033] Please refer to Figure 3 As shown, the buzzer driving method with signal compensation function of the present invention is applied to the buzzer 2 and can be executed by the buzzer driving system 1. The buzzer driving method includes the following steps:

[0034] S10: Acquire the complex test voltage generated by the complex test signals received by the buzzer 2. Step S10 can be executed by the analog-to-digital conversion module 30, and the test signals are sequentially increased or decreased according to the default frequency difference.

[0035] S20: Perform a Fast Fourier Transform (FFT) analysis on the complex test voltage to generate a spectrum of data for the buzzer 2. Step S20 can be executed by the FFT module 40.

[0036] S30: Using neural network technology, a filter coefficient corresponding to the frequency response of the buzzer 2 is generated based on the spectrum data. Step S30 can be executed by the neural network unit 21 in the control module 20.

[0037] S40: Filter an audio playback signal according to the filtering coefficient to generate an output signal to drive the buzzer 2. Step S40 can be executed by the filtering module 10.

[0038] Please refer to Figure 4 As shown, the buzzer driving method with signal compensation function of the present invention also includes a model training process, which is used for training the neural network unit 21:

[0039] S50: Receive complex test signals to generate complex sample voltages, and perform Fast Fourier Transform analysis on these complex sample voltages to generate complex sample spectrum data. Preferably, complex sample buzzers are used to receive complex test signals and generate complex sample voltages, and then Fast Fourier Transform analysis is performed on the complex sample voltage of each sample buzzer to generate the complex sample spectrum data of that complex buzzer 2.

[0040] S60: Train the model based on the complex sample spectrum data and the complex sample filter coefficients corresponding to the complex sample spectrum data.

[0041] In summary, the buzzer drive system 1 with signal compensation function of the present invention first calculates the spectrum data of the buzzer 2 through fast Fourier analysis, and then the trained neural network unit 21 calculates the filter coefficient corresponding to the spectrum data, and controls the filter module 10 to adjust its own filter coefficient accordingly, so that the signal processing of the filter module 10 can conform to the frequency response of various buzzers 2, so as to compensate the signal during filtering, prevent the buzzer 2 from causing signal distortion and retain more sound details, so that the buzzer 2 can retain the original audio sound quality, so that the buzzer 2 can exhibit sound quality comparable to that of a general loudspeaker, while retaining the advantages of the buzzer 2 such as large volume, low component cost and high durability.

[0042] This invention is disclosed herein only by preferred embodiments. Those skilled in the art should understand that the above embodiments are merely for describing the invention and are not intended to limit the scope of the claims. Any variations or substitutions equivalent to the above embodiments should be interpreted as being within the spirit or scope of this invention. Therefore, the scope of protection of this invention should be based on the definition of the appended claims.

Claims

1. A buzzer driving system with signal compensation function, used for a buzzer, characterized in that, A control module has a neural network unit, which generates a filter coefficient corresponding to the frequency response of the buzzer based on a spectrum data of the buzzer. A filtering module receives an audio playback signal and filters the audio playback signal according to the filtering coefficients to generate an output signal.

2. The buzzer drive system with signal compensation function as described in claim 1, characterized in that, An analog-to-digital converter module is connected to the buzzer to collect the complex test voltage generated by the buzzer receiving complex test signals; A Fast Fourier Transform (FFT) module is connected to the analog-to-digital converter (ADC) module and the control module. The FFT module performs fast Fourier transform analysis on the complex test voltage acquired by the ADC module to generate the spectrum data.

3. The buzzer drive system with signal compensation function as described in claim 1, characterized in that, The filtering module includes an Nth-order finite impulse response filter; the filtering coefficients are Nth-order filtering coefficients.

4. The buzzer drive system with signal compensation function as described in claim 2, characterized in that, Each test signal has a different signal frequency, and the signal frequencies of the complex test signals increase or decrease sequentially according to a default frequency difference.

5. The buzzer drive system with signal compensation function as described in claim 4, characterized in that, Each test signal has the same signal strength, and the complex test signals are superimposed on each other.

6. The buzzer drive system with signal compensation function as described in claim 1, characterized in that, The neural network unit trains the model based on complex sample spectrum data and the complex sample filter coefficients corresponding to the complex sample spectrum data; Each sample spectrum data is generated by receiving complex test signals from a sample buzzer and performing fast Fourier transform analysis on the complex sample voltages.

7. A buzzer driving method with signal compensation function, applied to a buzzer, characterized in that, The method includes the following steps: Collect the complex test voltage generated by the buzzer receiving complex test signals; The complex test voltage is subjected to Fast Fourier Transform analysis to generate a spectrum of data for the buzzer; A filter coefficient corresponding to the frequency response of the buzzer is generated by calculating the frequency response of the buzzer based on the spectrum data using a neural network. and An audio playback signal is filtered according to the filtering coefficients to generate an output signal that drives the buzzer.

8. The buzzer driving method with signal compensation function as described in claim 7, characterized in that, The filtering coefficients are Nth-order filtering coefficients of an Nth-order finite impulse response filter.

9. The buzzer driving method with signal compensation function as described in claim 7, characterized in that, Each test signal has a different signal frequency, and the signal frequencies of the complex test signals increase or decrease sequentially according to a default frequency difference.

10. The buzzer driving method with signal compensation function as described in claim 7, wherein, Further, a model training process is executed, wherein the steps of the model training process are characterized in that, The system receives complex test signals to generate complex sample voltages, and performs fast Fourier transform analysis on the complex sample voltages to generate complex sample spectrum data. The model is trained based on the complex sample spectrum data and the corresponding complex sample filter coefficients.

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