A speaker parameter adjustment system and method

Abstract

The embodiment of the specification provides a sound box parameter adjustment system and method, the system comprises a sound box, a microphone, a pressure sensor and a microprocessor, the microprocessor is configured to: when the sound pressure level of an ambient sound meets a preset sound pressure condition, control the sound box to emit an excitation signal to a bearing surface; control the microphone to collect a sound wave signal, and control the pressure sensor to collect a vibration signal of the bearing surface; extract a first characteristic value of the sound wave signal and a second characteristic value of the vibration signal, and determine a material parameter of the bearing surface based on the first characteristic value and the second characteristic value; determine an equalizer parameter based on the material parameter, and control the sound box to execute the equalizer parameter.

Description

Technical Field

[0001] This specification relates to the field of audio adjustment technology, and in particular to a speaker parameter adjustment system and method. Background Technology

[0002] With the development of audio technology, small desktop speakers have become widely used due to their portability and aesthetics. These speakers are typically placed on surfaces made of different materials such as solid wood, MDF, marble, and metal. Due to the differences in material density and elastic modulus, the sound propagation characteristics vary significantly across these surfaces, resulting in the same speaker exhibiting drastically different listening experiences on different desktops. Currently, small desktop speakers generally suffer from the following problems: different surfaces have varying bass absorption and reflection characteristics, leading to significant differences in bass volume; the strong equalizer driving strategy used to compensate for the weak bass reproduction capability of small-diameter drivers is prone to causing resonance noise and speaker displacement; and existing speakers cannot effectively detect environmental interference, limiting their tuning performance.

[0003] Therefore, it is necessary to provide a speaker parameter adjustment system and method that enables the speaker to actively sense the physical properties of the supporting surface and adaptively adjust the audio parameters, thereby providing consistent sound quality performance on desktops of different materials. Summary of the Invention

[0004] This specification provides one or more embodiments of a speaker parameter adjustment system. The system includes a speaker, a microphone, a pressure sensor, and a microprocessor. The microprocessor is configured to: control the speaker to emit an excitation signal to a support surface when the sound pressure level of the ambient sound meets a preset sound pressure condition; control the microphone to collect sound wave signals and control the pressure sensor to collect vibration signals from the support surface; extract a first feature value of the sound wave signal and a second feature value of the vibration signal, and determine the material parameters of the support surface based on the first feature value and the second feature value; determine equalizer parameters based on the material parameters, and control the speaker to execute the equalizer parameters.

[0005] This specification provides one or more embodiments of a speaker parameter adjustment method, which is executed by a microprocessor in a speaker parameter adjustment system. The system further includes a speaker, a microphone, and a pressure sensor. The method includes: controlling the speaker to emit an excitation signal to a support surface when the sound pressure level of the ambient sound meets a preset sound pressure condition; controlling the microphone to collect sound wave signals and controlling the pressure sensor to collect vibration signals from the support surface; extracting a first feature value of the sound wave signal and a second feature value of the vibration signal, and determining the material parameters of the support surface based on the first feature value and the second feature value; determining equalizer parameters based on the material parameters, and controlling the speaker to execute the equalizer parameters.

[0006] This specification provides a speaker parameter adjustment device according to one or more embodiments. The device includes: at least one storage medium for storing computer instructions; and at least one processor for executing the computer instructions to implement the speaker parameter adjustment method.

[0007] This specification provides one or more embodiments of a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions from the storage medium, the computer executes the speaker parameter adjustment method. Attached Figure Description

[0008] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0009] Figure 1 This is an exemplary structural diagram of a speaker parameter adjustment system according to some embodiments of this specification; Figure 2 This is an exemplary flowchart of a speaker parameter adjustment method according to some embodiments of this specification; Figure 3 This is an exemplary flowchart illustrating the control of a pressure sensor to acquire vibration signals from a bearing surface, according to some embodiments of this specification. Figure 4 This is an exemplary schematic diagram illustrating the acquisition of a clean vibration signal according to some embodiments of this specification; Figure 5 This is an exemplary schematic diagram illustrating the determination of material parameters of the bearing surface according to some embodiments of this specification. Detailed Implementation

[0010] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0011] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0012] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0013] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0014] Figure 1 This is an exemplary structural diagram of a speaker parameter adjustment system according to some embodiments of this specification. In some embodiments, the speaker parameter adjustment system 100 may include a speaker 110, a microphone 120, a pressure sensor 130, and a microprocessor 140.

[0015] A speaker parameter adjustment system refers to a collection of devices that can sense the physical properties of the supporting surface and dynamically adjust the audio output parameters based on the sensing results in order to achieve adaptive sound quality optimization.

[0016] A speaker is a device used to convert audio electrical signals into sound signals. A speaker may include sound-generating components such as a woofer. In some embodiments, the speaker 110 is used, under the control of a microprocessor 140, to transmit excitation signals to a bearing surface and to execute equalizer parameters to adjust the frequency response characteristics of the output audio.

[0017] A microphone is a sensor used to collect sound wave signals. In some embodiments, microphone 120 is used to collect ambient sound and sound wave signals reflected or radiated by a bearing surface.

[0018] A pressure sensor is a sensor used to sense mechanical signals. In some embodiments, a pressure sensor 130 is disposed at the bottom of a speaker 110 for contacting the bearing surface and acquiring vibration signals from the bearing surface.

[0019] A microprocessor is an integrated circuit used to execute data processing and control instructions. A microprocessor can be an embedded chip with digital signal processing capabilities, such as an ARM Cortex-M series microcontroller, a digital signal processor, or a dedicated audio processing chip. Microprocessors connect to pressure sensors, microphones, accelerometers, and sensing arrays via general-purpose input / output interfaces, read and write data to memory via an integrated circuit bus or serial peripheral interface, and control the audio output of speakers via pulse-width modulation or the integrated circuit's built-in audio bus interface.

[0020] In some embodiments, the microprocessor 140 is used to perform operations such as signal processing, feature extraction, material recognition, and parameter decision-making. In this specification, the microprocessor is used to perform a speaker parameter adjustment method.

[0021] In some embodiments, the speaker parameter adjustment system may further include a memory. A memory is a physical device used to store data and programs. In some embodiments, the memory may include random access memory (RAM), read-only memory (ROM), mass storage memory, removable memory, volatile read-write memory, and any combination thereof. In some embodiments, the memory may be integrated into or included in one or more other internal components of the speaker parameter adjustment system, or it may be a separate component.

[0022] In some embodiments, the various internal components of the speaker parameter adjustment system (such as the speaker, microprocessor, pressure sensor, and microphone) and the system can interact with external components via a network. In some embodiments, the network can include any one or more of wired or wireless networks. For example, the network can include cable networks, fiber optic networks, telecommunications networks, the Internet, local area networks (LANs), Bluetooth networks, near field communication (NFC), device internal buses, device internal wiring, cable connections, etc., or any combination thereof. In some embodiments, the network can be various topologies such as point-to-point, shared, and centralized, or a combination of multiple topologies.

[0023] This system constructs a "sound-force dual-dimensional" active sensing architecture through a hardware combination of speakers, microphones, pressure sensors, and a microprocessor. The microphone and pressure sensor simultaneously acquire the response of the load-bearing surface to the excitation signal from two physical dimensions: airborne sound waves and solid vibrations, respectively. The two sensors are physically independent but complementary in their information dimensions, effectively avoiding the problem of spatial reverberation interference when using a single microphone. The pressure sensor, located at the bottom of the speaker and in direct contact with the load-bearing surface, can directly capture the mechanical feedback of the surface, providing the microprocessor with independent physical quantity information distinct from the acoustic signal. The microprocessor, as the control and processing core of the system, integrates full-link processing capabilities for signal acquisition, feature extraction, material identification, and parameter decision-making. This enables the system to automatically trigger tests, complete material determination, and output equalizer parameters when the ambient sound meets preset sound pressure conditions, achieving closed-loop control from environmental perception to audio optimization.

[0024] This system can be applied to devices such as small desktop speakers, smart speakers, and portable Bluetooth speakers that need to be placed on surfaces with different materials to play audio. For example, when a user places the same smart speaker on a solid wood desk, a glass coffee table, or a marble dining table, the system can automatically sense the difference in the surface material and dynamically adjust the equalizer parameters to ensure consistent bass performance in different scenarios, while preventing the speaker from generating noise or physical displacement due to resonance with the surface.

[0025] Figure 2 This is an exemplary flowchart of a speaker parameter adjustment method according to some embodiments of this specification. In some embodiments, process 200 may be executed by microprocessor 140. Figure 2 As shown, process 200 may include the following steps:

[0026] Step 210: When the ambient sound pressure level meets the preset sound pressure condition, control the speaker to emit an excitation signal to the bearing surface.

[0027] Ambient sound refers to the sum of background sounds produced by non-speaker units within the physical space where the speaker is located. Examples include the hum of an air conditioner and the conversations of people in the room.

[0028] Sound pressure level (SPL) is a physical quantity that describes the intensity of sound, usually measured in decibels (dB). In some embodiments, SPL is obtained by collecting and calculating data using a microphone.

[0029] The excitation signal refers to a known characteristic audio signal actively emitted by the speaker to test the physical feedback of the load-bearing surface. The excitation signal can be preset, for example, a low-frequency linear sweep signal from 20Hz to 200Hz.

[0030] The load-bearing surface refers to the surface of the object on which the speaker is placed and in physical contact. For example, a desktop, tabletop, or floor surface, which may be made of solid wood, medium-density fiberboard, marble, or metal.

[0031] Preset sound pressure level (SPL) conditions refer to pre-defined sound intensity conditions used to determine whether environmental interference meets test requirements. SPL conditions can be preset; for example, the ambient sound SPL level can be set to not exceed a preset SPL threshold (e.g., 40 dB).

[0032] In some embodiments, the microprocessor can collect ambient sound through a microphone and calculate the sound pressure level. When it determines that the sound pressure level meets a preset sound pressure condition, it controls the speaker to emit an excitation signal toward the supporting surface. The method of controlling the speaker to emit the excitation signal may include sending a transmission command to the speaker to trigger the speaker's excitation signal emission action, etc.

[0033] Step 220: Control the microphone to collect sound wave signals and control the pressure sensor to collect vibration signals from the bearing surface.

[0034] A sound wave signal is a longitudinal wave electrical signal that carries the acoustic characteristics of the surrounding space after the excitation signal propagates through the air medium and is reflected or radiated by a supporting surface. For example, a 100Hz sound emitted by a speaker is reflected by a marble tabletop and captured by a microphone as a sound pulse. In some embodiments, the sound wave signal is acquired through a microphone.

[0035] A vibration signal refers to a mechanical wave electrical signal generated when an excitation signal is transmitted through the contacts at the bottom of the speaker to the bearing surface, causing mechanical displacement or stress changes on the bearing surface, and then reacting back to the sensor. For example, the physical vibration of a desktop under the impact of a speaker manifests as a tiny AC fluctuation output by a pressure sensor. In some embodiments, the vibration signal is acquired by a pressure sensor, which converts the mechanical vibration of the desktop into a corresponding electrical signal by sensing the tiny changes in the reaction force when the bearing surface vibrates.

[0036] In some embodiments, the microprocessor-controlled acquisition action includes: controlling a microphone via a conventional audio interface to record sound wave signals (such as reflected sound pulses) propagating in the air, while simultaneously acquiring vibration signals (such as minute AC fluctuations) generated by a pressure sensor due to physical vibrations of the desktop. The above signal acquisition process can be achieved through a basic sensor driver.

[0037] In some embodiments, the microprocessor can control the first sensing unit to acquire composite signals and control the second sensing unit to acquire ambient noise and system bias signals; using an AC-coupled filter circuit, the DC voltage signal in the composite signal is filtered out to extract the AC voltage signal representing the vibration of the bearing surface; based on the ambient noise and system bias signals, the AC voltage signal is compensated, and the compensated AC voltage signal is amplified using a gain amplifier circuit, and the amplified AC voltage signal is used as the vibration signal. For details on this part, please refer to [link to relevant documentation]. Figure 3 Related descriptions.

[0038] Step 230: Extract the first feature value of the acoustic signal and the second feature value of the vibration signal, and determine the material parameters of the bearing surface based on the first feature value and the second feature value.

[0039] The first eigenvalue refers to a quantitative value extracted from the acoustic signal that characterizes the acoustic reflectivity or absorption properties of the bearing surface. Examples include the peak frequency of the reflected sound, the acoustic decay time, or the energy proportion of a specific frequency band. In some embodiments, the microprocessor can extract the first eigenvalue in various ways. For example, the microprocessor can perform a Fast Fourier Transform on the acoustic signal to calculate the peak frequency of the reflected sound as the first eigenvalue.

[0040] The second eigenvalue refers to a quantified value extracted from the vibration signal that characterizes the mechanical impedance, natural frequency, or stiffness of the bearing surface. Examples include the natural resonant frequency of the bearing surface, the vibration decay time, or the peak vibration acceleration. In some embodiments, the microprocessor can extract the second eigenvalue in various ways. For example, the microprocessor can perform time-domain envelope analysis or frequency-domain peak detection on the vibration signal to calculate the vibration decay time or peak vibration acceleration as the second eigenvalue.

[0041] Material parameters refer to quantitative information used to describe the physical properties of the material supporting the surface. For example, material parameters can be material density type, such as high-density material corresponding to marble, and low-density material corresponding to solid wood or medium-density fiberboard. Material parameters can also be material density values, etc.

[0042] In some embodiments, the microprocessor can determine the material parameters of the bearing surface based on a first feature value and a second feature value in various ways. For example, the processor can obtain the corresponding material parameters by querying a first preset relationship table based on the first and second feature values. The first preset relationship table can be preset based on experimental data. Specifically, technicians select multiple bearing surface samples with known material density types, such as solid wood, stone, and composite boards; place a speaker on the surface of each bearing surface sample and emit a standard excitation signal; simultaneously collect sound wave signals and vibration signals using a microphone and pressure sensor to extract the corresponding first and second feature values; use the combination of the first and second feature values ​​as the input dimension, and the material density type of the bearing surface sample as the label, to establish a one-to-one mapping relationship data pair, thereby constructing the first preset relationship table and storing it in memory, so that the microprocessor can retrieve the matching material parameters based on the extracted first and second feature values ​​during runtime.

[0043] In some embodiments, the first characteristic value may include the arrival time of the acoustic signal, and the second characteristic value may include the arrival time of the vibration signal and the resonant frequency. The microprocessor can acquire the arrival time difference between the acoustic signal and the vibration signal to determine the wave velocity characteristic; determine the damping characteristic through the attenuation envelope of the vibration signal; and input the resonant frequency, wave velocity characteristic, and damping characteristic into the density recognition matrix to determine the material parameters. For more information on this topic, please refer to [link to relevant documentation]. Figure 5 Related descriptions.

[0044] Step 240: Determine the equalizer parameters based on the material parameters, and control the speaker to execute the equalizer parameters.

[0045] Equalizer parameters refer to the set of control instructions used to adjust the gain of different frequency components in the speaker's output signal. For example, a 3dB reduction at 100Hz and a 2dB increase at 1kHz.

[0046] In some embodiments, the microprocessor can determine the equalizer parameters based on material parameters in various ways. For example, the microprocessor can obtain the corresponding equalizer parameters by querying a second preset relationship table based on the material parameters. The second preset relationship table can be preset based on experimental data. Specifically, technicians measure the reference frequency response curve of the speaker on a non-resonant reference surface and the disturbed frequency response curve when the speaker is placed on multiple bearing surface samples with known material parameters. For each bearing surface sample with known material parameters, the amplitude-frequency deviation between the disturbed frequency response curve and the reference frequency response curve is calculated. A set of compensation coefficients is fitted using an inverse filtering algorithm to minimize the mean square error between the compensated frequency response curve and the reference frequency response curve. This set of compensation coefficients is determined as the equalizer parameters corresponding to that bearing surface sample. A one-to-one mapping relationship data pair is established between the equalizer parameters and the corresponding material parameters to construct the second preset relationship table and store it in memory, allowing the microprocessor to retrieve the matching equalizer parameters based on the material parameters during runtime.

[0047] The non-resonant reference surface refers to an ideal surface that does not produce additional resonance to the speaker's output sound. Examples include the hard floor of an anechoic chamber after acoustic treatment. The reference frequency response curve is the original response curve of sound pressure level versus frequency, collected by a microphone at a standard test position when the speaker is placed on the non-resonant reference surface without any equalization compensation. The disturbed frequency response curve is the actual response curve of sound pressure level versus frequency, collected by a microphone at the same standard test position when the speaker is placed on the surface sample. The compensation coefficient is the gain adjustment amount applied at each frequency point to counteract the coupling effect of the surface on the speaker's frequency response, making the disturbed frequency response curve approximate the reference frequency response curve.

[0048] In the embodiments of this specification, through the above-described speaker parameter adjustment method, the speaker can actively sense the physical properties of the placement environment and achieve high-precision material identification based on sound-force dual-dimensional fusion analysis, thereby dynamically matching equalizer parameters to ensure a consistent listening experience on surfaces with different material parameters.

[0049] The vibration signal collected by the pressure sensor includes not only the actual vibration of the bearing surface, but also the DC voltage signal generated by the speaker's own weight and environmental interference. To accurately extract the bearing surface vibration, this embodiment uses a first sensing unit and a second sensing unit to collect the composite signal, environmental noise, and system bias signal, respectively. After AC coupling filtering to extract the AC voltage signal, compensation and amplification are performed to finally obtain an accurate vibration signal.

[0050] Figure 3This is an exemplary flowchart illustrating the control of a pressure sensor to acquire vibration signals from a bearing surface, according to some embodiments of this specification. In some embodiments, the pressure sensor includes a first sensing unit disposed at the bottom of the speaker enclosure and a second sensing unit disposed inside the speaker enclosure. Flow 300 can be executed by microprocessor 140. Figure 3 As shown, process 300 may include the following steps 310-330.

[0051] Step 310: Control the first sensing unit to collect composite signals and control the second sensing unit to collect ambient noise and system bias signals.

[0052] The first sensing unit refers to the force-to-electric conversion device that directly bears the weight of the speaker and captures force fluctuations. For example, a thin-film pressure gauge installed inside the anti-slip feet of the speaker.

[0053] The second sensing unit refers to a reference sensing device that does not directly bear the weight of the speaker and is only used to sense environmental interference and the system's own state. For example, a pressure sensor suspended on the circuit board inside the speaker.

[0054] A composite signal refers to a superimposed electrical signal containing the constant voltage of the speaker's static gravity and the voltage of the minute vibration pulsations of the bearing surface. In some embodiments, the composite signal is obtained by reading the output voltage of the first sensing unit when the speaker emits an excitation signal.

[0055] The static gravity constant voltage of the speaker is caused by the speaker's own gravity and is obtained by reading the output voltage of the first sensing unit when the speaker is stationary and not playing music.

[0056] The pulsating voltage of the bearing surface due to minute vibration is the mechanical fluctuation that occurs over time when the bearing surface is excited to vibrate, which is the difference between the composite signal and the constant voltage of the speaker under static gravity.

[0057] Ambient noise refers to uncontrolled acoustic and vibration disturbances in a space. In some embodiments, ambient noise is collected by a second sensing unit.

[0058] The system bias signal refers to the fixed potential offset or zero drift generated by the second sensing unit and its associated signal conditioning circuit. When the speaker is not powered on or is in a static state without any external mechanical excitation, the system starts a self-test program. At this time, the circuit bottom level collected by the second sensing unit is the system bias signal.

[0059] Step 320: Using an AC coupling filter circuit, the DC voltage signal in the composite signal is filtered out to extract the AC voltage signal representing the vibration of the bearing surface.

[0060] An AC-coupled filter circuit refers to a hardware link used to filter out DC signals and retain AC signals. For example, an RC filter consisting of capacitors and resistors is connected in series at the output of the first sensing unit.

[0061] DC voltage signal refers to the low-frequency level component of a composite signal that represents the constant voltage of the speaker under static gravity.

[0062] AC voltage signal refers to the alternating level component of a composite signal that represents the pulsating voltage of the bearing surface vibration.

[0063] In some embodiments, the DC voltage signal is blocked when passing through the AC coupling filter circuit, and the AC voltage signal remaining after the composite signal is filtered out by the AC coupling filter circuit is the AC voltage signal.

[0064] Step 330: Based on the environmental noise and system bias signal, the AC voltage signal is compensated, the compensated AC voltage signal is amplified by a gain amplifier circuit, and the amplified AC voltage signal is used as a vibration signal.

[0065] A gain amplifier circuit is an electronic module that increases the amplitude of a signal. For example, a gain amplifier circuit may contain at least two operational amplifier stages, with the output of the first operational amplifier connected in series with the input of the second operational amplifier to form a cascaded amplifier circuit. The input of this cascaded amplifier circuit is connected to the output of an AC coupling filter circuit, and the output of the cascaded amplifier circuit is connected to a microprocessor.

[0066] In some embodiments, the processor can compensate the AC voltage signal in various ways based on environmental noise and system bias signals. For example, the microprocessor performs analog-to-digital conversion on the AC voltage signal to obtain a first digital sequence; performs analog-to-digital conversion on the signal composed of environmental noise and system bias signals to obtain a second digital sequence; after adaptive amplitude matching of the second digital sequence, the first digital sequence is subtracted from the matched second digital sequence point by point (the former subtracts the latter) to eliminate mixed environmental interference and system bias components, resulting in a compensated AC voltage signal; the compensated AC voltage signal is converted back to an analog signal by digital-to-analog conversion and sent to a gain amplifier circuit, which amplifies it by a fixed gain factor, and the amplified AC voltage signal is a vibration signal.

[0067] In some embodiments, the first sensing unit and the second sensing unit constitute a differential sensing module. The microprocessor can control the DC bias servo loop in the AC coupling filter circuit, and adjust the reference voltage at the front end of the pressure sensor in real time based on the DC voltage signal corresponding to the static pressure output by the first sensing unit to eliminate the DC bias caused by the static pressure in the system bias signal, thereby obtaining the real-time pressure value and the eliminated AC voltage signal. The temperature compensation bridge in the differential sensing module is used to monitor the temperature drift signal in the ambient noise, and the temperature drift signal is used as a common-mode signal to be canceled by the second sensing unit to obtain a pure AC voltage signal from the eliminated AC voltage signal. The gain amplifier circuit is controlled to call the sensitivity mapping curve according to the real-time pressure value to perform gain compensation on the pure AC voltage signal, and the compensated pure AC voltage signal is used as the vibration signal.

[0068] A differential sensing module is a compensation circuit composed of a first sensing unit and a second sensing unit through a subtraction operation. The output terminals of the first and second sensing units are respectively connected to the non-inverting and inverting input terminals of a differential operational amplifier. The differential operational amplifier performs a subtraction operation on the two input signals and outputs the difference between the two input signals.

[0069] A DC bias servo loop is a circuit structure that uses a negative feedback closed-loop system to automatically detect and cancel the DC bias in the input signal while maintaining the integrity of the AC voltage signal. For example, a DC bias servo loop includes hardware circuitry for an integrator. The input of the integrator is connected to the output of an operational amplifier in a gain amplifier circuit, and the output of the integrator is connected to the front end of a pressure sensor to control the reference voltage at the front end of the pressure sensor.

[0070] Static pressure refers to the constant pressure generated by the static gravity of the speaker, i.e., the constant static gravity pressure of the speaker mentioned above.

[0071] The reference voltage is the voltage value that allows the first sensing unit to operate at the midpoint of its linear operating region. The reference voltage is output from the DC bias servo loop and is used to offset the DC bias caused by static pressure, thus returning the operating point of the first sensing unit to the midpoint of its linear operating region.

[0072] The midpoint of the linear operating region refers to the position corresponding to the midpoint of the linear operating region of the first sensing unit. Specifically, when the input pressure of the first sensing unit is at the midpoint of the linear operating region, the positive and negative swing margin of the output voltage of the first sensing unit with pressure changes is the largest, making it an ideal working position for acquiring weak vibration signals. The voltage value at the midpoint of the linear operating region refers to the output voltage value predetermined during the calibration phase when the first sensing unit is operating at the midpoint of the linear operating region.

[0073] DC bias refers to the DC offset caused by static pressure, that is, the voltage offset of the first sensing unit's operating point from the midpoint of the linear operating region caused by static pressure.

[0074] The real-time pressure value refers to the total static pressure of the speaker on the bearing surface currently detected. The real-time pressure value is obtained by reading the voltage conversion value at the feedback terminal of the DC bias servo loop.

[0075] The eliminated AC voltage signal refers to the signal obtained after adjusting the reference voltage at the front end of the pressure sensor via the DC bias servo loop, thus removing the DC bias caused by static pressure from the AC voltage signal. The eliminated AC voltage signal no longer contains the operating point offset of the first sensing unit due to bearing the weight of the speaker, providing an accurate AC basis for subsequent temperature compensation and gain compensation.

[0076] In some embodiments, when the speaker is placed on a support surface, the first sensing unit experiences static pressure from the speaker. The DC voltage signal corresponding to this static pressure causes the operating point of the first sensing unit to deviate from the midpoint of the linear operating region. The DC bias servo loop compares the DC voltage signal corresponding to the static pressure with the voltage at the midpoint of the linear operating region in real time, calculating the deviation between the two. This deviation is the DC bias amount generated by the static pressure. Subsequently, the DC bias servo loop generates a compensation level equal in magnitude but opposite in polarity to this DC bias amount, which is output as a reference voltage to the front end of the pressure sensor to adjust the operating point of the first sensing unit back to the midpoint of the linear operating region.

[0077] Once this adjustment is complete, the DC bias caused by static pressure is canceled out by the reference voltage, and the output of the first sensing unit no longer contains the DC offset component generated by static gravity. At this time, the voltage conversion value at the feedback terminal of the DC bias servo loop directly reflects the total static pressure of the speaker on the bearing surface, and the microprocessor obtains the real-time pressure value accordingly. Simultaneously, the AC voltage signal adjusted by the reference voltage has eliminated the DC bias caused by static pressure in the system bias signal, resulting in the eliminated AC voltage signal.

[0078] A temperature-compensated bridge circuit refers to a structure that uses the characteristic of two units undergoing heating in the same direction to offset temperature drift. For example, it can be a circuit structure in which the first and second sensing units are arranged as adjacent arms of a Wheatstone bridge, and the first and second sensing units are connected to the non-inverting and inverting input terminals of a differential operational amplifier, respectively.

[0079] Temperature drift signal refers to the zero-point drift voltage generated by the first and second sensing units due to changes in ambient temperature. The temperature drift signal appears simultaneously on both sensing units as a common-mode signal and is sensed by the temperature-compensated bridge circuit's internal resistance changing with temperature.

[0080] Common-mode signals refer to interference signals that are equal in magnitude and phase and act simultaneously on both inputs of a differential operational amplifier. Temperature drift is a type of common-mode signal. Common-mode signals are eliminated through subtraction operations performed by the differential operational amplifier in the differential sensing module, with the second sensing unit providing a common-mode reference during this process.

[0081] In some embodiments, when the ambient temperature changes, the first and second sensing units generate temperature drift signals of equal magnitude and phase. The microprocessor can sense the temperature drift signal by measuring the temperature-dependent internal resistance of the temperature-compensated bridge. This temperature drift signal appears simultaneously as a common-mode signal at both the non-inverting and inverting inputs of the differential operational amplifier. The differential operational amplifier performs a subtraction operation on the signals at the non-inverting and inverting inputs. Since the temperature drift signals at the two inputs are equal in magnitude and phase, they are canceled out after subtraction, and the output signal no longer contains temperature drift interference. During this process, the second sensing unit provides a common-mode reference signal to the differential operational amplifier, enabling the temperature drift signal to be effectively identified and eliminated as a common-mode signal. Finally, the temperature drift signal is subtracted from the eliminated AC voltage signal to obtain a clean AC voltage signal.

[0082] A sensitivity mapping curve is a function that describes the sensitivity of a pressure sensor as a function of load. For example, a "real-time pressure value - gain factor" curve pre-stored in memory.

[0083] In some embodiments, after the microprocessor acquires the real-time pressure value, it calls the sensitivity mapping curve to determine the corresponding gain factor, controls the gain to compensate the pure AC voltage signal for the gain factor, and uses the compensated pure AC voltage signal as the vibration signal.

[0084] In some embodiments, the sensitivity mapping curve can be obtained in various ways. For example, in an experimental environment, a gradually increasing static pressure is applied to the pressure sensor to simulate speaker loads of different weights; at each static pressure, a standard micro-vibration signal with a constant amplitude is input to the pressure sensor using a high-precision exciter; the sensor's output voltage to this standard micro-vibration signal under different static pressures is recorded, the ratio of the output amplitude to the standard amplitude under no load is calculated, and its reciprocal is taken as the gain factor to be compensated; multiple sets of recorded "static pressure values" and "gain factors" are paired, and a continuous curve is generated using a polynomial fitting algorithm, which is the sensitivity mapping curve.

[0085] In some embodiments of this specification, the static pressure DC bias is eliminated by a servo loop, the temperature drift common-mode signal is canceled by a differential bridge, and the gain factor is dynamically compensated by a sensitivity mapping curve. This effectively solves the measurement deviation caused by sensor nonlinearity and significantly improves the sensing accuracy and stability of the system under extreme temperatures and different loads.

[0086] Some embodiments in this specification extract AC voltage signals through AC coupling filter circuits, thereby filtering out DC voltage generated by heavy loads, preventing signal saturation of the pressure sensor under high gravity, significantly improving the dynamic range of the system to respond to minute physical events, and solving the problem of difficulty in extracting weak signals under heavy load conditions.

[0087] Before extracting the second feature value, the microprocessor needs to purify the acquired vibration signal to remove interference components that do not belong to the feedback from the load-bearing surface. When the speaker emits an excitation signal, its shell vibration mixes with the vibration signal, forming self-vibration interference. At the same time, the foot pads attenuate and hysteresis the vibration feedback from the load-bearing surface. If used directly, the feature value will be distorted, affecting the accuracy of material determination. Therefore, this embodiment uses an accelerometer to obtain structural vibration characteristics, thereby filtering out self-vibration interference and restoring the waveform of the filtered signal to obtain a pure vibration signal.

[0088] Figure 4 This is an exemplary schematic diagram illustrating the acquisition of a clean vibration signal according to some embodiments of this specification.

[0089] In some embodiments, the speaker parameter adjustment system further includes an acceleration sensor disposed inside the speaker. For example... Figure 4 As shown, before extracting the second feature value, the microprocessor can control the accelerometer 410 to collect the structural vibration features 420 of the speaker; based on the structural vibration features 420, the self-vibration interference component 440 of the speaker is filtered out from the vibration signal 430; the waveform of the vibration signal 450 after filtering out the self-vibration interference component is restored to obtain a pure vibration signal 460.

[0090] An accelerometer is a sensor device used to measure acceleration signals. It is installed inside a speaker enclosure to sense the physical vibration of the speaker enclosure itself, such as a built-in microelectromechanical system accelerometer.

[0091] Structural vibration characteristics refer to the inherent physical vibration information generated by hardware such as the speaker enclosure and bracket when producing sound. For example, the resonant point of a plastic shell at 180Hz. In some embodiments, a microprocessor can send pre-built acquisition instructions to an accelerometer to control the accelerometer to acquire structural vibration characteristics.

[0092] Self-vibration interference refers to signals mixed in with the vibration signal that do not belong to the feedback from the load-bearing surface. In some embodiments, the microprocessor can filter out the self-vibration interference of the speaker from the vibration signal in various ways. For example, the microprocessor can perform correlation analysis between the structural vibration characteristics and the vibration signal, extract the co-frequency components of the two as self-vibration interference, and filter out the co-frequency components from the vibration signal by subtraction.

[0093] In some embodiments, the microprocessor can use an adaptive filtering algorithm based on structural vibration characteristics to filter out self-vibration interference components from the vibration signal. The step size factor of the adaptive filtering algorithm is inversely proportional to the environmental energy level monitored by the microphone.

[0094] Adaptive filtering algorithms are algorithms that can adjust filter coefficients in real time according to signal changes to counteract specific interference. For example, they may perform minimum mean square error (MMS) or normalized minimum mean square error (MMS) recursive calculations. Adaptive filtering algorithms are executed by adaptive filters.

[0095] In some embodiments, the microprocessor uses structural vibration characteristics as the reference input to an adaptive filter and the vibration signal as the original input to the adaptive filter. The adaptive filter processes the reference input using filter coefficients to generate an estimated interference signal. The microprocessor subtracts the estimated interference signal from the original input to obtain the vibration signal after filtering out the natural vibration interference component. In this process, the step size factor of the adaptive filtering algorithm is inversely proportional to the environmental energy level monitored by the microphone.

[0096] The step size factor is a control parameter that determines the iterative update speed and stability of an adaptive filtering algorithm.

[0097] Ambient energy level refers to a physical parameter captured by a microphone that is used to quantify the intensity of ambient noise. In some embodiments, when the ambient energy level increases, the microprocessor automatically reduces the step size factor to prevent ambient noise from causing the adaptive filter to diverge or misjudge.

[0098] The embodiments in this specification inversely correlate the step size factor of the adaptive filtering algorithm with the ambient energy level monitored by the microphone. When the ambient noise increases, the update step size of the filter is automatically reduced to prevent the filter coefficients from diverging or being misadjusted due to sudden external noise. This improves the accuracy and robustness of filtering out speaker self-vibration interference components in noisy environments.

[0099] Waveform restoration refers to the process of reversing the filtering effect of the foot pad on the vibration signal and restoring the true stress condition of the bearing surface.

[0100] In some embodiments, the microprocessor can restore the waveform of the vibration signal after filtering out self-vibration interference components in various ways to obtain a pure vibration signal. For example, the microprocessor can restore the waveform of the interference-filtered signal based on the real-time pressure value output by the pressure sensor and a preset damping characteristic curve of the foot pad.

[0101] The damping characteristic curve of the speaker foot pad describes the relationship between the attenuation coefficient and phase offset of the foot pad for vibrations at different frequencies under different pressure values. The attenuation coefficient characterizes the degree to which the foot pad weakens the vibration amplitude, and the phase offset characterizes the hysteresis of the vibration phase caused by the foot pad. The damping characteristic curve of the foot pad is pre-stored in memory. The foot pad is a buffer component installed between the bottom of the speaker cabinet and the supporting surface for anti-slip and vibration damping. For example, rubber pads installed at the four corners of the bottom of the speaker cabinet. The pressure value is the real-time pressure value output by the pressure sensor.

[0102] In some embodiments, the microprocessor uses the real-time pressure value output by the pressure sensor as an index to retrieve the attenuation coefficient and phase offset corresponding to the real-time pressure value from a preset foot pad damping characteristic curve; it performs frequency domain decomposition on the vibration signal after filtering out self-vibration interference components, multiplies the amplitude of each frequency component by the reciprocal of the corresponding attenuation coefficient, and performs phase lead correction according to the phase offset; it synthesizes the compensated frequency components into a time domain signal, thereby restoring the original vibration waveform that actually acts on the bearing surface, and obtaining a pure vibration signal.

[0103] In some embodiments, the microprocessor can use a preset calibration excitation signal as an excitation during a preset time period to update the foot pad damping characteristic curve in the memory.

[0104] A preset time period refers to a low-interference time interval that is manually set in advance. For example, the early morning period.

[0105] In some embodiments, the microprocessor controls the speaker to emit a preset calibration excitation signal during a preset time period, and simultaneously acquires the vibration signal obtained at this time after filtering out the self-vibration interference component as the actual vibration feedback; the microprocessor uses the preset calibration excitation signal as input and calculates the theoretical vibration signal based on the foot pad damping characteristic curve currently stored in the memory; by comparing the deviation between the actual vibration feedback and the theoretical vibration signal, a correction coefficient is calculated to update the foot pad damping characteristic curve stored in the memory.

[0106] The preset calibration excitation signal refers to a known audio signal that is pre-stored in memory and used to excite the bearing surface to evaluate the damping characteristics of the foot pad in offline calibration mode. For example, a low-frequency sweep signal in the same frequency range as the aforementioned excitation signal.

[0107] In some embodiments, the speaker parameter adjustment system further includes a reference sensor disposed inside the speaker. The microprocessor can control the reference sensor to acquire a reference signal; perform blind source separation between the vibration signal after filtering out self-vibration interference components and the reference signal based on an independent component analysis algorithm; extract the reaction vibration component of the bearing surface from the blind source separation result through a spatial transfer matrix, and use the reaction vibration component as the pure vibration signal.

[0108] A reference sensor is an accelerometer installed in a non-contact location inside the speaker enclosure. For example, a microelectromechanical system (MEMS) accelerometer installed on the inner wall of the speaker enclosure.

[0109] A reference signal is a baseline electrical signal acquired by a reference sensor that represents the vibration of the speaker's own structure. For example, it could be the electrical signal generated by the vibration of the speaker casing during playback. In some embodiments, a microprocessor can send a preset acquisition command to the reference sensor to control it to acquire the reference signal.

[0110] Independent Component Analysis (ICA) is a processing method that extracts independent signal components from multiple mixed signals using statistical algorithms when the mixing method of the source signals is unknown. In some embodiments, the microprocessor uses the vibration signal after filtering out self-vibration interference components and a reference signal as inputs to the ICA algorithm to perform blind source separation and obtain the blind source separation result.

[0111] Blind source separation results refer to the multiple independent signal components output by the independent component analysis algorithm after decoupling multiple mixed signals. In this specification, one independent component is the original vibration component of the speaker structure, and the other independent component is the reaction vibration component of the bearing surface.

[0112] The spatial transfer matrix is ​​a mathematical array that describes the amplitude attenuation ratio and phase shift of each frequency component as vibration energy propagates from the vibration source inside the speaker to different sensing positions. For example, a 2×2 matrix stored in memory specifies that the reference signal must be multiplied by a coefficient of 0.8 and shifted by 2ms phase to align with the original vibration components of the speaker structure mixed in with the vibration signal after filtering out self-vibration interference.

[0113] In some embodiments, in an unoccupied laboratory environment, the microprocessor can input a standard single-frequency excitation to the speaker, synchronously record the reference response signal output by the reference sensor and the pressure response signal output by the pressure sensor; calculate the amplitude ratio and phase difference of the reference response signal and the pressure response signal at different frequencies; and arrange the amplitude ratio and phase difference corresponding to each frequency into a matrix to obtain the spatial transfer matrix.

[0114] The reaction vibration component refers to the physical feedback vibration generated by the load-bearing surface on the speaker, which is decoupled from the mixed signal of the vibration signal after filtering out the self-vibration interference component and the reference signal after blind source separation processing.

[0115] In some embodiments, the microprocessor uses the signal obtained by mapping the reference signal through a spatial transfer matrix as the expected shape signal. For each independent component in the blind source separation result, the microprocessor calculates the cross-correlation coefficient between the independent component and the expected shape signal. If the cross-correlation coefficient is greater than or equal to a preset threshold, the independent component is determined to be highly correlated with the expected shape signal and is identified as a native vibration component of the speaker structure. If the cross-correlation coefficient is less than the preset threshold, it is determined to be uncorrelated and is identified as a reaction vibration component of the bearing surface. The microprocessor uses the identified reaction vibration component as a pure vibration signal.

[0116] The embodiments in this specification achieve decoupling of mixed signals rather than simple subtraction through independent component analysis and spatial transfer matrix, solving the problem of reference signal contamination caused by acoustic feedback, and enabling the system to accurately capture the weak response of the bearing surface even during high-power playback.

[0117] In some embodiments, the microprocessor can acquire the instantaneous pressure value and pressure change rate output by the pressure sensor; determine the energy loss value based on the instantaneous pressure value and pressure change rate; and perform amplitude correction and phase compensation on the AC voltage signal corresponding to the vibration signal after filtering out self-vibration interference components based on the energy loss value, determine the excitation force characteristic waveform of the bearing surface, and use the excitation force characteristic waveform as the pure vibration signal.

[0118] Instantaneous pressure value refers to the total force value sensed by the pressure sensor at a preset sampling time. It is composed of the constant pressure generated by the static gravity of the speaker and the pulsating pressure generated by the slight vibration of the bearing surface.

[0119] The rate of change of pressure refers to the speed at which the instantaneous pressure value fluctuates over time, i.e., the first derivative of the pressure signal.

[0120] Energy loss refers to the portion of vibration energy that is converted into heat and absorbed due to the material's internal resistance during transmission through the footpad. In some embodiments, the microprocessor substitutes the instantaneous pressure value and pressure change rate into a preset dynamic equation to calculate the energy loss value.

[0121] The excitation force characteristic waveform refers to the pure mechanical waveform that is restored and directly acts on the bearing surface after eliminating the interference of the foot pad.

[0122] In some embodiments, the microprocessor can perform amplitude correction and phase compensation on the AC voltage signal corresponding to the vibration signal after filtering out self-vibration interference components, based on the energy loss value. For example, the microprocessor converts the energy loss value into a corresponding voltage compensation amount according to a preset force-to-electricity conversion coefficient; the voltage compensation amount is superimposed with the amplitude of the AC voltage signal to obtain the corrected amplitude. The microprocessor determines the equivalent time delay based on the ratio of the energy loss value to the pressure change rate; the equivalent time delay is divided by the main vibration period of the AC voltage signal to obtain the phase lag angle; the sampling sequence of the AC voltage signal is resampled ahead of time according to the phase lag angle to compensate for the phase delay, obtaining the corrected phase information. The microprocessor synthesizes the corrected amplitude and the corrected phase information into an excitation force characteristic waveform, and uses the excitation force characteristic waveform as the pure vibration signal.

[0123] The embodiments in this manual completely eliminate the nonlinear distortion of vibration signals caused by foot pads with different hardness and aging levels, achieving precise "penetrating foot pad" sensing and ensuring the accuracy of the material parameters of the bearing surface.

[0124] This embodiment of the specification introduces an accelerometer to collect the structural vibration characteristics of the speaker itself. Using this as a reference, it filters out the speaker's self-vibration interference components from the vibration signal, and then restores the waveform of the filtered signal, achieving a complete separation between "speaker self-vibration" and "passive vibration of the load-bearing surface." This solution effectively prevents the speaker shell resonance from being misjudged as a characteristic of the load-bearing surface material, ensuring that the subsequently extracted second feature value accurately reflects the true physical properties of the load-bearing surface, thereby improving the accuracy of material identification.

[0125] After obtaining the first characteristic value of the acoustic signal and the second characteristic value of the vibration signal, the microprocessor can determine the material parameters of the bearing surface based on multi-dimensional feature fusion. Relying solely on a single feature such as resonant frequency for material determination is prone to misjudgment due to interference from factors such as the thickness and geometric dimensions of the bearing surface—for example, a thin steel plate and a thick wooden board may exhibit similar resonant frequencies, but their material densities are drastically different. To improve the accuracy of material identification, this embodiment introduces wave velocity characteristics and damping properties as supplementary determination dimensions.

[0126] Figure 5 This is an exemplary schematic diagram illustrating the determination of material parameters of the bearing surface according to some embodiments of this specification.

[0127] In some embodiments, such as Figure 5As shown, the first characteristic value includes the arrival time of the sound wave signal 510, and the second characteristic value includes the arrival time of the vibration signal 520 and the resonant frequency 530. The microprocessor can obtain the arrival time difference 540 between the sound wave signal and the vibration signal to determine the wave velocity characteristic 550; determine the damping characteristic 570 through the attenuation envelope 560 of the vibration signal; and input the resonant frequency 530, wave velocity characteristic 550, and damping characteristic 570 into the density recognition matrix 580 to determine the material parameters 590.

[0128] The arrival time refers to the time stamp from the moment of excitation to the first detection by the corresponding sensor after the excitation signal is emitted. The arrival time of the acoustic signal is a component of the first characteristic value; the arrival time of the vibration signal is a component of the second characteristic value.

[0129] The resonant frequency refers to the frequency point at which the bearing surface produces the maximum amplitude response after being excited. In some embodiments, the microprocessor can perform a fast Fourier transform on the vibration signal to obtain the power spectral density distribution and identify the peak frequency with the most concentrated energy as the resonant frequency.

[0130] The time difference of arrival (TDOA) refers to the difference between the arrival time of a sound wave signal and the arrival time of a vibration signal. In some embodiments, a microprocessor can use a cross-correlation algorithm to find the time offset point with the largest correlation coefficient between the sound wave signal and the vibration signal, and use this as the TDOA.

[0131] Wave velocity characteristic refers to an equivalent physical quantity that characterizes the propagation speed of a mechanical wave within a bearing surface. For example, the wave velocity characteristic of a certain type of wood surface is 1500 m / s. In some embodiments, a microprocessor can use preset speaker geometry (fixed distances from the sound-emitting unit to the pressure sensor and to the microphone) and the measured arrival time difference, combined with the air velocity constant, to inversely deduce the propagation speed of the mechanical wave in the solid using a formula, and determine this propagation speed as the wave velocity characteristic.

[0132] The attenuation envelope is a geometric contour line that describes the natural decay trend of the vibration signal amplitude over time. For example, it is a smooth curve that decreases exponentially from a peak value of 1V to 0.1V. In some embodiments, a microprocessor can perform a Hilbert transform on the digitized vibration signal to extract the analytical envelope of the vibration signal and determine the attenuation envelope.

[0133] Damping characteristics refer to the ability of a bearing surface material to absorb and dissipate vibrational energy. In some embodiments, a microprocessor can calculate the logarithmic reduction rate based on the attenuation envelope, that is, calculate the natural logarithm of the ratio of the amplitudes of two adjacent vibration periods, thereby obtaining the damping characteristics.

[0134] A density identification matrix is ​​a multidimensional lookup table stored in memory, used to match corresponding material parameters based on resonant frequency, wave velocity characteristics, and damping characteristics. The density identification matrix takes a vector composed of resonant frequency, wave velocity characteristics, and damping characteristics as its input vector. Material parameters are determined by calculating the Euclidean distance between the input vector and the feature vectors of each template within the matrix. For example, the material parameter corresponding to the template feature vector (resonant frequency f1, wave velocity characteristic v1, damping characteristic e1) is "high-density thin plate"; the material parameter corresponding to the template feature vector (resonant frequency f2, wave velocity characteristic v2, damping characteristic e2) is "low-density thick plate," where f1 is greater than f2, v1 is greater than v2, and e1 is less than e2. The density identification matrix is ​​pre-calibrated and set using experimental data.

[0135] In some embodiments, the speaker parameter adjustment system further includes a sensing array. The microprocessor can control the sensing array to acquire multiple vibration signals and calculate the spatial phase difference between each vibration signal to identify the geometric boundary constraint state of the bearing surface. Based on the geometric boundary constraint state, spatial compensation is performed on the multiple vibration signals. Harmonic distortion fingerprints are extracted from the compensated multiple vibration signals. The harmonic distortion fingerprints, resonant frequencies, wave velocity characteristics, and damping characteristics are input into the density recognition matrix to determine the material parameters.

[0136] A sensing array refers to a combination of multiple pressure sensors distributed at different physical locations on the speaker base to simultaneously collect multi-point mechanical information. For example, four piezoelectric pressure sensors are installed at the four support feet on the bottom of the speaker.

[0137] Multichannel vibration signals refer to independent vibration signals with location characteristics captured by each pressure sensor in a sensing array within the same time period. For example, digital signal vectors with the same timestamp but originating from different pressure sensors.

[0138] Spatial phase difference refers to the difference in the time it takes for the same vibration wave to arrive at different pressure sensors in a sensing array. In some embodiments, the microprocessor performs cross-correlation calculations on multiple vibration signals and finds the lag time offset corresponding to the maximum value of the cross-correlation function; this lag time offset is the spatial phase difference.

[0139] Geometric boundary constraint state refers to the physical relative relationship between the speaker's placement on the load-bearing surface and the geometric boundaries of the load-bearing surface, such as the edge of the table, table legs, or supporting beams. This includes localized central rigid support and free-field uniform plane. In some embodiments, the microprocessor compares the spatial phase difference and amplitude ratio of each vibration signal: if the phase difference between each vibration signal is extremely small (e.g., less than a preset phase difference threshold) and the amplitude is extremely high (e.g., greater than a preset amplitude threshold), then the load-bearing surface is determined to be in a central rigid support state, such as when there is table leg support directly below the speaker; if the phase difference between each vibration signal is linearly uniformly distributed, then it is determined to be in a free-field uniform plane state, such as when the speaker is located in the center of a large-area table.

[0140] Spatial compensation refers to the process of correcting the amplitude and phase of each vibration signal based on the geometric boundary constraints. In some embodiments, the microprocessor, based on the geometric boundary constraints, calls the position gain correction matrix in memory, multiplies the vibration signal near the geometric boundary of the bearing surface by a coefficient that reduces its weight, and smoothly aligns the phases of each vibration signal to obtain the compensated multi-channel vibration signals. For example, when a speaker is placed near a table leg, the vibration signal collected by the pressure sensor near the table leg has a larger amplitude due to the support of the table leg. The microprocessor reduces the amplitude weight of this vibration signal to prevent misjudging "table leg stiffness" as "material density".

[0141] Harmonic distortion fingerprints refer to the characteristics of a material during excited vibration, generated by its nonlinear elastic properties, consisting of the power ratio and phase coupling degree of higher-order harmonic components (second and third harmonics) relative to the fundamental frequency. In some embodiments, a microprocessor performs higher-order spectrum analysis on the compensated multi-channel vibration signals to extract the power ratio and phase coupling degree of the higher-order harmonic components as the harmonic distortion fingerprint.

[0142] After obtaining the harmonic distortion fingerprint, the microprocessor inputs it, along with the resonant frequency, wave velocity characteristics, and damping characteristics, into the density recognition matrix. At this point, the density recognition matrix uses the vector composed of the resonant frequency, wave velocity characteristics, damping characteristics, and harmonic distortion fingerprint as its input vector. By calculating the Euclidean distance between the input vector and the feature vectors of each template within the matrix, the material parameters are determined.

[0143] This specification's embodiments acquire multiple vibration signals using a sensing array and analyze their spatial phase differences. This allows for the identification of the speaker's geometric boundary constraints on the load-bearing surface, thereby spatially compensating for the multiple vibration signals and eliminating vibration response artifacts caused by the speaker's proximity to table legs, table edges, etc. Simultaneously, by introducing harmonic distortion fingerprints as the fourth input dimension of the density recognition matrix, materials with similar resonant frequencies, wave velocity characteristics, and damping properties but different nonlinear elastic properties (such as solid wood and composite boards) can be effectively distinguished. This solution significantly improves the accuracy and robustness of material parameter identification, ensuring that the system can match optimal equalizer parameters in different placement positions and scenarios with highly similar materials.

[0144] This specification's embodiments acquire multiple vibration signals using a sensing array and analyze their spatial phase differences. This allows for the identification of the speaker's geometric boundary constraints on the load-bearing surface, thereby spatially compensating for the multiple vibration signals and eliminating vibration response artifacts caused by the speaker's proximity to table legs, table edges, etc. Simultaneously, by introducing harmonic distortion fingerprints as the fourth input dimension of the density recognition matrix, materials with similar resonant frequencies, wave velocity characteristics, and damping properties but different nonlinear elastic properties (such as solid wood and composite boards) can be effectively distinguished. This solution significantly improves the accuracy and robustness of material parameter identification, ensuring that the system can match optimal equalizer parameters in different placement positions and scenarios with highly similar materials.

[0145] Some embodiments of this specification also provide a speaker parameter adjustment device, which includes: at least one storage medium for storing computer instructions; and at least one processor for executing the computer instructions to implement the speaker parameter adjustment method of any of the above embodiments.

[0146] Some embodiments of this specification also provide a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the speaker parameter adjustment method of any of the above embodiments.

[0147] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0148] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0149] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0150] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0151] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A speaker parameter adjustment system, characterized in that, Includes a speaker, microphone, pressure sensor, and microprocessor, wherein the microprocessor is configured to: When the ambient sound pressure level meets the preset sound pressure condition, the speaker is controlled to emit an excitation signal toward the bearing surface; The microphone is controlled to collect sound wave signals, and the pressure sensor is controlled to collect vibration signals from the bearing surface; Extract the first feature value of the acoustic signal and the second feature value of the vibration signal, and determine the material parameters of the bearing surface based on the first feature value and the second feature value; The equalizer parameters are determined based on the material parameters, and the speaker is controlled to execute the equalizer parameters.

2. The system according to claim 1, characterized in that, The pressure sensor includes a first sensing unit disposed at the bottom of the speaker and a second sensing unit disposed inside the speaker; the microprocessor is further configured to: The first sensing unit is controlled to acquire composite signals, and the second sensing unit is controlled to acquire ambient noise and system bias signals. An AC coupling filter circuit is used to filter out the DC voltage signal in the composite signal in order to extract the AC voltage signal representing the vibration of the bearing surface. Based on the environmental noise and the system bias signal, the AC voltage signal is compensated, the compensated AC voltage signal is amplified using a gain amplifier circuit, and the amplified AC voltage signal is used as the vibration signal.

3. The system according to claim 1, characterized in that, The system also includes an accelerometer sensor disposed inside the speaker, and the microprocessor is further configured to: before extracting the second feature value. The accelerometer is controlled to collect the structural vibration characteristics of the speaker. Based on the structural vibration characteristics, the self-vibration interference component of the speaker is filtered out from the vibration signal; The waveform of the vibration signal after filtering out the self-vibration interference component is restored to obtain a pure vibration signal.

4. The system according to claim 1, characterized in that, The first feature value includes the arrival time of the acoustic signal, and the second feature value includes the arrival time and resonant frequency of the vibration signal; the microprocessor is further configured to: The arrival time difference between the acoustic signal and the vibration signal is obtained to determine the wave velocity characteristics; The damping characteristics are determined by the attenuation envelope of the vibration signal; The material parameters are determined by inputting the resonant frequency, wave velocity characteristics, and damping characteristics into the density identification matrix.

5. A method for adjusting speaker parameters, characterized in that, The method is executed by a microprocessor in a speaker parameter adjustment system, the system further including a speaker, a microphone, and a pressure sensor, and the method includes: When the ambient sound pressure level meets the preset sound pressure condition, the speaker is controlled to emit an excitation signal toward the bearing surface; The microphone is controlled to collect sound wave signals, and the pressure sensor is controlled to collect vibration signals from the bearing surface; Extract the first feature value of the acoustic signal and the second feature value of the vibration signal, and determine the material parameters of the bearing surface based on the first feature value and the second feature value; The equalizer parameters are determined based on the material parameters, and the speaker is controlled to execute the equalizer parameters.

6. The method according to claim 5, characterized in that, The pressure sensor includes a first sensing unit disposed at the bottom of the speaker enclosure and a second sensing unit disposed inside the speaker enclosure; controlling the pressure sensor to collect vibration signals from the bearing surface includes: The first sensing unit is controlled to acquire composite signals, and the second sensing unit is controlled to acquire ambient noise and system bias signals. An AC coupling filter circuit is used to filter out the DC voltage signal in the composite signal in order to extract the AC voltage signal representing the vibration of the bearing surface. Based on the environmental noise and the system bias signal, the AC voltage signal is compensated, the compensated AC voltage signal is amplified using a gain amplifier circuit, and the amplified AC voltage signal is used as the vibration signal.

7. The method according to claim 5, characterized in that, The system also includes an acceleration sensor disposed inside the speaker, and the method further includes: Before extracting the second feature value: The accelerometer is controlled to collect the structural vibration characteristics of the speaker. Based on the structural vibration characteristics, the self-vibration interference component of the speaker is filtered out from the vibration signal; The waveform of the vibration signal after filtering out the self-vibration interference component is restored to obtain a pure vibration signal.

8. The method according to claim 5, characterized in that, The first characteristic value includes the arrival time of the acoustic signal, and the second characteristic value includes the arrival time and resonant frequency of the vibration signal; determining the material parameters of the bearing surface based on the first characteristic value and the second characteristic value includes: The arrival time difference between the acoustic signal and the vibration signal is obtained to determine the wave velocity characteristics; The damping characteristics are determined by the attenuation envelope of the vibration signal; The material parameters are determined by inputting the resonant frequency, wave velocity characteristics, and damping characteristics into the density identification matrix.

9. A speaker parameter adjustment device, characterized in that, The device includes: At least one storage medium that stores computer instructions; At least one processor executes the computer instructions to implement the speaker parameter adjustment method of claim 5.

10. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions. When the computer reads the computer instructions from the storage medium, the computer executes the speaker parameter adjustment method as described in claim 5.

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