A multi-zone speaker control system

Abstract

The present invention relates to the technical field of audio processing, and particularly relates to a multi-zone loudspeaker control system, which includes an audio input processing module, a room geometry information acquisition module, an acoustic model construction module, a sound effect optimization module, and a zone control module; wherein: the audio input processing module: is used for receiving external audio signals; the room geometry information acquisition module: is used for collecting the geometric structure data and material property information of the room in real time; the acoustic model construction module: is used for constructing a three-dimensional acoustic model of the room; the sound effect optimization module: is used for optimizing audio signals; the zone control module: distributes the optimized audio signals to different loudspeaker zones. In the present invention, by combining deep learning algorithms and real-time environmental data, the audio signals are intelligently optimized to precisely adapt to the acoustic characteristics of the room. At the same time, audio synchronization of multi-zone loudspeakers is achieved through a low-latency communication protocol, thereby significantly improving the sound quality and the user's auditory experience.

Description

Technical Field

[0001] The present invention relates to the technical field of audio processing, and particularly to a multi-zone speaker control system. Background Art

[0002] With the continuous pursuit of audio effects in the fields of home entertainment, audio systems, and commercial performances, more and more application scenarios require speaker systems that can provide precise, balanced, and high-quality audio output; in these application scenarios, the geometric shape of the room, building materials, and environmental conditions have a significant impact on the propagation characteristics of audio signals; in order to achieve the best audio effects, traditional audio systems often require manual adjustment or rely on simple sound enhancement technologies, and cannot effectively solve the impact brought by the room acoustic characteristics; for example, the acoustic characteristics of the room (such as reflection, refraction, sound wave propagation path, etc.) will cause sound distortion or excessive noise in certain areas, while in some areas, it may be unclear due to the attenuation of sound waves, which makes the traditional audio system unable to make full use of the acoustic characteristics of the space, thereby affecting the overall audio effect.

[0003] Although existing multi-zone speaker control systems can perform audio output for different zones, they usually lack intelligent adaptation and optimization of room acoustic characteristics; most systems in the prior art adopt fixed sound processing algorithms or simplified room models, and cannot analyze and adjust the acoustic characteristics in the room in real time, resulting in inaccurate audio signals of the zone speakers and unable to effectively achieve synchronization between the zone speakers; in addition, traditional sound optimization technologies mostly rely on manual adjustment, lack automation and intelligent processing capabilities, and fail to fully solve the sound adjustment problems under different environmental conditions. Summary of the Invention

[0004] Based on the above purposes, the present invention provides a multi-zone speaker control system.

[0005] A multi-zone speaker control system includes an audio input processing module, a room geometric information acquisition module, an acoustic model construction module, a sound effect optimization module, and a zone control module; wherein:

[0006] The audio input processing module: is used to receive an external audio signal and perform digital processing on it;

[0007] The room geometric information acquisition module: is used to collect real-time geometric structure data and material property information of the room through an integrated ultrasonic sensor and an infrared imaging device;

[0008] The acoustic model construction module: based on the data provided by the room geometric information acquisition module, combines deep learning algorithms to construct a three-dimensional acoustic model of the room and generate a corresponding acoustic transfer function for describing the propagation characteristics of audio signals in the room;

[0009] The acoustic model construction module includes a feature extraction unit, a deep learning network unit, an environmental data acquisition unit, and an acoustic transfer function generation unit; among which:

[0010] Feature extraction unit: Based on the room geometry information and material property data, extract the acoustic features of the room, including the sound wave propagation path, reflection, and refraction information;

[0011] Deep learning network unit: Adopt the deep learning algorithm of the recurrent neural network, and based on the acoustic feature data provided by the feature extraction unit, train and construct a three-dimensional acoustic model of the room to simulate the propagation and interaction of sound waves in the room;

[0012] Environmental data acquisition unit: Used to collect real-time environmental data in the room, including temperature and humidity;

[0013] Acoustic transfer function generation unit: According to the three-dimensional acoustic model output by the deep learning network unit, combined with the temperature and humidity data provided by the environmental data acquisition unit, calculate and generate the corresponding acoustic transfer function, which is used to describe the propagation characteristics of the audio signal in the room, including the attenuation, reflection, refraction, and propagation delay of sound waves;

[0014] Sound effect optimization module: Used to optimize the audio signal according to the acoustic transfer function generated by the acoustic model construction module, so that the audio signal adapts to the acoustic characteristics of the room;

[0015] Partition control module: Used to allocate the optimized audio signal output by the sound effect optimization module to different speaker partitions, and achieve audio synchronization between the speakers in each partition through a low-latency communication protocol.

[0016] Optionally, the audio input processing module includes a receiving unit, an analog-to-digital conversion unit, a signal processing unit, and a data interface unit; among which:

[0017] Receiving unit: Used to receive external audio signals through a wired or wireless interface. The wired interface includes an HDMI interface and a fiber optic interface, and the wireless interface includes Bluetooth and Wi-Fi;

[0018] Analog-to-digital conversion unit: Includes an analog-to-digital converter, which is used to convert the external audio signal received by the receiving unit into a digital signal;

[0019] Signal processing unit: Used to perform noise reduction and signal enhancement processing on the digital audio signal converted by the analog-to-digital conversion unit to eliminate noise interference and enhance the clarity of the audio signal;

[0020] Data interface unit: Used to transmit the digital audio signal processed by the signal processing unit to the sound effect optimization module.

[0021] Optionally, the room geometry information acquisition module includes an ultrasonic measurement unit; wherein:

[0022] Ultrasonic measurement unit: It emits and receives ultrasonic signals through multiple integrated ultrasonic sensors to measure the distances and angles between objects in the room in real time, so as to obtain the geometric structure data of the room.

[0023] Optionally, the deep learning network unit includes:

[0024] Data input sub-unit: It is used to receive the room acoustic feature data provided by the feature extraction unit, including the feature data of the sound wave propagation path, reflection, and refraction, and convert it into a time series format suitable for processing by a recurrent neural network. The data conversion includes converting the acoustic feature data into a series of vector representations and arranging them in the order of time series;

[0025] Recurrent neural network sub-unit: It adopts a recurrent neural network of the long short-term memory network type, and by learning the propagation law of sound waves in the room, it identifies and extracts potential time series patterns to simulate the propagation and interaction of sound waves in the room;

[0026] Model training sub-unit: Based on the input acoustic feature data, through the backpropagation algorithm and the gradient descent optimization algorithm, it iteratively updates the weights of the long short-term memory network, gradually optimizing the prediction ability of the network, so as to generate a three-dimensional acoustic model of the room.

[0027] Optionally, the model training sub-unit includes:

[0028] Data initialization: Initialize all the weight parameters and bias terms in the recurrent neural network, and set the initial values to random values or small constant values; at the same time, set the hyperparameters of the learning rate and the number of iterations;

[0029] Forward propagation: According to the input time series data and the current weights and biases of the network, perform forward propagation through the recurrent neural network to obtain the output at each moment and calculate the predicted value and the error;

[0030] Calculate the loss function: According to the predicted output and the actual target value y t , calculate the error at the current moment, that is, the loss function, and the loss function is the mean square error; its calculation formula is: where L t is the loss value at time t, y t is the actual value, is the predicted value;

[0031] Backpropagation and weight update: According to the calculated loss function Lt , the gradient is calculated using the backpropagation algorithm, and the weights and biases are updated through the gradient descent method;

[0032] Optimization process: By gradually updating the weight parameters of the network, the model gradually learns the ability to predict room acoustic characteristics and optimizes its output at each moment;

[0033] Generating a three-dimensional acoustic model: Through the above training process, the final three-dimensional acoustic model M is obtained, and its expression is: M = f(W (T) , b (T) , x t ), where W (T) and b (T) are the weights and biases after training, x t is the input acoustic feature data, and f is the trained neural network model.

[0034] Optionally, the acoustic transfer function generation unit includes:

[0035] An environmental data integration sub-unit: used to receive the real-time temperature and humidity data provided by the environmental data acquisition unit, and integrate the environmental data with the three-dimensional acoustic model data output by the deep learning network unit;

[0036] An acoustic characteristic adjustment sub-unit: Based on the temperature and humidity data in the room, analyze its effects on the sound wave propagation speed, sound wave attenuation, and reflection characteristics, and adjust the relevant parameters in the three-dimensional acoustic model to reflect the actual situation of sound wave propagation under the current environmental conditions;

[0037] A transfer function calculation sub-unit: According to the adjusted three-dimensional acoustic model data, calculate the acoustic transfer functions of each partition speaker to describe the propagation characteristics of audio signals in the room;

[0038] A transfer function output sub-unit: Transmit the calculated acoustic transfer functions to the sound effect optimization module for optimizing the frequency response and volume distribution of audio signals.

[0039] Optionally, the acoustic characteristic adjustment sub-unit includes:

[0040] Data analysis: Based on the real-time temperature and humidity data provided by the environmental data acquisition unit, analyze its effects on the sound wave propagation speed v, sound wave attenuation α, and reflection characteristics; The specific analysis steps include:

[0041] The calculation formula for the sound wave propagation speed v is: where v0 is the standard sound speed and T is the current temperature; The formula for the sound wave propagation speed v shows that the sound speed increases with the increase in temperature;

[0042] The calculation formula for acoustic wave attenuation α is: α = α0(1 + k·H), where α0 is the standard attenuation coefficient, k is the humidity influence coefficient, and H is the current humidity; this formula for acoustic wave attenuation α indicates that an increase in humidity will lead to an increase in acoustic wave attenuation.

[0043] Adjust the reflection coefficient R according to the temperature and humidity data, and its calculation formula is: where R0 is the standard reflection coefficient, T0 and H0 are the reference temperature and humidity respectively, and m and n are the adjustment coefficients for temperature and humidity;

[0044] Parameter adjustment: According to the above calculation results, substitute the calculated sound speed v into the sound speed parameter in the three-dimensional acoustic model to ensure that the sound wave propagation speed is consistent with the actual environment; according to the calculated attenuation coefficient α, modify the acoustic wave attenuation parameter in the model to describe the energy loss of sound waves in the environment; based on the adjusted reflection coefficient R, update the reflection characteristics of the material surface in the model to ensure that the reflection effect of sound waves is consistent with the actual environmental conditions.

[0045] Optionally, the transfer function calculation subunit includes:

[0046] Acoustic parameter extraction: Based on the adjusted three-dimensional acoustic model data, extract the acoustic parameters of the sound wave propagation speed v, acoustic wave attenuation coefficient α, and reflection coefficient R corresponding to each partition speaker;

[0047] Frequency response calculation: For each partition speaker, calculate its frequency response H(f) at different frequencies according to the extracted acoustic parameters, where f represents the frequency;

[0048] Acoustic wave propagation model application: Apply the acoustic wave propagation model, combined with the reflection coefficient R, to adjust the frequency response H(f) to reflect the influence of multiple reflections and refractions of sound waves in the room; the adjusted frequency response formula is: where H′(f) is the adjusted frequency response, and τ r is the propagation delay time of the reflected sound wave;

[0049] Transfer function integration: Integrate the adjusted frequency response H′(f) of each partition speaker into a complete acoustic transfer function H total (f), which is used to describe the propagation characteristics of audio signals in the entire room.

[0050] Optionally, the sound effect optimization module includes an audio signal processing unit, a dynamic range compression unit, and an output signal generation unit; among them:

[0051] Audio signal processing unit: Used to receive the acoustic transfer function H generated by the acoustic model construction module total(f), and process the input audio signal according to the acoustic characteristics of the room; specifically, a band-pass filter is used to filter the audio signal; let the output audio signal after filtering be y(t), and the calculation formula is: y(t) = F -1 (H total (f) · F(x(t))), where x(t) is the input audio signal, F represents the Fourier transform, and F -1 represents the inverse Fourier transform;

[0052] Dynamic range compression unit: used to adjust the dynamic range of the audio signal; by setting the compression threshold T and the compression ratio R, the intensity of the audio signal is dynamically adjusted according to the amplitude of the input audio signal, and the formula is:

[0053]

[0054] where x(t) is the input audio signal, and y * (t) is the compressed audio signal, T is the compression threshold, and R is the compression ratio;

[0055] Output signal generation unit: used to convert the compressed audio signal y * (t) into the format for speaker playback, and output the optimized audio signal s(t) through each partition speaker.

[0056] Optionally, the partition control module includes an audio signal distribution unit and an audio synchronization unit; where:

[0057] Audio signal distribution unit: used to receive the optimized audio signal s(t) output by the sound effect optimization module, and distribute it to different speaker partitions according to the speaker partition settings;

[0058] Audio synchronization unit: used to ensure audio synchronization between each partition speaker through a low-latency communication protocol to avoid time deviation between different speakers;

[0059] The audio synchronization unit includes a clock synchronization subunit and a low-latency communication protocol subunit; where:

[0060] Clock synchronization subunit: used to ensure time synchronization between each speaker partition according to the system global clock;

[0061] Low-latency communication protocol subunit: adopts a low-latency communication protocol, including Wi-Fi, Bluetooth or a wired protocol, to realize real-time transmission of audio signals, and ensures that the moments when each speaker partition receives and plays the audio signal are consistent through the timestamp and synchronization identifier of the data packet.

[0062] Advantages of the present invention:

[0063] The present invention effectively solves the problem that traditional audio systems cannot intelligently adapt to room acoustic characteristics by introducing deep learning algorithms and real-time environmental data acquisition technologies. The system can real-time sense and analyze environmental factors such as the geometric shape, material properties, temperature, and humidity of the room. By constructing an accurate three-dimensional acoustic model and generating the corresponding acoustic transfer function, it realizes the precise optimization of audio signals. This intelligent optimization not only improves the sound quality but also can dynamically adjust the frequency response and volume distribution of audio signals to ensure the best propagation effect of audio signals under different room conditions.

[0064] The present invention realizes the precise synchronization between multi-zone speakers through a low-latency communication protocol, avoiding the time-delay differences of audio signals between different zones. The optimization and synchronous distribution of audio signals can ensure that the speakers in each zone output audio signals with optimal timing and equalization effects, further improving the overall performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only those of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0066] Figure 1 It is a schematic diagram of a multi-zone speaker control system according to an embodiment of the present invention;

[0067] Figure 2 It is a schematic diagram of an acoustic model construction module according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0068] The following will describe the present invention in detail with reference to the drawings and specific embodiments. At the same time, it should be noted here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments. For some well-known technologies, those skilled in the art can also adopt other alternative methods for implementation; moreover, the drawing part is only for more specifically describing the embodiments and is not intended to specifically limit the present invention.

[0069] As Figure 1 - Figure 2 shown, a multi-zone speaker control system includes an audio input processing module, a room geometry information acquisition module, an acoustic model construction module, a sound effect optimization module, and a zone control module; where:

[0070] The audio input processing module: is used to receive external audio signals and perform digital processing on them to provide audio input for the sound effect optimization module;

[0071] Room geometry information acquisition module: It is used to collect the geometric structure data and material property information of the room in real time through the integrated ultrasonic sensors and infrared imaging devices, providing accurate data support for the acoustic modeling of the room;

[0072] Acoustic model construction module: Based on the data provided by the room geometry information acquisition module and combined with deep learning algorithms, it constructs a three-dimensional acoustic model of the room and generates the corresponding Acoustic Transfer Function (RTF) to describe the propagation characteristics of audio signals in the room;

[0073] The acoustic model construction module includes a feature extraction unit, a deep learning network unit, an environmental data acquisition unit, and an acoustic transfer function generation unit; among them:

[0074] Feature extraction unit: Based on the room geometry information and material property data, it extracts the acoustic features of the room, including the sound wave propagation path, reflection and refraction information, and uses the extracted features as input data for further training of the deep learning model;

[0075] Deep learning network unit: Adopting the deep learning algorithm of the recurrent neural network, based on the acoustic feature data provided by the feature extraction unit, it trains and constructs a three-dimensional acoustic model of the room to simulate the propagation and interaction of sound waves in the room;

[0076] Environmental data acquisition unit: It is used to collect the real-time environmental data in the room, including temperature and humidity, to compensate for the influence of environmental condition changes on the sound wave propagation characteristics;

[0077] Acoustic transfer function generation unit: According to the three-dimensional acoustic model output by the deep learning network unit and combined with the temperature and humidity data provided by the environmental data acquisition unit, it calculates and generates the corresponding acoustic transfer function to describe the propagation characteristics of audio signals in the room, including the attenuation, reflection, refraction, and propagation delay of sound waves;

[0078] Sound effect optimization module: It is used to optimize the audio signal according to the acoustic transfer function generated by the acoustic model construction module, making the audio signal adapt to the acoustic characteristics of the room to ensure that the sound effects output by the speakers in each zone are clear, balanced, and distortion-free;

[0079] Zone control module: It is used to distribute the optimized audio signal output by the sound effect optimization module to different speaker zones and achieve audio synchronization between the speakers in each zone through a low-latency communication protocol to ensure the consistency and coordination of multi-zone audio output.

[0080] The audio input processing module includes a receiving unit, an analog-to-digital conversion unit, a signal processing unit, and a data interface unit; among them:

[0081] Receiving Unit: It is used to receive external audio signals through wired or wireless interfaces. The wired interfaces include HDMI interface and optical fiber interface, and the wireless interfaces include Bluetooth and Wi-Fi;

[0082] Analog-to-Digital Conversion Unit: It includes an analog-to-digital converter (ADC) and is used to convert the external audio signals received by the receiving unit into digital signals;

[0083] Signal Processing Unit: It is used to perform noise reduction and signal enhancement processing on the digital audio signals converted by the analog-to-digital conversion unit to eliminate noise interference and enhance the clarity of the audio signals;

[0084] Data Interface Unit: It is used to transmit the digital audio signals processed by the signal processing unit to the sound effect optimization module to ensure that the sound effect optimization module receives high-quality audio input. Through the design of the above audio input processing module, the system can efficiently and accurately receive and digitize external audio signals, and through processing steps such as filtering, noise reduction, and signal enhancement, provide high-quality digital audio signals to the sound effect optimization module.

[0085] The room geometry information acquisition module includes an ultrasonic measurement unit; where:

[0086] Ultrasonic Measurement Unit: It emits and receives ultrasonic signals through multiple integrated ultrasonic sensors to measure the distances and angles between various objects in the room in real time, so as to obtain the geometric structure data of the room.

[0087] The deep learning network unit includes:

[0088] Data Input Sub-Unit: It is used to receive the room acoustic feature data provided by the feature extraction unit, including the feature data of sound wave propagation paths, reflections, and refractions, and convert it into a time series format suitable for processing by a recurrent neural network (RNN). The data conversion includes converting the acoustic feature data into a series of vector representations and arranging them in chronological order for input into the recurrent neural network unit for further processing;

[0089] Recurrent Neural Network Sub-Unit: It uses a recurrent neural network of the long short-term memory (LSTM) network type to identify and extract potential time series patterns by learning the propagation laws of sound waves in the room, so as to simulate the propagation and interaction of sound waves in the room;

[0090] The above long short-term memory network specifically processes and learns data through the following steps:

[0091] Input Gate: Receives the current input x t and the hidden state h at the previous moment t-1 , and obtains the candidate value of the input through weighted summation The formula is: Among them, W x and W h are the weight matrices of the input and the previous hidden state, b c is the bias term, tanh is the hyperbolic tangent activation function, is the candidate cell state at the current moment;

[0092] Forget gate: Controls how much of the cell state C t-1 from the previous moment is retained. The formula is: f t = σ(W f x t + U f h t-1 + b f ), where σ is the sigmoid activation function, W f and U f are the weight matrices of the forget gate, b f is the bias term, and f t is the output of the forget gate, representing the part to be forgotten;

[0093] Output gate: Determines the output h t at the current moment. By combining the cell state and the hidden state at the current moment, the formula is: h t = o t ·tanh(C t ), where o t is the control signal of the output gate, C t is the current cell state, and tanh is the activation function;

[0094] Cell state update: Combines the input gate and the forget gate to update the cell state C t , and the formula is: where i t is the output of the input gate, C t-1 is the cell state of the previous moment, is the candidate cell state at the current moment; By using the trained LSTM network to identify the time series pattern of sound wave propagation in the room, including characteristics such as sound wave reflection, refraction, and propagation delay.

[0095] Model training subunit: Based on the input acoustic feature data, through the backpropagation algorithm and the gradient descent optimization algorithm, iteratively update the weights of the long short-term memory network, gradually optimize the prediction ability of the network, and thus generate a three-dimensional acoustic model of the room; By using the LSTM network to perform deep learning on the room acoustic features, the time series propagation law of sound waves in a complex room can be effectively captured; The LSTM network can identify long-term dependencies in the acoustic feature data to ensure the accurate construction of the three-dimensional acoustic model; This process greatly improves the accuracy of the room acoustic model and provides more reliable data support for the subsequent generation of the acoustic transfer function.

[0096] The model training subunit includes:

[0097] Data initialization: Initialize all weight parameters and bias terms in the recurrent neural network, and set the initial values to random values or small constant values; At the same time, set the hyperparameters of the learning rate and the number of iterations;

[0098] Forward propagation: According to the input time series data and the current network weights and biases, perform forward propagation through the recurrent neural network to obtain the output at each moment and calculate the predicted value and the error;

[0099] Calculate the loss function: According to the predicted output and the actual target value y t , calculate the error at the current moment, that is, the loss function, and the loss function is the mean squared error (MSE); Its calculation formula is: where, L t is the loss value at time t, y t is the actual value, is the predicted value;

[0100] Backpropagation and weight update: According to the calculated loss function L t , use the backpropagation algorithm to calculate the gradient and update the weights and biases through the gradient descent method; Specifically, first calculate the partial derivatives of the loss function with respect to each weight and bias, and the update formula is: where, η is the learning rate, T is the number of iterations, W (n) and b (n) are the weights and biases in the nth iteration respectively, and are the gradients of the weights and biases respectively; Then update the weights through multiple iterations until the loss function reaches the minimum value or meets the set stopping conditions;

[0101] Optimization process: By gradually updating the weight parameters of the network, the model gradually learns the ability to predict room acoustic characteristics and optimizes its output at each moment; the training process continuously adjusts the weights through backpropagation and gradient descent algorithms, ultimately achieving the goal of optimizing the room acoustic model;

[0102] Generating a three-dimensional acoustic model: Through the above training process, the finally obtained three-dimensional acoustic model M has the expression: M = f(W (T) , b (T) , x t ), where W (T) and b (T) are the weights and biases after the training ends, x t is the input acoustic feature data, and f is the trained neural network model; through the steps of the above model training unit, the system can gradually optimize the weights in the recurrent neural network through the backpropagation algorithm and the gradient descent method, enhancing the network's learning ability for room acoustic characteristics; the minimization of the loss function ensures the accuracy of the network, and finally the three-dimensional acoustic model obtained through iteration can efficiently and accurately describe the propagation characteristics of sound waves in the room, providing accurate data support for subsequent sound effect optimization and speaker zoning control.

[0103] The acoustic transfer function generation unit includes:

[0104] Environmental data integration sub-unit: Used to receive the real-time temperature and humidity data provided by the environmental data acquisition unit and integrate this environmental data with the three-dimensional acoustic model data output by the deep learning network unit;

[0105] Acoustic characteristic adjustment sub-unit: Based on the temperature and humidity data in the room, analyze its effects on the sound wave propagation speed, sound wave attenuation, and reflection characteristics, and adjust the relevant parameters in the three-dimensional acoustic model to reflect the actual situation of sound wave propagation under the current environmental conditions;

[0106] Transfer function calculation sub-unit: According to the adjusted three-dimensional acoustic model data, calculate the acoustic transfer functions of each partition speaker to describe the propagation characteristics of audio signals in the room;

[0107] Transfer function output sub-unit: Transmit the calculated acoustic transfer functions to the sound effect optimization module for optimizing the frequency response and volume distribution of audio signals; by introducing the environmental data integration sub-unit and the acoustic characteristic adjustment sub-unit in the acoustic transfer function generation unit, the system can dynamically respond to the real-time temperature and humidity changes in the room and accurately adjust the parameters of the three-dimensional acoustic model; this design ensures that the acoustic transfer function can accurately reflect the sound wave propagation characteristics under the current environmental conditions, thereby optimizing the clarity and balance of the sound effect output.

[0108] The acoustic characteristic adjustment sub-unit includes:

[0109] Data analysis: Based on the real-time temperature and humidity data provided by the environmental data acquisition unit, analyze their effects on the sound wave propagation speed v, sound wave attenuation α, and reflection characteristics; the specific analysis steps include:

[0110] The calculation formula for the sound wave propagation speed v is: Among them, v0 is the standard sound speed (about 343 m / s), and T is the current temperature (in degrees Celsius); the formula for the sound wave propagation speed v shows that the sound speed increases with the increase in temperature;

[0111] The calculation formula for the sound wave attenuation α is: α = α0(1 + k·H), where α0 is the standard attenuation coefficient, k is the humidity influence coefficient, and H is the current humidity (in percentage); this formula for the sound wave attenuation α indicates that an increase in humidity will lead to an increase in sound wave attenuation;

[0112] Adjust the reflection coefficient R according to the temperature and humidity data, and its calculation formula is: Among them, R0 is the standard reflection coefficient, T0 and H0 are the reference temperature and humidity respectively, m and n are the adjustment coefficients for temperature and humidity, and this formula is used to reflect the influence of environmental changes on the reflection characteristics of materials;

[0113] Parameter adjustment: According to the above calculation results, substitute the calculated sound speed v into the sound speed parameter in the three-dimensional acoustic model to ensure that the sound wave propagation speed is consistent with the actual environment; according to the calculated attenuation coefficient α, modify the sound wave attenuation parameter in the model to describe the energy loss of sound waves in the environment; based on the adjusted reflection coefficient R, update the reflection characteristics of the material surface in the model to ensure that the reflection effect of sound waves is consistent with the actual environmental conditions; through the design of the above acoustic characteristic adjustment subunit, the system can accurately analyze and adjust the sound wave propagation speed, attenuation, and reflection characteristics based on the real-time collected temperature and humidity data.

[0114] The transfer function calculation subunit includes:

[0115] Acoustic parameter extraction: Based on the adjusted three-dimensional acoustic model data, extract the acoustic parameters of the sound wave propagation speed v, sound wave attenuation coefficient α, and reflection coefficient R corresponding to each partition speaker;

[0116] Frequency response calculation: For each partition speaker, calculate its frequency response H(f) at different frequencies based on the extracted acoustic parameters, where f represents the frequency; the calculation formula for the frequency response is: Among them, Y(f) is the frequency domain representation of the speaker output signal, X(f) is the frequency domain representation of the input signal, A(f) is the gain coefficient, τ is the propagation delay time, α(f) is the frequency-dependent attenuation coefficient, and j is the imaginary unit;

[0117] Application of Sound Wave Propagation Model: Apply the sound wave propagation model and combine with the reflection coefficient R to adjust the frequency response H(f) to reflect the influence of multiple reflections and refractions of sound waves in the room; the adjusted frequency response formula is: where H′(f) is the adjusted frequency response, and τ r is the propagation delay time of the reflected sound wave;

[0118] Transfer Function Integration: Integrate the adjusted frequency response H′(f) of each partition speaker into a complete acoustic transfer function H total (f) to describe the propagation characteristics of the audio signal in the entire room; the integration formula is: where N is the total number of partition speakers, and H i ′(f) is the adjusted frequency response of the i-th partition speaker; through the design of the transfer function calculation subunit, the system can accurately calculate the acoustic transfer function of each partition speaker based on the adjusted three-dimensional acoustic model data and real-time environment data; by extracting key acoustic parameters, calculating the frequency response, applying the sound wave propagation model, and integrating the transfer function, it is ensured that the propagation characteristics of the audio signal in the room are accurately described.

[0119] The sound effect optimization module includes an audio signal processing unit, a dynamic range compression unit, and an output signal generation unit; among them:

[0120] Audio Signal Processing Unit: Used to receive the acoustic transfer function H total (f) generated by the acoustic model construction module, and process the input audio signal according to the acoustic characteristics of the room; specifically, use a band-pass filter to filter the audio signal to enhance or suppress the audio components in the specified frequency band to adapt to the frequency characteristics in the room; let the output audio signal after filtering be y(t), and the calculation formula is: y(t) = F -1 (H total (f) · F(x(t))), where x(t) is the input audio signal, F represents the Fourier transform, and F -1 represents the inverse Fourier transform;

[0121] Dynamic Range Compression Unit: Used to adjust the dynamic range of the audio signal to ensure that the audio signal will not be over-amplified or compressed when played in the room, and adapt to the reflection characteristics of the room; by setting the compression threshold T and the compression ratio R, dynamically adjust the intensity of the audio signal according to the amplitude of the input audio signal, and the formula is:

[0122]

[0123] where x(t) is the input audio signal, and y *(t) is the compressed audio signal, T is the compression threshold, and R is the compression ratio; through the design of the sound effect optimization module, it is possible to precisely adjust the audio signal based on the acoustic transfer function generated by the acoustic model construction module to adapt it to the acoustic characteristics of the room;

[0124] Output signal generation unit: used to convert the compressed audio signal y * (t) into the format for speaker playback and output the optimized audio signal s(t) through each partition speaker; through the design of the sound effect optimization module, the system can accurately adjust the frequency response and dynamic range of the audio signal based on the acoustic transfer function generated by the acoustic model construction module to ensure that the audio signal adapts to the acoustic characteristics of the room.

[0125] The partition control module includes an audio signal distribution unit and an audio synchronization unit; where:

[0126] Audio signal distribution unit: used to receive the optimized audio signal s(t) output by the sound effect optimization module and distribute it to different speaker partitions according to the speaker partition settings;

[0127] Audio synchronization unit: used to ensure audio synchronization between each partition speaker through a low-latency communication protocol to avoid time deviation between different speakers;

[0128] The audio synchronization unit includes a clock synchronization subunit and a low-latency communication protocol subunit; where:

[0129] Clock synchronization subunit: used to ensure time synchronization between each speaker partition according to the system global clock;

[0130] Low-latency communication protocol subunit: adopts a low-latency communication protocol, including Wi-Fi, Bluetooth or a wired protocol, to achieve real-time transmission of audio signals, and through the timestamp and synchronization identifier of data packets, ensures that the moments when each speaker partition receives and plays the audio signal are consistent, thereby eliminating audio asynchronization caused by communication latency; through the design of the partition control module, the system can efficiently and accurately distribute the optimized audio signal to different speaker partitions and ensure the audio synchronization between each partition speaker.

[0131] This invention covers any substitutions, modifications, equivalent methods, and solutions made within the essence and scope of this invention. To enable the public to have a thorough understanding of this invention, specific details are elaborated in the following preferred embodiments of this invention, but those skilled in the art can fully understand this invention without the description of these details. Additionally, to avoid unnecessary confusion to the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0132] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A multi-zone speaker control system, characterized in that, It includes an audio input processing module, a room geometry information acquisition module, an acoustic model construction module, a sound effect optimization module, and a zoning control module; among which: The audio input processing module: is used to receive external audio signals and perform digital processing on them; The room geometry information acquisition module: is used to collect the geometric structure data and material property information of the room in real time through the integrated ultrasonic sensor and the integrated infrared imaging device; The acoustic model construction module: based on the data provided by the room geometry information acquisition module, combines deep learning algorithms to construct a three-dimensional acoustic model of the room and generate the corresponding acoustic transfer function to describe the propagation characteristics of audio signals in the room; The acoustic model construction module includes a feature extraction unit, a deep learning network unit, an environmental data acquisition unit, and an acoustic transfer function generation unit; among which: The feature extraction unit: based on the room geometry information and material property data, extracts the acoustic features of the room, including the sound wave propagation path, reflection, and refraction information; The deep learning network unit: adopts the deep learning algorithm of the recurrent neural network, based on the acoustic feature data provided by the feature extraction unit, trains and constructs a three-dimensional acoustic model of the room to simulate the propagation and interaction of sound waves in the room; The environmental data acquisition unit: is used to collect the real-time environmental data in the room, including temperature and humidity; The acoustic transfer function generation unit: according to the three-dimensional acoustic model output by the deep learning network unit, combines the temperature and humidity data provided by the environmental data acquisition unit, calculates and generates the corresponding acoustic transfer function to describe the propagation characteristics of audio signals in the room, including the attenuation, reflection, refraction, and propagation delay of sound waves; The acoustic transfer function generation unit includes: The environmental data integration sub-unit: is used to receive the real-time temperature and humidity data provided by the environmental data acquisition unit and integrate the environmental data with the three-dimensional acoustic model data output by the deep learning network unit; The acoustic characteristic adjustment sub-unit: based on the temperature and humidity data in the room, analyzes its influence on the sound wave propagation speed, sound wave attenuation, and reflection characteristics, and adjusts the relevant parameters in the three-dimensional acoustic model to reflect the actual situation of sound wave propagation under the current environmental conditions; The transfer function calculation sub-unit: according to the adjusted three-dimensional acoustic model data, calculates the acoustic transfer function of each partition speaker to describe the propagation characteristics of audio signals in the room; The transfer function output sub-unit: transmits the calculated acoustic transfer function to the sound effect optimization module for optimizing the frequency response and volume distribution of audio signals; The acoustic characteristic adjustment sub-unit includes: Data analysis: based on the real-time temperature and humidity data provided by the environmental data acquisition unit, analyzes its influence on the sound wave propagation speed v, sound wave attenuation α, and reflection characteristics; the specific analysis steps include: The calculation formula for the sound wave propagation speed v is as follows: where v0 is the standard sound speed and T is the current temperature; The calculation formula of the sound wave attenuation α is: α = α0(1 + k·H), where α0 is the standard attenuation coefficient, k is the humidity influence coefficient, and H is the current humidity; Adjust the reflection coefficient R according to the temperature and humidity data, and its calculation formula is: where R0 is the standard reflection coefficient, T0 and H0 are the reference temperature and humidity respectively, and m and n are the adjustment coefficients of temperature and humidity; Parameter adjustment: According to the calculation result of the acoustic wave propagation speed v, substitute the calculated sound speed v into the sound speed parameter in the three-dimensional acoustic model to ensure that the acoustic wave propagation speed is consistent with the actual environment; according to the calculated attenuation coefficient α, modify the acoustic wave attenuation parameter in the model to describe the energy loss of the acoustic wave in the environment; based on the adjusted reflection coefficient R, update the reflection characteristics of the material surface in the model to ensure that the reflection effect of the acoustic wave is consistent with the actual environmental conditions; The transfer function calculation subunit includes: Acoustic parameter extraction: Based on the adjusted three-dimensional acoustic model data, extract the acoustic parameters of the sound wave propagation speed v, the sound wave attenuation coefficient α, and the reflection coefficient R corresponding to each partition speaker; Frequency response calculation: For each partition speaker, calculate its frequency response H(f) at different frequencies according to the extracted acoustic parameters, where f represents the frequency; Application of acoustic wave propagation model: Apply the acoustic wave propagation model, combined with the reflection coefficient R, to adjust the frequency response H(f) to reflect the effects of multiple reflections and refractions of acoustic waves in a room; the adjusted frequency response formula is: where H′(f) is the adjusted frequency response, τ r is the propagation delay time of the reflected acoustic wave, and j is the imaginary unit; Transfer function integration: Integrate the adjusted frequency response H′(f) of each partitioned loudspeaker into the complete acoustic transfer function H total (f) to describe the propagation characteristics of the audio signal throughout the room; Sound effect optimization module: Used to optimize the audio signal according to the acoustic transfer function generated by the acoustic model construction module, so that the audio signal adapts to the acoustic characteristics of the room; Partition control module: Used to distribute the optimized audio signal output by the sound effect optimization module to different speaker partitions and achieve audio synchronization between the speakers in each partition through a low-latency communication protocol.

2. The multi-zone speaker control system according to claim 1, wherein The audio input processing module includes a receiving unit, an analog-to-digital conversion unit, a signal processing unit, and a data interface unit; among them: Receiving unit: Used to receive external audio signals through a wired or wireless interface. The wired interface includes HDMI interface and optical fiber interface, and the wireless interface includes Bluetooth and Wi-Fi; Analog-to-digital conversion unit: Includes an analog-to-digital converter for converting the external audio signal received by the receiving unit into a digital signal; Signal processing unit: Used to perform noise reduction and signal enhancement processing on the digital audio signal converted by the analog-to-digital conversion unit to eliminate noise interference and enhance the clarity of the audio signal; Data interface unit: Used to transmit the digital audio signal processed by the signal processing unit to the sound effect optimization module.

3. The multi-zone speaker control system according to claim 1, wherein The sound effect optimization module includes an audio signal processing unit, a dynamic range compression unit, and an output signal generation unit; among them: Audio signal processing unit: used to receive the acoustic transfer function H generated by the acoustic model construction module total (f), and process the input audio signal according to the acoustic characteristics of the room; specifically, a band-pass filter is used to filter the audio signal; let the output audio signal after filtering be y(t), and the calculation formula is: y(t) = F -1 (H total (f) · F(x(t))), where x(t) is the input audio signal, F represents the Fourier transform, and F -1 represents the inverse Fourier transform; Dynamic range compression unit: Used to adjust the dynamic range of the audio signal; by setting a compression threshold T and a compression ratio R, dynamically adjust the intensity of the audio signal according to the amplitude of the input audio signal. The formula is: Among them, x(t) is the input audio signal, and y * (t) is the compressed audio signal, T is the compression threshold, and R is the compression ratio; Output signal generation unit: used to convert the compressed audio signal y * (t) into the format for speaker playback and output the optimized audio signal s(t) through the speakers of each partition.

4. A multi-zone speaker control system according to claim 3, characterized in that, The partition control module includes an audio signal distribution unit and an audio synchronization unit; among them: Audio signal distribution unit: Used to receive the optimized audio signal s(t) output by the sound effect optimization module and distribute it to different speaker partitions according to the speaker partition settings; Audio synchronization unit: Used to ensure audio synchronization between the speakers in each partition through a low-latency communication protocol to avoid time deviation between different speakers; The audio synchronization unit includes a clock synchronization subunit and a low-latency communication protocol subunit; among them: Clock synchronization subunit: Used to ensure time synchronization between each speaker partition according to the system global clock; Low-latency communication protocol sub-unit: Adopt a low-latency communication protocol to achieve real-time transmission of audio signals, and ensure that each speaker zone receives and plays the audio signals at the same time through the timestamps and synchronization identifiers of data packets.

Images