Intelligent house cinema system based on voice control

Abstract

The invention discloses an intelligent house cinema system based on voice control, and relates to the technical field of intelligent house cinema. Comprising a voice receiving and preprocessing module, a noise filtering and signal processing module, a context understanding and instruction analysis module, an equipment control and execution module, an equipment state monitoring and feedback module and an error rollback and safety feedback module, and the received voice instruction is preprocessed. The voice recognition accuracy is improved through the multi-microphone array and the deep learning noise filtering algorithm, and the instruction can be effectively recognized in a noisy environment. The context understanding and instruction analysis module enhances the intelligence of the system, and can process multiple instructions and avoid conflicts. And a real-time feedback and error rollback mechanism improves the safety and stability of the system, ensures that the equipment can be quickly recovered when misoperation or abnormity occurs, and improves the user experience and the system reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of smart house theaters, and in particular to a smart house theater system based on voice control. Background Art

[0002] A voice-controlled smart home theater combines voice recognition technology with smart home systems, enabling users to control their home's audio, TV, projector, lights, curtains, and other devices solely through voice commands, delivering a cinematic audiovisual experience. This system typically involves connecting a voice assistant (such as Alexa or Google Assistant) to home automation devices. Users can use voice commands to adjust volume, select programs, play movies, adjust screen brightness, and adjust the indoor environment, creating a more convenient, comfortable, and efficient home entertainment experience. Voice control technology makes home theater operation more intuitive and hands-free, significantly enhancing user interactivity and entertainment enjoyment.

[0003] The existing technology has the following deficiencies: In smart home theaters based on voice control, misidentification and misoperation of the voice recognition system is a problem that may lead to serious consequences. In a complex home theater environment, background noise, multiple people issuing voice commands at the same time, or mutual interference between devices may cause the system to misidentify commands. For example, the system may mistakenly recognize "turn off the TV" as "turn on the lights" or mistakenly recognize the volume adjustment command as a command to play other content, causing confusion in device operation and even causing damage to the equipment or safety hazards. Long-term occurrence of misoperation may not only affect the user experience, but may also cause electrical equipment to be misactivated or electrical failures, and in extreme cases even cause safety accidents such as fires. Therefore, the system needs to strengthen the optimization of noise filtering, context understanding, and multi-device collaborative control to ensure efficient and accurate voice recognition and safety.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a voice-controlled smart home theater system. This system utilizes a multi-microphone array and deep learning noise filtering algorithms to improve speech recognition accuracy and effectively identify commands in noisy environments. A contextual understanding and command analysis module enhances the system's intelligence, enabling it to process multiple commands and avoid conflicts. Real-time feedback and error rollback mechanisms improve system security and stability, ensuring rapid device recovery in the event of misoperation or anomalies, reducing risk, improving user experience, and enhancing system reliability, thereby addressing the aforementioned background issues.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a voice-controlled smart home theater system, comprising a voice reception and preprocessing module, a noise filtering and signal processing module, a context understanding and command analysis module, a device control and execution module, a device status monitoring and feedback module, and an error rollback and safety feedback module:

[0007] The voice receiving and preprocessing module receives the user's voice command through the voice input module and preprocesses the received voice command;

[0008] The noise filtering and signal processing module uses a noise filtering algorithm to process the received voice signal, eliminating environmental noise and background interference to ensure the accuracy of voice commands;

[0009] Context understanding and instruction analysis module: The context understanding module analyzes the context of user instructions, determines the true intention of the instructions, identifies whether multiple instructions are issued at the same time, and takes appropriate measures;

[0010] The device control and execution module transmits the processed and confirmed voice commands to the smart home device control module to perform the corresponding device control operations;

[0011] The device status monitoring and feedback module dynamically monitors the device status during the execution process, confirms whether the instructions have been executed correctly through the feedback mechanism, and makes corrections as needed;

[0012] The error rollback and safety feedback module will immediately roll back and feedback the error information to the user if an erroneous operation or device abnormality occurs during the execution process to ensure the safe operation of the equipment.

[0013] Preferably, the voice input module receives the user's voice commands through a high-precision microphone array and performs signal enhancement based on the strength and directionality of the voice signal, thereby improving the accuracy of voice recognition. The specific steps are as follows:

[0014] Activate the microphone array to determine the source direction of the user's voice signal;

[0015] Adjust the receiving sensitivity of the microphone array according to the user's voice signal strength and source direction;

[0016] Preprocessing the received signal;

[0017] The processed voice signal is passed to the voice recognition module for further instruction decoding and analysis.

[0018] Preferably, the noise filtering algorithm includes an adaptive noise suppression model based on deep learning, which processes the speech signal in real time through a multi-layer neural network. The specific steps are as follows:

[0019] Use a trained deep neural network to analyze the input speech signal and identify the speech and noise components;

[0020] Extract the distinguishing features between noise signal and speech signal based on the frequency and time domain features of speech signal;

[0021] Adaptively suppress the identified noise part and achieve real-time noise removal by dynamically adjusting the filter parameters;

[0022] The optimized voice signal is passed to the voice recognition module for command processing.

[0023] Preferably, the noise filtering algorithm includes a dual filtering method based on spectrum analysis and time-frequency conversion, and the specific steps are as follows:

[0024] Perform short-time Fourier transform on the received speech signal to obtain the time-frequency representation of the signal. The calculation expression is as follows:

[0025]

[0026] , where S(f, t) is the time-frequency representation of the speech signal, x(τ) is the value of the input speech signal at time τ, w(t-τ) is the time window function, and e -j2πfτ is the complex exponential function in Fourier transform, f is frequency, τ is time, j is the imaginary unit, e is the natural base,

[0027] The noise spectrum of the signal is estimated to obtain the noise spectrum. By comparing the background noise and the speech spectrum, the noise components are determined. The spectral threshold method is used to remove the noise components to obtain a clear speech signal. The calculation expression is as follows:

[0028]

[0029] , where S clean (f, t) is the time-frequency representation of the clear speech signal, S(f, t) is the time-frequency representation of the original signal, 1 is the indicator function, S noise (f, t) is the time-frequency representation of the noise signal, γ is the threshold factor between noise and signal;

[0030] The frequency domain signal is converted back to the time domain signal through inverse transformation to obtain the final clear speech signal for voice command recognition.

[0031] Preferably, the context understanding and instruction analysis module uses an algorithm based on semantic analysis to identify the user's true intention. The specific steps are as follows:

[0032] Semantically decompose the received voice commands and identify the core information of verbs and nouns;

[0033] Based on the context analysis algorithm, the instruction is compared with the previous user operation records to determine whether the instruction is ambiguous;

[0034] If there is ambiguity, clarify the user's intention by asking rhetorical questions and prompting them;

[0035] Unambiguous instructions are further processed and converted into specific device control commands.

[0036] Preferably, the context understanding and instruction analysis module performs context recognition and multi-instruction processing on the voice instruction in the following specific steps:

[0037] Assume that the input speech signal is X, the context information is C, and the instruction sequence is I={I i}={I1,I2,……,I n}, where I i is the i-th instruction;

[0038] According to the context information C and the input signal X, the matching degree of each instruction is calculated. The calculation expression is as follows:

[0039]

[0040] , where M(I i , C) is instruction I i The matching degree between the context information C, m is the number of features used to calculate the instruction matching degree, w k is the weight of the kth feature, f k (I i , C, X) is the kth characteristic function, calculate the i-th instruction I i The degree of match between the context information C and the speech signal X;

[0041] By selecting the instruction with the maximum matching degree, the user's intention is determined. If the matching degree M(I max , C)>T, then the instruction is considered valid, where I max is the instruction with the highest matching degree M, and T is the preset threshold;

[0042] If multiple instructions are issued at the same time, the following formula is used to merge the instructions;

[0043]

[0044] , where I final is the instruction that is finally selected, n is the total number of instructions, and argmax represents the operation that selects the maximum value.

[0045] Preferably, the device control and execution module realizes compatibility among multiple devices through an integrated device protocol conversion layer, and the specific steps are as follows:

[0046] Receive the processed voice command and select the corresponding device protocol according to the command type;

[0047] Analyze each device protocol and generate corresponding control instructions;

[0048] Send control instructions to corresponding devices through the smart home control system to perform relevant operations;

[0049] During multi-device operation, coordinate the interactions between devices to avoid conflicts and duplicate instructions.

[0050] Preferably, the device status monitoring and feedback module monitors the device operating status in real time through a sensor network to ensure the accuracy of instruction execution. The specific steps are as follows:

[0051] Install sensors on smart home devices to monitor their operating status;

[0052] Regularly send equipment operation data to the equipment status monitoring module to detect whether the current status of the equipment is consistent with the user's instructions;

[0053] If the device status does not match, the user is notified through a feedback mechanism and provided with correction suggestions;

[0054] Based on user feedback, restore the device status to the expected state.

[0055] Preferably, anomaly detection of device status is performed through a machine learning model, and potential errors in device operation are identified through the model. Device behavior can be modeled based on real-time collected data to detect whether the device status is consistent with expectations. When anomalies are found, the user is promptly notified through a feedback mechanism and corrective measures are taken.

[0056] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0057] The present invention significantly improves the accuracy and robustness of speech recognition by introducing a multi-microphone array and a deep learning noise filtering algorithm. In traditional single-microphone systems, interference from environmental noise (such as TV audio, air conditioning noise, etc.) and voice signals often leads to recognition failure or misrecognition. The present invention captures the directionality of voice signals through a multi-microphone array, which can clearly identify voices from a specific direction and suppress background noise from other directions. Combined with the deep learning noise filtering algorithm, the system can optimize the voice signal in real time, eliminate environmental interference, and convert the originally unclear voice signal into high-quality voice commands. Such a technical solution ensures that the system can efficiently and accurately capture and parse the user's voice commands even in a noisy home theater environment, thereby improving the overall performance of voice recognition and reducing misoperation and user inconvenience.

[0058] The context understanding and instruction analysis module of the present invention enables the system to have a higher intelligent understanding ability. Through the context analysis of the user's voice instructions and the comparison of historical operation records, the system not only simply recognizes each voice instruction, but also makes a more intelligent response based on the current state of the device, the user's operating habits and the logical relationship between the instructions. The system can understand the relationship between multiple instructions and avoid conflicts and misunderstandings through priority processing. For example, when the user issues the instructions of "turn up the volume" and "turn off the TV" at the same time, the system can judge the user's true intention based on the context and give priority to the most urgent instructions. In addition, the system can handle complex instruction interactions in the context, such as multiple instructions issued at the same time, which enhances the multi-tasking capability. This intelligent instruction analysis not only improves the user experience, but also makes the device operation more efficient and accurate, avoiding the situation where the user repeats the operation or the device executes the instruction incorrectly.

[0059] The real-time feedback and error rollback mechanism of the present invention greatly improves the security and stability of the system. In the process of voice control of smart home devices, misoperation or device abnormality may cause device failure, damage, or even cause safety hazards. The present invention dynamically monitors the device status and promptly captures potential abnormalities or misoperations during the device execution process. Once it is detected that the device operation is not as expected, the system can quickly roll back to a safe state to prevent the device from being damaged by incorrect operation. At the same time, the system will also issue warnings and repair suggestions to users in a timely manner through the feedback mechanism to ensure that users can get the most direct help. For example, when the volume is not adjusted as expected, the system can remind the user of the operational error through feedback and help restore it to the correct state. Through this mechanism, the system can quickly recover when problems occur, ensuring the stable operation of the device and the safety of the user, reducing the risks caused by human errors, and increasing the reliability of the system and the trust of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0061] Figure 1 This is a module diagram of the voice-controlled smart house theater system of the present invention. DETAILED DESCRIPTION

[0062] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0063] The present invention provides Figure 1 The voice-controlled smart home theater system shown in the figure includes a voice reception and preprocessing module, a noise filtering and signal processing module, a context understanding and command analysis module, a device control and execution module, a device status monitoring and feedback module, and an error rollback and safety feedback module:

[0064] The voice receiving and preprocessing module receives the user's voice command through the voice input module and preprocesses the received voice command;

[0065] The voice input module receives user voice commands through a high-precision microphone array and enhances the voice signal by combining its strength and directionality to improve the accuracy of voice recognition. The specific steps are as follows:

[0066] Activate the microphone array to determine the source direction of the user's voice signal;

[0067] Adjust the receiving sensitivity of the microphone array according to the user's voice signal strength and source direction;

[0068] Pre-process the received signal, including echo cancellation, spectrum analysis, etc.;

[0069] The processed voice signal is passed to the voice recognition module for further instruction decoding and analysis.

[0070] In this way, the impact of external noise and device interference on voice commands can be reduced, the system's recognition accuracy in noisy environments can be improved, and the user's operating experience can be enhanced.

[0071] The noise filtering and signal processing module uses a noise filtering algorithm to process the received voice signal, eliminating environmental noise and background interference to ensure the accuracy of voice commands;

[0072] The noise filtering algorithm includes an adaptive noise suppression model based on deep learning. This model processes the speech signal in real time through a multi-layer neural network. The specific steps are as follows:

[0073] Use a trained deep neural network to analyze the input speech signal and identify the speech and noise components;

[0074] Extract the distinguishing features between noise signal and speech signal based on the frequency and time domain features of speech signal;

[0075] Adaptively suppress the identified noise part and achieve real-time noise removal by dynamically adjusting the filter parameters;

[0076] The optimized voice signal is passed to the voice recognition module for command processing.

[0077] This noise filtering algorithm improves the accuracy of noise processing through a deep learning model, and can effectively reduce the false recognition rate, especially in high-noise environments.

[0078] The noise filtering algorithm includes a dual filtering method based on spectrum analysis and time-frequency conversion. The specific steps are as follows:

[0079] Perform short-time Fourier transform (STFT) on the received speech signal to obtain the time-frequency representation of the signal. The calculation expression is as follows:

[0080]

[0081] , where S(f, t) is the time-frequency representation of the speech signal, which describes the distribution of the signal in time and frequency through Fourier transform. It is a function of frequency domain representation, which can show the changes of different frequency components of the signal at different times. x(τ) is the value of the input speech signal at time τ, and w(t-τ) is the time window function, which is used to control the localization processing of the signal. e -j2πfτ is the complex exponential function in Fourier transform, f is frequency, τ is time, j is the imaginary unit, e is the natural base,

[0082] The noise spectrum of the signal is estimated to obtain the noise spectrum. By comparing the background noise and the speech spectrum, the noise components are determined. The spectral threshold method is used to remove the noise components to obtain a clear speech signal. The calculation expression is as follows:

[0083]

[0084] , where S clean (f, t) is the time-frequency representation of the clear speech signal, S(f, t) is the time-frequency representation of the original signal, 1 is the indicator function, when S(f, t)>γ·S noise (f, t), the indicator function returns 1, indicating that the frequency component is retained; otherwise, it returns 0, indicating that the frequency component is removed. noise (f, t) is the time-frequency representation of the noise signal, which is used to estimate the intensity of the background noise. γ is the threshold factor between noise and signal, which determines the relative relationship between noise and signal. When the signal strength is higher than the noise strength by γ times the threshold value, the frequency component is retained.

[0085] The frequency domain signal is converted back to the time domain signal through inverse transformation to obtain the final clear speech signal for voice command recognition.

[0086] This method effectively improves the accuracy of speech recognition in noisy environments through dual filtering technology.

[0087] Context understanding and instruction analysis module: The context understanding module analyzes the context of user instructions, determines the true intention of the instructions, identifies whether multiple instructions are issued at the same time, and takes appropriate measures;

[0088] The context understanding and instruction analysis module uses an algorithm based on semantic analysis to identify the user's true intention. The specific steps are as follows:

[0089] Semantically decompose the received voice commands and identify the core information of verbs and nouns;

[0090] Based on the context analysis algorithm, the instruction is compared with the previous user operation records to determine whether the instruction is ambiguous;

[0091] If there is ambiguity, clarify the user's intention by asking rhetorical questions and prompting them;

[0092] Unambiguous instructions are further processed and converted into specific device control commands.

[0093] This method optimizes the instruction recognition process through semantic analysis, can better understand the user's various expressions and contexts, and improves the accuracy and response speed of instructions.

[0094] The context understanding and command analysis module performs context recognition and multi-command processing on voice commands in the following specific steps:

[0095] Assume that the input speech signal is X, the context information is C, and the instruction sequence is I={I i}={I1,I2,……,I n}, where I i is the i-th instruction;

[0096] According to the context information C and the input signal X, the matching degree of each instruction is calculated. The calculation expression is as follows:

[0097]

[0098] , where M(I i , C) is instruction I i The matching degree between the instruction and the context information C reflects the consistency between the instruction and the context. The higher the value, the stronger the matching degree. m is the number of features used to calculate the instruction matching degree, and w k is the weight of the kth feature, f k(I i , C, X) is the kth characteristic function, calculate the i-th instruction I i The degree of match between the context information C and the speech signal X;

[0099] By selecting the instruction with the maximum matching degree, the user's intention is determined. If the matching degree M(I max , C)>T, then the instruction is considered valid, where I max is the instruction with the highest matching degree M, which is also the final selected execution instruction, and T is the preset threshold;

[0100] If multiple instructions are issued at the same time, the following formula is used to merge the instructions;

[0101]

[0102] , where I final is the instruction that is finally selected, n is the total number of instructions, and argmax represents the operation that selects the maximum value.

[0103] This method can efficiently identify multiple instructions issued simultaneously and process them correctly.

[0104] The device control and execution module transmits the processed and confirmed voice commands to the smart home device control module to perform the corresponding device control operations;

[0105] The device control and execution module achieves compatibility between multiple devices through an integrated device protocol conversion layer. The specific steps are as follows:

[0106] Receive the processed voice command and select the corresponding device protocol according to the command type;

[0107] Analyze each device protocol and generate corresponding control instructions;

[0108] Send control instructions to corresponding devices through the smart home control system to perform relevant operations;

[0109] During multi-device operation, coordinate the interactions between devices to avoid conflicts and duplicate instructions.

[0110] This method enables devices from different manufacturers or different types to work together seamlessly in the same system, enhancing the compatibility of the smart home system.

[0111] The device status monitoring and feedback module dynamically monitors the device status during the execution process, confirms whether the instructions have been executed correctly through the feedback mechanism, and makes corrections as needed;

[0112] The device status monitoring and feedback module monitors the device operating status in real time through the sensor network to ensure the accuracy of instruction execution. The specific steps are as follows:

[0113] Install sensors on smart home devices to monitor their operating status (such as on / off status, volume, playback content, etc.);

[0114] Regularly send equipment operation data to the equipment status monitoring module to detect whether the current status of the equipment is consistent with the user's instructions;

[0115] If the device status does not match, the user is notified through a feedback mechanism and provided with correction suggestions;

[0116] Based on user feedback, restore the device status to the expected state.

[0117] This monitoring and feedback mechanism enhances the intelligence and fault tolerance of the system, ensuring that no errors or anomalies occur during the execution of the equipment.

[0118] The error rollback and safety feedback module will immediately roll back and feedback the error information to the user if an erroneous operation or device abnormality occurs during the execution process to ensure the safe operation of the equipment.

[0119] Machine learning models are used to detect anomalies in device status and identify potential errors in device operation. Device behavior can be modeled based on real-time collected data to detect whether the device status is consistent with expectations. When anomalies are found, users are promptly notified through a feedback mechanism and corrective measures are taken.

[0120] This solution enables the system to automatically identify states in device behavior that do not conform to normal patterns, improving the intelligence and adaptability of device operation.

[0121] Implementation 1: In this implementation, to address noise interference in voice control systems, we designed a high-precision multi-microphone array system. This system includes multiple microphones positioned throughout a home theater environment, working collaboratively to capture sound signals from different directions. These microphones use algorithms to locate the sound source and automatically adjust their sensitivity to ensure the clearest and most accurate voice capture. In a home theater environment, background noise is particularly complex, including sound from speakers, the hum of household appliances, and even ambient air flow. This noise can significantly hinder voice recognition systems.

[0122] To overcome this problem, we employed a deep learning-based noise filtering algorithm that can dynamically identify and suppress noise in complex environments. Specifically, speech signals are first received from multiple microphones and synchronously transmitted to the processing module. The processing module uses spectral analysis to separate the speech signal into frequencies and decompose it in the time domain, thereby identifying the noise components within the signal. These noises typically include sources such as air conditioning, electrical appliances, and external traffic, and their spectral and time domain characteristics differ from those of normal speech. By extracting these features, the system can automatically distinguish between speech signals and background noise.

[0123] On this basis, the system employs adaptive noise suppression technology, automatically adjusting the parameters of the noise suppression algorithm based on environmental changes and the dynamics of the user's voice. By continuously learning and optimizing the noise model, the system can more accurately eliminate noise and improve the quality of the voice signal. For example, in particularly noisy environments, the system will increase its suppression of low-frequency noise; while in indoor environments with flowing air, the system will focus on addressing high-frequency noise interference. This allows users to clearly issue voice commands even in high ambient noise, and the system can accurately understand their needs.

[0124] Furthermore, the noise filtering algorithm continuously iteratively optimizes the voice signal to reduce interference in the original signal. Based on the signal's strength and clarity, the system dynamically determines which are user commands and which are irrelevant noise using complex mathematical models and calculation formulas. When a noise signal is detected, the system automatically performs reverse filtering to minimize its intensity, ensuring that the voice signal ultimately delivered to the voice recognition module is pure and high-quality. This intelligent noise filtering significantly improves voice recognition accuracy, especially in complex environments, avoiding the misidentification and missed recognition issues that can occur with traditional single-microphone systems.

[0125] To ensure the system's applicability in diverse home theater environments, we've designed an adaptive learning mechanism. During user use, the system continuously records voice input and environmental changes, gradually optimizing the parameters of the noise filtering algorithm. This allows the system to automatically adjust its recognition strategy for specific home environments, improving its long-term stability and robustness.

[0126] Implementation 2: In this implementation, we further optimize the intelligence of the voice recognition system by incorporating contextual understanding and analysis modules. Traditional voice recognition systems typically match a single user's voice command, failing to understand the true intent behind the command or the contextual connections between multiple commands. To improve the accuracy and flexibility of voice control, this implementation conducts in-depth analysis of the context and semantics of the user's voice, enabling the system to better understand the command and automatically adjust its response based on device status and historical operations.

[0127] First, the system will parse the voice signal to extract the core content of the instruction. In this step, the voice instruction will be disassembled through a series of language processing modules, including natural language processing (NLP), named entity recognition (NER) and semantic analysis. Each voice instruction will be converted into a structured data containing verbs, nouns and indicative information. For example, the instruction "turn up the volume" will be converted into the verb "turn up" and the noun "volume" and the required quantitative information. This process enables the system to better extract the specific content of the instruction from the voice.

[0128] Secondly, the system uses contextual analysis to determine whether the command is consistent with the current device state. For example, if the user issues a "turn up the volume" command, and the volume is already at maximum, the system will determine whether the user needs to adjust other devices or remind the user that the volume has reached maximum based on contextual information and historical records. When the system detects potential conflicts between voice commands (such as issuing "turn on the lights" and "close the curtains" at the same time), the system will first prioritize the most urgent or most relevant command to avoid confusion and errors in device operation.

[0129] To further enhance command accuracy, the system also identifies user idioms based on historical operation records. For example, if a user frequently issues the command "turn up the volume" while watching a movie, the system can infer the user's intention and automatically adjust the volume. This predictive mechanism significantly improves the user experience and avoids the need for explicit voice commands every time.

[0130] When detecting multiple commands being issued simultaneously, the system automatically adjusts the processing order based on priority rules. For example, if a user issues the commands "turn off the lights" and "lower the volume" at the same time, the system automatically determines the user's current needs and prioritizes the most urgent command, ensuring coordination between devices and operational continuity. If there are command conflicts or unreasonable context, the system will prompt the user to reissue the command through a feedback mechanism to prevent device execution errors.

[0131] The introduction of this contextual understanding and dynamic command adjustment system enables voice recognition to go beyond simple command matching and fully understand the intent and context behind the voice, thereby achieving a smarter and more humane control experience.

[0132] Implementation Method 3: The core of this implementation method is a real-time feedback and error rollback mechanism. This design enables the voice control system to continuously monitor the device's status during execution and, if misoperation or anomalies occur, to immediately correct them and ensure stable system operation. In voice-controlled smart home theaters, due to the complex environment and diverse user commands, device misoperation or anomalies may occur for a variety of reasons. Traditional voice control systems are often unable to detect and correct these issues in a timely manner, resulting in device failure to respond correctly and even damage.

[0133] To prevent this from happening, we've designed a mechanism to monitor device status in real time. Whenever a user issues a voice command, the device control module immediately initiates a status monitoring process to continuously track the device's operating status. For example, if a user issues the command "Turn on the TV," the system will check in real time whether the TV has successfully powered on, whether the display is normal, and whether the volume is as expected. If the device detects a discrepancy with expected status during execution, the system will issue a warning to the user through a feedback mechanism. For example, if the TV fails to start, the system will prompt "The TV failed to turn on properly. Please check the power supply or reissue the command."

[0134] In addition, if the system detects an abnormality or erroneous operation of the device (such as the volume not being adjusted as required, or the smart light not working as expected), it will automatically trigger the rollback mechanism. The rollback mechanism will restore the device to its previous safe state to prevent the device from being affected by the erroneous operation. The rollback process is completed automatically without user intervention, and the system will fix any potential error state in a short time. For example, if a volume adjustment command issued by the user is mistakenly executed as turning off the device, the system will provide corrective suggestions in the feedback mechanism and restore the device to its original normal state.

[0135] When device operation cannot be repaired or the system cannot automatically identify a problem, the system notifies the user and provides a detailed error report, advising the user to take necessary actions. For example, the system may prompt the user to recalibrate the device, check device connections, and ensure that device operation is not disrupted. Through this real-time feedback and error rollback mechanism, this implementation can greatly improve system stability and security in complex home theater environments, while also preventing device damage or safety hazards caused by voice misrecognition or device anomalies.

[0136] The present invention significantly improves the accuracy and robustness of speech recognition by introducing a multi-microphone array and a deep learning noise filtering algorithm. In traditional single-microphone systems, interference from environmental noise (such as TV audio, air conditioning noise, etc.) and voice signals often leads to recognition failure or misrecognition. The present invention captures the directionality of voice signals through a multi-microphone array, which can clearly identify voices from a specific direction and suppress background noise from other directions. Combined with the deep learning noise filtering algorithm, the system can optimize the voice signal in real time, eliminate environmental interference, and convert the originally unclear voice signal into high-quality voice commands. Such a technical solution ensures that the system can efficiently and accurately capture and parse the user's voice commands even in a noisy home theater environment, thereby improving the overall performance of voice recognition and reducing misoperation and user inconvenience.

[0137] The context understanding and instruction analysis module of the present invention enables the system to have a higher intelligent understanding ability. Through the context analysis of the user's voice instructions and the comparison of historical operation records, the system not only simply recognizes each voice instruction, but also makes a more intelligent response based on the current state of the device, the user's operating habits and the logical relationship between the instructions. The system can understand the relationship between multiple instructions and avoid conflicts and misunderstandings through priority processing. For example, when the user issues the instructions of "turn up the volume" and "turn off the TV" at the same time, the system can judge the user's true intention based on the context and give priority to the most urgent instructions. In addition, the system can handle complex instruction interactions in the context, such as multiple instructions issued at the same time, which enhances the multi-tasking capability. This intelligent instruction analysis not only improves the user experience, but also makes the device operation more efficient and accurate, avoiding the situation where the user repeats the operation or the device executes the instruction incorrectly.

[0138] The real-time feedback and error rollback mechanism of the present invention greatly improves the security and stability of the system. In the process of voice control of smart home devices, misoperation or device abnormality may cause device failure, damage, or even cause safety hazards. The present invention dynamically monitors the device status and promptly captures potential abnormalities or misoperations during the device execution process. Once it is detected that the device operation is not as expected, the system can quickly roll back to a safe state to prevent the device from being damaged by incorrect operation. At the same time, the system will also issue warnings and repair suggestions to users in a timely manner through the feedback mechanism to ensure that users can get the most direct help. For example, when the volume is not adjusted as expected, the system can remind the user of the operational error through feedback and help restore it to the correct state. Through this mechanism, the system can quickly recover when problems occur, ensuring the stable operation of the device and the safety of the user, reducing the risks caused by human errors, and increasing the reliability of the system and the trust of users.

[0139] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0140] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

[0141] It should be noted that, in this document, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0142] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0143] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0144] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0145] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0146] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0147] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0148] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

Claims

1. The intelligent house theater system based on voice control is characterized by: It includes voice reception and preprocessing module, noise filtering and signal processing module, context understanding and instruction analysis module, device control and execution module, device status monitoring and feedback module, and error rollback and safety feedback module: The voice receiving and preprocessing module receives the user's voice command through the voice input module and preprocesses the received voice command; The noise filtering and signal processing module uses a noise filtering algorithm to process the received voice signal, eliminating environmental noise and background interference to ensure the accuracy of voice commands; Context understanding and instruction analysis module: The context understanding module analyzes the context of user instructions, determines the true intention of the instructions, identifies whether multiple instructions are issued at the same time, and takes appropriate measures; The device control and execution module transmits the processed and confirmed voice commands to the smart home device control module to perform the corresponding device control operations; The device status monitoring and feedback module dynamically monitors the device status during the execution process, confirms whether the instructions have been executed correctly through the feedback mechanism, and makes corrections as needed; The error rollback and safety feedback module will immediately roll back and feedback the error information to the user if an erroneous operation or device abnormality occurs during the execution process to ensure the safe operation of the equipment.

2. The voice-controlled smart house theater system according to claim 1, characterized in that: The voice input module receives user voice commands through a high-precision microphone array and enhances the voice signal by combining its strength and directionality to improve the accuracy of voice recognition. The specific steps are as follows: Activate the microphone array to determine the source direction of the user's voice signal; Adjust the receiving sensitivity of the microphone array according to the user's voice signal strength and source direction; Preprocessing the received signal; The processed voice signal is passed to the voice recognition module for further instruction decoding and analysis.

3. The voice-controlled smart house theater system according to claim 1, characterized in that: The noise filtering algorithm includes an adaptive noise suppression model based on deep learning. This model processes the speech signal in real time through a multi-layer neural network. The specific steps are as follows: Use a trained deep neural network to analyze the input speech signal and identify the speech and noise components; Extract the distinguishing features between noise signal and speech signal based on the frequency and time domain features of speech signal; Adaptively suppress the identified noise part and achieve real-time noise removal by dynamically adjusting the filter parameters; The optimized voice signal is passed to the voice recognition module for command processing.

4. The voice-controlled smart house theater system according to claim 1, characterized in that: The noise filtering algorithm includes a dual filtering method based on spectrum analysis and time-frequency conversion. The specific steps are as follows: Perform short-time Fourier transform on the received speech signal to obtain the time-frequency representation of the signal. The calculation expression is as follows: ， Where S(f, t) is the time-frequency representation of the speech signal, x(τ) is the value of the input speech signal at time τ, w(t-τ) is the time window function, and e -j2πfτ is the complex exponential function in Fourier transform, f is frequency, τ is time, j is the imaginary unit, e is the natural base, The noise spectrum of the signal is estimated to obtain the noise spectrum. By comparing the background noise and the speech spectrum, the noise components are determined. The spectral threshold method is used to remove the noise components to obtain a clear speech signal. The calculation expression is as follows: ， Where S clean (f, t) is the time-frequency representation of the clear speech signal, S(f, t) is the time-frequency representation of the original signal, 1 is the indicator function, S noise (f, t) is the time-frequency representation of the noise signal, γ is the threshold factor between noise and signal; The frequency domain signal is converted back to the time domain signal through inverse transformation to obtain the final clear speech signal for voice command recognition.

5. The voice-controlled smart house theater system according to claim 1, characterized in that: The context understanding and instruction analysis module uses an algorithm based on semantic analysis to identify the user's true intention. The specific steps are as follows: Semantically decompose the received voice commands and identify the core information of verbs and nouns; Based on the context analysis algorithm, the instruction is compared with the previous user operation records to determine whether the instruction is ambiguous; If there is ambiguity, clarify the user's intention by asking rhetorical questions and prompting them; Unambiguous instructions are further processed and converted into specific device control commands.

6. The voice-controlled smart house theater system according to claim 1, characterized in that: The context understanding and command analysis module performs context recognition and multi-command processing on voice commands in the following specific steps: Assume that the input speech signal is X, the context information is C, and the instruction sequence is I={I i }={I1,I2,……,I n }, where I i is the i-th instruction; According to the context information C and the input signal X, the matching degree of each instruction is calculated. The calculation expression is as follows: ， Where, M(I i , C) is instruction I i The matching degree between the context information C, m is the number of features used to calculate the instruction matching degree, w k is the weight of the kth feature, f k (I i , C, X) is the kth characteristic function, calculate the i-th instruction I i The degree of match between the context information C and the speech signal X; By selecting the instruction with the maximum matching degree, the user's intention is determined. If the matching degree M(I max , C)>T, then the instruction is considered valid, where I max is the instruction with the highest matching degree M, and T is the preset threshold; If multiple instructions are issued at the same time, the following formula is used to merge the instructions; ， Where, I final is the instruction that is finally selected, n is the total number of instructions, and argmax represents the operation that selects the maximum value.

7. The voice-controlled smart house theater system according to claim 1, characterized in that: The device control and execution module achieves compatibility between multiple devices through an integrated device protocol conversion layer. The specific steps are as follows: Receive the processed voice command and select the corresponding device protocol according to the command type; Analyze each device protocol and generate corresponding control instructions; Send control instructions to corresponding devices through the smart home control system to perform relevant operations; During multi-device operation, coordinate the interactions between devices to avoid conflicts and duplicate instructions.

8. The voice-controlled smart house theater system according to claim 1, characterized in that: The device status monitoring and feedback module monitors the device operating status in real time through the sensor network to ensure the accuracy of instruction execution. The specific steps are as follows: Install sensors on smart home devices to monitor their operating status; Regularly send equipment operation data to the equipment status monitoring module to detect whether the current status of the equipment is consistent with the user's instructions; If the device status does not match, the user is notified through a feedback mechanism and provided with correction suggestions; Based on user feedback, restore the device status to the expected state.

9. The voice-controlled smart house theater system according to claim 1, characterized in that: Machine learning models are used to detect anomalies in device status and identify potential errors in device operation. Device behavior can be modeled based on real-time collected data to detect whether the device status is consistent with expectations. When anomalies are found, users are promptly notified through a feedback mechanism and corrective measures are taken.

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