Directional sound collection processing method and system suitable for remote monitoring and high in signal-to-noise ratio

Abstract

The invention discloses a high-signal-to-noise-ratio directional sound collection processing method, system and platform suitable for remote monitoring and a storage medium, and the method comprises the steps: obtaining to-be-processed audio original signal data in an environment in real time based on remote monitoring, processing the audio original signal data at a time, and generating corresponding first audio signal data; filtering the first audio signal data to generate corresponding second audio signal data, and converting the second audio signal data to generate third audio signal data corresponding to the second audio signal data; in combination with a deep learning model, performing noise reduction processing on the third audio signal data, and generating and backing up fourth audio signal data corresponding to the third audio signal data; according to the invention, noise can be effectively suppressed, the signal-to-noise ratio of an audio signal is improved, and the sound collection quality is improved; data tampering is prevented, and authenticity and integrity of audio data and safety of sound collection are ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of speech noise reduction processing, and in particular relates to a directional sound collection processing method and system suitable for remote monitoring and with a high signal-to-noise ratio. Background Art

[0002] With the rapid development of information technology and communication technology, remote monitoring systems have been widely used in various fields, including security monitoring, traffic management, intelligent buildings, etc. Remote monitoring systems usually include video monitoring and audio monitoring. Video monitoring technology has been relatively mature and widely used, while audio monitoring is particularly important in some special environments, such as extracting effective voice signals in a noisy environment and monitoring abnormal sounds in the environment.

[0003] However, when facing noise interference in complex environments, the existing audio monitoring system has a low signal-to-noise ratio (SNR), which makes it difficult to ensure the clarity and accuracy of the audio signal, affecting the monitoring effect. Especially in open spaces or environments with many noise sources, ordinary omnidirectional microphones are easily interfered by various noises and cannot accurately collect the target sound, resulting in unsatisfactory monitoring effects.

[0004] In other words, the existing industry or products have the following defects or deficiencies: Insufficient noise processing capability: The existing sound collection system lacks effective noise processing technology, resulting in more noise in the collected audio signals, thereby reducing the signal-to-noise ratio and affecting the sound collection quality.

[0005] Lack of remote monitoring function: Most current sound collection systems do not have remote monitoring functions and cannot obtain the collected sound content in real time. This limitation not only affects the ability to monitor audio data in real time, but also cannot prevent subsequent tampering of the collected sound content.

[0006] Poor system application effect: Due to the above defects, the overall system has poor effect in practical applications and cannot meet the needs of high-quality audio acquisition.

[0007] Therefore, in view of the above technical problems and defects, it is urgent to design and develop a directional sound processing method and system suitable for remote monitoring and with high signal-to-noise ratio. Summary of the invention

[0008] In order to overcome the shortcomings and difficulties of the above-mentioned prior art, the purpose of the present invention is to provide a directional sound collection processing method, system, platform and storage medium with high signal-to-noise ratio suitable for remote monitoring in order to solve the problem that although the current sound collection system can collect audio, it lacks a good noise processing process, resulting in a low signal-to-noise ratio of sound collection, and cannot improve the sound collection quality. In addition, it does not have a remote monitoring function for sound collection, cannot obtain the sound collection content in real time, and cannot avoid subsequent tampering, resulting in poor application effect of the overall system. The present invention can effectively suppress noise, significantly improve the signal-to-noise ratio of audio signals, and thus improve the sound collection quality. It can also prevent data tampering, ensure the authenticity and integrity of audio data, and ensure the security of sound collection.

[0009] The first object of the present invention is to provide a directional sound processing method suitable for remote monitoring and with a high signal-to-noise ratio; the second object of the present invention is to provide a directional sound processing system suitable for remote monitoring and with a high signal-to-noise ratio; the third object of the present invention is to provide a directional sound processing platform suitable for remote monitoring and with a high signal-to-noise ratio; the fourth object of the present invention is to provide a computer-readable storage medium.

[0010] The first object of the present invention is achieved in that the method comprises the following steps: Based on remote monitoring, the original audio signal data to be processed in the environment is obtained in real time, and the original audio signal data is processed once to generate corresponding first audio signal data; wherein the original audio signal data is a sound signal in the environment captured by a microphone array; Filtering the first audio signal data to generate corresponding second audio signal data, and converting the second audio signal data to generate third audio signal data corresponding to the second audio signal data; wherein the second audio signal data is audio signal data after filtering; and the third audio signal data is audio signal data after analog-to-digital conversion; In combination with a deep learning model, the third audio signal data is subjected to noise reduction processing to generate and back up fourth audio signal data corresponding to the third audio signal data; wherein the fourth audio signal data is high signal-to-noise ratio audio signal data after noise processing.

[0011] Furthermore, the method of acquiring the original audio signal data to be processed in the environment in real time based on remote monitoring, and processing the original audio signal data once to generate corresponding first audio signal data, further includes: Detect valid audio signal data according to an algorithm based on feature extraction and machine learning, and edit irrelevant audio signal data in combination with an audio signal processing method to retain valid audio signal data; Adjust the gain of each frequency band of the audio signal data and keep the volume stable based on the automatic gain control algorithm.

[0012] Further, the filtering and processing of the first audio signal data to generate corresponding second audio signal data, and the conversion and processing of the second audio signal data to generate third audio signal data corresponding to the second audio signal data, further includes: The sensor is used to detect the strength of the audio signal data, and the signal data strength is adjusted in combination with the amplification circuit; The signal-to-noise ratio of the first audio signal data is improved, and the corresponding audio signal data is combined and processed.

[0013] Further, the filtering and processing of the first audio signal data to generate corresponding second audio signal data, and the conversion and processing of the second audio signal data to generate third audio signal data corresponding to the second audio signal data, further includes: Combine sensors to search for analog sound signal data, and use analog-to-digital converters to convert the analog signal data into digital signal data; Based on Fast Fourier Transform, the corresponding audio signal data is processed in the time domain and frequency domain.

[0014] Further, the combining of the deep learning model, denoising the third audio signal data, generating and backing up fourth audio signal data corresponding to the third audio signal data, further includes: Extracting the spectral feature data corresponding to the noise, and identifying and classifying the noise spectral feature data based on the deep learning model; The third audio signal data is denoised in real time according to the noise spectrum characteristic data.

[0015] The second object of the present invention is achieved in this way: the system is used to implement the directional sound collection processing method suitable for remote monitoring and with a high signal-to-noise ratio, and the system includes: A data acquisition and generation unit, used to acquire the original audio signal data to be processed in the environment in real time based on remote monitoring, and process the original audio signal data once to generate corresponding first audio signal data; wherein the original audio signal data is a sound signal in the environment captured by a microphone array; a filtering and converting processing unit, configured to filter and process the first audio signal data to generate corresponding second audio signal data, and convert and process the second audio signal data to generate third audio signal data corresponding to the second audio signal data; wherein the second audio signal data is audio signal data after filtering; and the third audio signal data is audio signal data after analog-to-digital conversion; An audio data noise reduction generation unit is used to combine a deep learning model to perform noise reduction processing on the third audio signal data, generate and back up fourth audio signal data corresponding to the third audio signal data; wherein the fourth audio signal data is high signal-to-noise ratio audio signal data after noise processing.

[0016] Furthermore, the data acquisition generation unit further includes: A first data processing module, configured to detect valid audio signal data according to an algorithm based on feature extraction and machine learning, and to clip irrelevant audio signal data in combination with an audio signal processing method, and retain valid audio signal data; The second data processing module is used to adjust the gain of each frequency band of the audio signal data and keep the volume stable based on the automatic gain control algorithm; And / or, the filtering conversion processing unit further includes: A third data processing module is used to detect the strength of the audio signal data using a sensor and adjust the signal data strength in combination with an amplification circuit; a fourth data processing module, configured to improve the signal-to-noise ratio of the first audio signal data and combine and process the corresponding audio signal data; And / or, the audio data noise reduction generation unit further includes: A fifth data processing module is used to extract frequency spectrum feature data corresponding to the noise, and respectively identify and classify the noise frequency spectrum feature data based on a deep learning model; The sixth data processing module is used to perform real-time denoising processing on the third audio signal data according to the noise spectrum characteristic data.

[0017] Furthermore, the filtering conversion processing unit further includes: a seventh data processing module, for searching analog sound signal data in combination with the sensor, and converting the analog signal data into digital signal data using an analog-to-digital converter; The eighth data processing module is used to process the corresponding audio signal data in the time domain and the frequency domain based on the fast Fourier transform.

[0018] The third object of the present invention is achieved as follows: it includes a processor, a memory, and a directional sound collection processing platform control program suitable for remote monitoring and with a high signal-to-noise ratio; wherein the directional sound collection processing platform control program suitable for remote monitoring and with a high signal-to-noise ratio is executed in the processor, and the directional sound collection processing platform control program suitable for remote monitoring and with a high signal-to-noise ratio is stored in the memory, and the directional sound collection processing platform control program suitable for remote monitoring and with a high signal-to-noise ratio implements the directional sound collection processing method suitable for remote monitoring and with a high signal-to-noise ratio.

[0019] The fourth object of the present invention is achieved in this way: the computer-readable storage medium stores a directional sound collection processing platform control program suitable for remote monitoring and with a high signal-to-noise ratio, and the directional sound collection processing platform control program suitable for remote monitoring and with a high signal-to-noise ratio implements the directional sound collection processing method suitable for remote monitoring and with a high signal-to-noise ratio.

[0020] The present invention uses a method to obtain the original audio signal data to be processed in the environment in real time based on remote monitoring, and processes the original audio signal data once to generate corresponding first audio signal data; wherein the original audio signal data is a sound signal in the environment captured by a microphone array; Filtering the first audio signal data to generate corresponding second audio signal data, and converting the second audio signal data to generate third audio signal data corresponding to the second audio signal data; wherein the second audio signal data is audio signal data after filtering; and the third audio signal data is audio signal data after analog-to-digital conversion; Combined with the deep learning model, the third audio signal data is denoised and processed, and fourth audio signal data corresponding to the third audio signal data is generated and backed up; wherein the fourth audio signal data is high signal-to-noise ratio audio signal data after noise processing, as well as the system, platform and storage medium corresponding to the method, which can effectively suppress noise and significantly improve the signal-to-noise ratio of the audio signal, thereby improving the sound collection quality; it can also prevent data tampering, ensure the authenticity and integrity of the audio data, and ensure the security of sound collection.

[0021] In other words, the signal-to-noise ratio can be significantly improved through the solution of the present invention: by introducing advanced noise suppression algorithms and technologies, such as adaptive filters and noise reduction microphone arrays, noise can be effectively suppressed, the signal-to-noise ratio of the audio signal can be significantly improved, thereby improving the sound collection quality.

[0022] Realize remote monitoring: The system integrates a remote monitoring module, which can realize real-time monitoring and management of the sound collection equipment through network connection. It can not only obtain the sound collection content in real time, but also prevent data tampering, ensure the authenticity and integrity of the audio data, and ensure the security of sound collection.

[0023] Modular design enhances scalability and flexibility: The modular design makes the system more scalable and flexible. Different modules (such as automatic sound processing module, signal preprocessing module, noise processing module, etc.) work together and can be expanded and adjusted according to specific needs, improving the overall performance and applicability of the system.

[0024] Efficient noise processing: Through three steps of noise processing (preliminary noise processing, signal filtering, noise feature extraction and deep learning), the system can greatly improve the noise processing effect. In particular, the noise feature extraction and deep learning module can quickly identify and process noise, improving the audio noise reduction effect.

[0025] Automated audio processing: The automatic audio processing module can automatically detect, edit, reduce noise, balance and control the volume of the collected audio, simplifying the audio collection process and improving audio collection efficiency and audio quality.

[0026] Improved system application effect: Through the comprehensive application of the above technical solutions, the system's effect in actual application is significantly improved, which can meet the needs of high-quality audio acquisition and is suitable for a variety of application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 It is a schematic diagram of the three-dimensional structure of a device according to an embodiment of a directional sound collection and processing method suitable for remote monitoring and having a high signal-to-noise ratio; Figure 2 It is a schematic diagram of the exploded stereoscopic structure of a device according to an embodiment of a directional sound collection and processing method suitable for remote monitoring and having a high signal-to-noise ratio of the present invention; Figure 3 A schematic diagram of the system module composition of an embodiment of a directional sound collection and processing method suitable for remote monitoring and having a high signal-to-noise ratio according to the present invention; Figure 4 It is a schematic diagram of the submodule structure of a microphone information acquisition module of an embodiment of a directional sound collection processing method suitable for remote monitoring and having a high signal-to-noise ratio of the present invention; Figure 5 It is a schematic diagram of the submodule structure of the automatic sound collection processing module of an embodiment of a directional sound collection processing method suitable for remote monitoring and having a high signal-to-noise ratio of the present invention; Figure 6 It is a schematic diagram of the submodule structure of a signal preprocessing module of an embodiment of a directional sound collection processing method suitable for remote monitoring and having a high signal-to-noise ratio of the present invention; Figure 7 It is a schematic diagram of the submodule structure of a digital signal processing module of an embodiment of a directional sound collection processing method suitable for remote monitoring and having a high signal-to-noise ratio of the present invention; Figure 8It is a schematic diagram of the submodule structure of a noise processing module of an embodiment of a directional sound collection processing method suitable for remote monitoring and having a high signal-to-noise ratio of the present invention; Fig. 9 A schematic diagram of the process steps of a directional sound collection processing method suitable for remote monitoring and having a high signal-to-noise ratio according to the present invention; Fig.10 This is a schematic diagram of the architecture of a directional sound processing system suitable for remote monitoring and having a high signal-to-noise ratio according to the present invention; Fig.11 This is a schematic diagram of the architecture of a directional sound processing platform suitable for remote monitoring and having a high signal-to-noise ratio according to the present invention; Fig.12 A schematic diagram of a computer-readable storage medium architecture in an embodiment of the present invention; Fig.13 This is a schematic diagram of the structure of the amplifying circuit of the signal preprocessing module of an embodiment of a directional sound collection processing method suitable for remote monitoring and having a high signal-to-noise ratio according to the present invention; In the figure, 1-mounting bracket; 2-sound collection microphone; 3-mounting column; 4-turntable; 5-base; 6-adjustable rotary head; 7-sound collection port; 8-microphone information acquisition module; 9-sound collection automatic processing module; 10-signal preprocessing module; 11-digital signal processing module; 12-noise processing module.

[0029] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0030] In order to better understand the purpose, technical solutions and advantages of the present invention, the present invention is further described below in conjunction with the accompanying drawings and specific implementation methods. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification.

[0031] The present invention may also be implemented or applied through other different specific examples, and the details in this specification may also be modified and changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention.

[0032] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back...), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0033] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. Secondly, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0034] Preferably, the directional sound collection processing method suitable for remote monitoring and with high signal-to-noise ratio of the present invention is applied in one or more terminals or servers. The terminal is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application-specific integrated circuits (ASIC), programmable gate arrays (FPGA), digital signal processors (DSP), embedded devices, etc.

[0035] The terminal can be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal can interact with the client through a keyboard, a mouse, a remote control, a touch pad, or a voice control device.

[0036] The present invention is to realize a directional sound collection processing method, system, platform and storage medium suitable for remote monitoring and with high signal-to-noise ratio.

[0037] like Fig. 9 , which is a flow chart of a directional sound collection processing method suitable for remote monitoring and having a high signal-to-noise ratio provided by an embodiment of the present invention.

[0038] In this embodiment, the directional sound collection and processing method suitable for remote monitoring and with a high signal-to-noise ratio can be applied to a terminal with a display function or a fixed terminal. The terminal is not limited to a personal computer, a smart phone, a tablet computer, a desktop computer or an all-in-one computer equipped with a camera, etc.

[0039] The directional sound collection processing method suitable for remote monitoring and having a high signal-to-noise ratio can also be applied to a hardware environment consisting of a terminal and a server connected to the terminal through a network. The network includes but is not limited to: a wide area network, a metropolitan area network or a local area network. The directional sound collection processing method suitable for remote monitoring and having a high signal-to-noise ratio according to the embodiment of the present invention can be executed by a server, can be executed by a terminal, or can be executed by both a server and a terminal.

[0040] For example, for a terminal that needs to perform directional sound processing suitable for remote monitoring and with a high signal-to-noise ratio, the directional sound processing function suitable for remote monitoring and with a high signal-to-noise ratio provided by the method of the present invention can be directly integrated on the terminal, or a client for implementing the method of the present invention can be installed. For another example, the method provided by the present invention can also be run on a server or other device in the form of a software development kit (SDK), and an interface for the directional sound processing function suitable for remote monitoring and with a high signal-to-noise ratio is provided in the form of SDK. The terminal or other device can implement the directional sound processing function suitable for remote monitoring and with a high signal-to-noise ratio through the provided interface. The present invention is further described below in conjunction with the accompanying drawings.

[0041] like Figure 1-Figure 9 As shown, the present invention provides a directional sound collection processing method suitable for remote monitoring and having a high signal-to-noise ratio, wherein the method comprises the following steps: S1. Based on remote monitoring, obtain the original audio signal data to be processed in the environment in real time, and process the original audio signal data once to generate corresponding first audio signal data; wherein the original audio signal data is the sound signal in the environment captured by the microphone array; S2, filtering and processing the first audio signal data to generate corresponding second audio signal data, and converting and processing the second audio signal data to generate third audio signal data corresponding to the second audio signal data; wherein the second audio signal data is the audio signal data after filtering; and the third audio signal data is the audio signal data after analog-to-digital conversion; S3. In combination with a deep learning model, perform noise reduction processing on the third audio signal data, generate and back up fourth audio signal data corresponding to the third audio signal data; wherein the fourth audio signal data is high signal-to-noise ratio audio signal data after noise processing.

[0042] The method of acquiring the original audio signal data to be processed in the environment in real time based on remote monitoring, and processing the original audio signal data once to generate corresponding first audio signal data, further includes: S11. Detecting valid audio signal data according to an algorithm based on feature extraction and machine learning, and editing irrelevant audio signal data in combination with an audio signal processing method to retain valid audio signal data; S12. Adjust the gain of each frequency band of the audio signal data, and keep the volume stable based on an automatic gain control algorithm.

[0043] The filtering process of the first audio signal data to generate corresponding second audio signal data, and the conversion process of the second audio signal data to generate third audio signal data corresponding to the second audio signal data, further includes: S21, using a sensor to detect the strength of the audio signal data, and adjusting the signal data strength in combination with an amplifying circuit; S22: Improve the signal-to-noise ratio of the first audio signal data, and merge and process the corresponding audio signal data.

[0044] The filtering process of the first audio signal data to generate corresponding second audio signal data, and the conversion process of the second audio signal data to generate third audio signal data corresponding to the second audio signal data, further includes: S23, searching for analog sound signal data in combination with a sensor, and converting the analog signal data into digital signal data using an analog-to-digital converter; S24. Based on fast Fourier transform, process the corresponding audio signal data in the time domain and the frequency domain.

[0045] The method of combining the deep learning model, performing noise reduction processing on the third audio signal data, and generating and backing up fourth audio signal data corresponding to the third audio signal data, further includes: S31, extracting frequency spectrum feature data corresponding to the noise, and identifying and classifying the noise frequency spectrum feature data based on a deep learning model; S32: Perform real-time denoising on the third audio signal data according to the noise spectrum feature data.

[0046] Specifically, in an embodiment of the present invention, a high signal-to-noise ratio directional sound collection system based on remote monitoring is provided, including a microphone information acquisition module and a remote monitoring module, the output ends of the microphone information acquisition module and the remote monitoring module are both connected to the input end of the automatic sound collection module, the output end of the automatic sound collection module is connected to the input end of the sound collection automatic processing module, and the output end of the sound collection automatic processing module is connected to the input end of the signal preprocessing module.

[0047] The output end of the signal preprocessing module is connected to the input end of the digital signal processing module, the output end of the digital signal processing module is connected to the input end of the noise processing module, and the output end of the noise processing module is connected to the input end of the data information backup module.

[0048] The microphone information acquisition module includes a microphone array structure acquisition module, the output end of the microphone array structure acquisition module is connected to the input end of the microphone array element spacing acquisition module, the output end of the microphone array element spacing acquisition module is connected to the input end of the microphone array size acquisition module, the output end of the microphone array size acquisition module is connected to the input end of the microphone spatial resolution acquisition module, and the output end of the microphone spatial resolution acquisition module is connected to the input end of the microphone model acquisition module. Among them, the microphone information acquisition module is used to obtain the structure and configuration information of the microphone array to ensure the accuracy of the basic data of the subsequent signal processing. The microphone array structure acquisition module is used to obtain the structural information of the microphone array, including the arrangement of the microphones and the geometric shape of the array. The microphone array element spacing acquisition module is used to measure and record the spacing between adjacent microphones in the microphone array. The microphone array size acquisition module is used to determine the size of the entire microphone array, that is, the spatial range covered by the array. The microphone spatial resolution acquisition module is used to measure and record the spatial resolution of the microphone array, that is, the ability of the array to distinguish sounds from different directions. The microphone model acquisition module is used to identify and record the model and technical parameters of each microphone.

[0049] The automatic sound processing module includes an automatic sound detection module, the output end of the automatic sound detection module is connected to the input end of the automatic sound editing processing module, and the output end of the automatic sound editing processing module is connected to the input end of the automatic sound noise reduction module. The output end of the automatic sound noise reduction module is connected to the input end of the sound equalization control module, the output end of the sound equalization control module is connected to the input end of the sound volume control module, and the output end of the sound volume control module is connected to the input end of the sound compression and limitation automatic processing module. Among them, the automatic sound processing module is used to perform preliminary processing on the acquired audio signal, including detection, editing, noise reduction, equalization control and volume control. The automatic detection module is used to detect valid audio signals. The automatic editing processing module is used to clip irrelevant audio signals and retain valid parts. The automatic noise reduction module is used to perform noise reduction processing using an adaptive filter and a deep learning algorithm. The equalization control module is used to adjust the gain of each frequency band of the audio. The volume control module is used to keep the volume stable based on the automatic gain control algorithm. The compression and limitation automatic processing module is used to compress and limit the audio signal to prevent overload. The signal preprocessing module includes a signal strength detection module, the output end of the signal strength detection module is connected to the input end of the signal amplification adjustment module, the output end of the signal amplification adjustment module is connected to the input end of the filter selection module, and the output end of the filter selection module is connected to the input end of the signal automatic filtering processing module. Among them, the signal preprocessing module is used to further filter and amplify the audio signal to improve the signal-to-noise ratio. The signal strength detection module is used to detect the strength of the audio signal. The signal amplification adjustment module is used to adjust the signal strength through the amplification circuit. The filter selection module is used to select a suitable filter to filter the signal. The signal automatic filtering processing module is used to automatically filter the signal. The signal-to-noise ratio improvement module is used to improve the signal-to-noise ratio of the audio signal through an algorithm. The code re-merging module is used to re-merge the processed signal. The output end of the signal automatic filtering processing module is connected to the input end of the signal-to-noise ratio improvement module, and the output end of the signal-to-noise ratio improvement module is connected to the input end of the code re-merging module.

[0050] The digital signal processing module includes an analog sound signal search module, the output end of the analog sound signal search module is connected to the input end of the digital signal conversion module, the output end of the digital signal conversion module is connected to the input end of the sound time domain search module, and the output end of the sound time domain search module is connected to the input end of the sound frequency domain conversion module. Wherein, the digital signal processing module is used to convert the analog audio signal into a digital signal and process it in the time domain and the frequency domain. The analog sound signal search module is used to search for analog sound signals. The digital signal conversion module is used to convert analog signals into digital signals. The sound time domain search module is used to process audio signals in the time domain. The sound frequency domain conversion module is used to process audio signals in the frequency domain.

[0051] The noise processing module includes a noise source tracking module, the output end of the noise source tracking module is connected to the input end of the noise feature automatic extraction module, the output end of the noise feature automatic extraction module is connected to the input end of the deep learning module, and the output end of the deep learning module is connected to the input end of the noise audio elimination module. Among them, the noise processing module is used to identify, classify and eliminate the noise in the audio signal to improve the clarity of the audio signal. The noise source tracking module is used to track the noise source. The noise feature automatic extraction module is used to extract the spectral characteristics of the noise. The deep learning module is used to identify and classify noise using a deep learning model. The noise audio elimination module is used to process and eliminate noise based on the recognition result.

[0052] By setting up an automatic sound collection processing module and a signal preprocessing module in the system, the automatic sound collection processing module can effectively process the collected audio, including preliminary processing of noise. The signal preprocessing module can further filter the signal to improve the signal-to-noise ratio of the audio and further improve the audio noise reduction effect. At the same time, a noise processing module of the third process is also provided. The module can automatically extract noise features and perform deep learning on them. It can quickly identify noise in the subsequent process to facilitate rapid processing. Through these three steps of noise processing, the system's noise processing effect can be greatly improved. At the same time, a remote monitoring module is also provided in the system. The module can monitor the sound collection content in real time to avoid subsequent tampering with the sound collection content, ensure the security of sound collection, and improve the actual application effect of the overall system.

[0053] like Figure 1-Figure 2As shown, the present invention also discloses a high signal-to-noise ratio directional sound collection device based on remote monitoring, including a base 5, a turntable 4 is rotatably mounted on the top of the base 5 through a rotating shaft, a mounting column 3 is longitudinally fixedly mounted on the top surface of the turntable 4, mounting frames 1 are fixedly mounted on both sides of the top of the mounting column 3, a sound receiving microphone 2 is rotatably mounted between the two mounting frames 1 through a rotating shaft, an adjusting rotary head 6 is fixedly mounted on one end of the rotating shaft, and a plurality of sound receiving ports 7 are arranged on the outer surface of the sound receiving microphone 2.

[0054] Connection between the mounting frame 1 and the mounting column 3: The mounting frame is connected to both sides of the top of the mounting column by a fixed installation method. This can be achieved by welding, bolting or other mechanical connection methods. Connection between the mounting frame 1 and the radio microphone 2: The radio microphone is installed between the two mounting frames through a shaft. An adjustment head 6 is fixed at one end of the shaft, and the user can adjust the angle of the microphone by adjusting the head. The installation of the shaft can use bearings or sliding devices to achieve smooth rotation.

[0055] The microphone fixing method in the prior art is usually relatively simple and cannot flexibly adjust the angle. The present invention introduces an adjustable shaft and an adjustable rotating head to achieve flexible adjustment of the microphone angle to meet different sound collection requirements.

[0056] like Figure 3-Figure 8 As shown, the present invention also provides a high signal-to-noise ratio directional sound collection system based on remote monitoring, including a microphone information acquisition module 8 and a remote monitoring module, the output ends of the microphone information acquisition module 8 and the remote monitoring module are both connected to the input end of the automatic sound collection module, the output end of the automatic sound collection module is connected to the input end of the sound collection automatic processing module 9, the output end of the sound collection automatic processing module 9 is connected to the input end of the signal preprocessing module 10, the output end of the signal preprocessing module 10 is connected to the input end of the digital signal processing module 11, the output end of the digital signal processing module 11 is connected to the input end of the noise processing module 12, and the output end of the noise processing module 12 is connected to the input end of the data information backup module.

[0057] The microphone information acquisition module 8 includes a microphone array structure acquisition module, the output end of the microphone array structure acquisition module is connected to the input end of the microphone array element spacing acquisition module, the output end of the microphone array element spacing acquisition module is connected to the input end of the microphone array size acquisition module, the output end of the microphone array size acquisition module is connected to the input end of the microphone spatial resolution acquisition module, and the output end of the microphone spatial resolution acquisition module is connected to the input end of the microphone model acquisition module.

[0058] The automatic sound processing module 9 includes an automatic sound detection module, the output end of the automatic sound detection module is connected to the input end of the automatic sound editing processing module, the output end of the automatic sound editing processing module is connected to the input end of the automatic sound noise reduction module, the output end of the automatic sound noise reduction module is connected to the input end of the sound equalization control module, the output end of the sound equalization control module is connected to the input end of the sound volume control module, and the output end of the sound volume control module is connected to the input end of the sound compression and limitation automatic processing module.

[0059] The signal preprocessing module 10 includes a signal strength detection module, the output end of the signal strength detection module is connected to the input end of the signal amplification adjustment module, the output end of the signal amplification adjustment module is connected to the input end of the filter selection module, the output end of the filter selection module is connected to the input end of the signal automatic filtering processing module, the output end of the signal automatic filtering processing module is connected to the input end of the signal-to-noise ratio improvement module, and the output end of the signal-to-noise ratio improvement module is connected to the input end of the code re-merging module.

[0060] The digital signal processing module 11 includes an analog sound signal search module, the output end of the analog sound signal search module is connected to the input end of the digital signal conversion module, the output end of the digital signal conversion module is connected to the input end of the sound time domain search module, and the output end of the sound time domain search module is connected to the input end of the sound frequency domain conversion module.

[0061] The noise processing module 12 includes a noise source tracking module, the output end of the noise source tracking module is connected to the input end of the noise feature automatic extraction module, the output end of the noise feature automatic extraction module is connected to the input end of the deep learning module, and the output end of the deep learning module is connected to the input end of the noise audio elimination module.

[0062] Among them, the automatic sound processing module: automatic detection, using algorithms based on feature extraction and machine learning to detect valid audio signals. Automatic editing, using audio signal processing technology to edit irrelevant parts and retain valid parts. Automatic noise reduction, using adaptive filters and deep learning algorithms for noise reduction. Equalization control, using spectrum analysis technology to automatically adjust the gain of each frequency band of audio. Volume control, based on the automatic gain control (AGC) algorithm, to keep the volume stable.

[0063] Signal preprocessing module: Signal strength detection, using sensors to detect the strength of audio signals. Signal amplification adjustment, adjusting the signal strength through the amplification circuit. Filtering, selecting appropriate filters (such as low-pass, high-pass, band-pass filters) to filter the signal. Signal-to-noise ratio improvement, using algorithms to improve the signal-to-noise ratio of audio signals. Code re-merging, merging the processed signals for subsequent processing. Among them, in the amplification circuit, input signal (V_in): This is the audio signal source, indicating the signal input from the audio source, and there is no specific model. Input resistance (R_in): Select a resistor that matches the input impedance requirements of the circuit. Common models include: metal film resistors, such as the Vishay Dale CMF series or the Panasonic ERJ series. Operational amplifier (Op-Amp): Select an op amp with low noise, high input impedance, and suitable for audio applications. Common models include: Texas Instruments OPA2134; Analog Devices AD797; Texas Instruments TL072; Feedback resistor (R_f): Similar to R_in, a precision resistor should be selected. Common models include: VishayDale CMF series or Panasonic ERJ series. Output signal (V_out): This is the output signal of the op amp, and there is no specific model because it is an amplified audio signal.

[0064] Digital signal processing module: Analog sound signal search, using sensors to search for analog sound signals. Digital signal conversion, using ADC (analog-to-digital converter) to convert analog signals into digital signals. Time domain and frequency domain processing, using FFT (Fast Fourier Transform) and other technologies to process signals in the time domain and frequency domain.

[0065] Noise processing module: Noise tracking, using sensors and algorithms to track the noise source. Noise feature extraction, extracting the spectral features of the noise. Deep learning, using deep learning models to identify and classify noise. Noise audio elimination, processing and eliminating noise based on the recognition results.

[0066] In this embodiment, by providing an automatic sound collection processing module and a signal preprocessing module in the system, the automatic sound collection processing module can effectively process the collected audio, including preliminary processing of noise. The signal preprocessing module can further filter the signal to improve the signal-to-noise ratio of the audio and further improve the audio noise reduction effect. At the same time, a noise processing module of the third process is also provided. The module can automatically extract noise features and perform deep learning on the features, and can subsequently quickly identify noise for rapid processing. Through these three steps of noise processing, the system's noise processing effect can be greatly improved. At the same time, a remote monitoring module is also provided in the system. The module can monitor the sound collection content in real time to avoid subsequent tampering with the sound collection content, ensure the security of sound collection, and improve the actual application effect of the overall system. Among them, adaptive filter and noise reduction microphone array: the existing technology mostly adopts simple filtering technology with limited effect. However, in the solution of the present invention, adaptive filter and noise reduction microphone array are introduced, and advanced algorithms are used to dynamically adjust the filtering parameters, effectively suppress noise and improve the signal-to-noise ratio.

[0067] Remote monitoring module: In the prior art, most sound collection systems lack remote monitoring functions and cannot obtain and manage the sound collection content in real time. In the solution of the present invention, a remote monitoring module is integrated, which can monitor and manage the sound collection equipment in real time through the network, ensure the authenticity and integrity of the data, prevent tampering, and improve data security.

[0068] Modular design: In the prior art, the design is relatively fixed and does not have good scalability. In the solution of the present invention, a modular design is adopted to make the system more flexible and scalable. Different modules can be adjusted and expanded according to needs to improve the overall performance and applicability of the system.

[0069] Adaptive filter: Principle, adaptive filter dynamically adjusts filter parameters through iterative algorithms (such as LMS or RLS) to minimize output error and achieve noise suppression. Implementation: DSP (digital signal processor) or FPGA (field programmable gate array) is used to implement the filtering algorithm in hardware, and programming languages ​​(such as C or Python) are used to implement the algorithm logic in software.

[0070] Noise suppression algorithm: Principle, using frequency domain subtraction, Wiener filtering and other algorithms, combined with deep learning models, to identify and eliminate noise in audio signals. Implement the algorithm using Python or MATLAB, train the deep learning model to identify noise features, and combine hardware acceleration to achieve real-time processing.

[0071] In general, the overall implementation process of the solution of the present invention is: significantly improve the signal-to-noise ratio, and the implementation process is as follows: Sound capture: The microphone array captures the sound signal in the environment. Preliminary processing: The adaptive filter processes the initially captured sound signal, dynamically adjusts the filter parameters, and reduces background noise. Advanced noise reduction: The noise reduction microphone array uses beamforming technology to further suppress noise from non-target directions and enhance the target sound.

[0072] Realize remote monitoring: Realize the process, data collection: The collected audio data is transmitted in real time through the remote monitoring module. Network connection: The audio data is transmitted to the remote monitoring terminal through the network. Real-time monitoring: Users can view and manage audio data in real time through the remote terminal to ensure that the data has not been tampered with. Data encryption: The audio data is encrypted during transmission to ensure the security and integrity of the data.

[0073] Modular design, enhanced scalability and flexibility: Implementation process, module division: The system is divided into multiple independent modules (such as automatic sound collection processing module, signal preprocessing module, noise processing module, etc.). Module collaboration: Each module runs independently, but can collaborate with each other to complete audio collection and processing tasks. Functional expansion: According to actual needs, users can add or upgrade modules, and the system can flexibly adapt to different application scenarios.

[0074] Efficient noise processing: Implementation process, preliminary processing: Adaptive filter performs preliminary noise reduction on the captured sound signal. Signal filtering: Select appropriate filters (such as low-pass, high-pass, band-pass filters) to further remove residual noise. Feature extraction and deep learning: Extract noise features, quickly identify and process noise through deep learning models, and achieve efficient noise reduction.

[0075] Automatic sound processing: implement the process, automatic detection: use machine learning algorithms to automatically detect valid audio signals. Automatic editing: according to the characteristics of the audio signal, automatically edit the irrelevant parts and retain the valid parts. Automatic noise reduction: use adaptive filters and deep learning algorithms to reduce the noise of the audio. Equalization control: automatically adjust the gain of each frequency band of the audio through spectrum analysis. Volume control: use automatic gain control algorithm to ensure stable volume.

[0076] Improved system application effect: Implementation process, comprehensive application technology: Integrate and apply the above technologies (such as adaptive filter, remote monitoring, modular design, three-step noise processing, automatic sound processing, etc.). Practical application: The system has significantly improved the effect in practical application and is suitable for security monitoring, traffic management, intelligent buildings and other fields. User experience: Through the combination of the above technologies, the system can achieve high-quality audio acquisition, meet the needs of different scenarios, and improve user experience and system performance.

[0077] In the scheme of the present invention, this scheme adopts multi-stage noise reduction processing: a multi-stage noise reduction processing mechanism is introduced, spectrum analysis is performed after preliminary noise reduction, and deep noise reduction processing is performed again to improve the overall noise reduction effect.

[0078] Adaptive filter: Adaptive filter can automatically adjust the filter parameters according to the real-time environmental noise to further improve the noise reduction effect.

[0079] Multi-level noise reduction processing, specific solution: Step 1: Primary noise reduction uses a traditional adaptive filter (such as LMS filter) to perform preliminary noise reduction on the audio signal. Step 2: Perform spectrum analysis on the audio signal after preliminary noise reduction to extract the main noise features. Step 3: Based on the spectrum features, a deep learning model (such as convolutional neural network CNN) is used to perform secondary noise reduction to further remove complex noise.

[0080] Virtual result: After multi-level noise reduction processing, the audio signal has significantly reduced noise, the signal-to-noise ratio is increased by more than 30%, and the audio quality is significantly improved. Users can clearly hear the target sound in a noisy environment.

[0081] Adaptive filter: Specific solution: Step 1: Use the LMS algorithm to adjust the filter coefficients in real time and update the filter parameters based on the error between the input signal and the expected output.

[0082] Step 2: In a high-noise environment, the adaptive filter dynamically adjusts the filtering parameters according to environmental changes to achieve the best noise reduction effect.

[0083] Virtual results: After using the adaptive filter, the system can quickly respond to changes in environmental noise, the filtering effect is more stable and efficient, and the clarity of the audio signal is improved by more than 20%.

[0084] When processing large amounts of audio data, there may be delays, affecting real-time performance. This solution uses hardware acceleration: GPU or FPGA is introduced for audio processing to speed up the calculation speed of the deep learning model.

[0085] Parallel processing: Use multithreading or parallel computing technology to improve processing efficiency and reduce latency.

[0086] Hardware acceleration: Specific solution: Step 1: Introduce GPU or FPGA for audio processing to accelerate the calculation speed of deep learning models.

[0087] Step 2: Deploy the inference process of the deep learning model on the GPU or FPGA to utilize its parallel computing capabilities to improve processing efficiency.

[0088] Virtual result: Through hardware acceleration, the real-time performance of audio processing is significantly improved, the processing delay is reduced to 50% of the original, and the system can still run smoothly under high load.

[0089] Parallel processing: Specific solutions: Step 1: Use multi-threading technology to assign audio acquisition, preprocessing, noise reduction and other tasks to different threads for parallel execution.

[0090] Step 2: Take advantage of multi-core processors to increase overall processing speed.

[0091] Virtual results: After parallel processing, the audio data processing efficiency is improved by 40%. Multi-threaded execution enables the system to process more audio data at the same time, significantly improving processing capabilities.

[0092] The current system design is relatively fixed and difficult to adapt to the needs of different application scenarios. This solution adopts modular design: the system is divided into multiple independent modules (such as sound collection module, pre-processing module, noise reduction module, etc.), and each module can be independently developed and upgraded.

[0093] Plug-in architecture: allows users to add or remove specific functional modules as needed to improve the flexibility of the system.

[0094] Modular design: Specific solution: Step 1: Divide the system into multiple independent modules, such as sound collection module, pre-processing module, noise reduction module, etc. Step 2: Each module has independent interfaces and functions and can be developed, tested and upgraded separately.

[0095] Virtual results: The modular design greatly improves the scalability and flexibility of the system. Users can add or replace modules according to their needs. The system can quickly adapt to different application scenarios, improving user satisfaction.

[0096] Plug-in architecture: Specific solution: Step 1: Design a plug-in framework that allows users to extend system functionality by adding plug-ins.

[0097] Step 2: Each plugin is developed independently and can be added or removed at any time without affecting other parts of the system.

[0098] Virtual results: The plug-in architecture makes system function expansion simple and fast. Users can freely customize system functions according to their needs, which greatly improves the system's applicability and user experience.

[0099] Security of data storage and transmission: This solution uses data encryption: encryption algorithms are used during audio data transmission to ensure data confidentiality and integrity.

[0100] Blockchain technology: Blockchain technology is used for data storage to ensure that the data cannot be tampered with and cannot be traced.

[0101] Data encryption: Specific solution: Step 1: Use the AES encryption algorithm to encrypt the data during audio data transmission. Step 2: Use the corresponding key to decrypt the data at the receiving end to ensure the confidentiality and integrity of the data.

[0102] Virtual result: After data encryption, the data security during transmission is greatly improved. Even if the data is intercepted, it cannot be interpreted, effectively preventing data leakage and tampering.

[0103] Blockchain technology: Specific solutions: Step 1: Use blockchain technology to record and store audio data. Each piece of data has a unique hash value and timestamp.

[0104] Step 2: Once the data is written into the blockchain, it cannot be tampered with, ensuring the authenticity and integrity of the data.

[0105] Virtual result: The application of blockchain technology ensures the immutability and traceability of audio data. Users can trust the source and content of the data, and data security and credibility are significantly improved.

[0106] Audio signal processing accuracy, this solution uses a high-precision analog-to-digital converter: a high-precision analog-to-digital converter is used to ensure the accuracy of audio signal conversion.

[0107] Advanced filtering algorithms: Introduce more advanced filtering algorithms, such as Kalman filters, to improve the accuracy of signal processing.

[0108] High-precision analog-to-digital converter, specific solution: Step 1: Use a 24-bit or 32-bit high-precision analog-to-digital converter (such as TI's ADS1299) to perform analog-to-digital conversion of the audio signal.

[0109] Step 2: Ensure that the converted digital signal can accurately reflect the details of the original audio signal.

[0110] Virtual result: The use of high-precision analog-to-digital converters significantly improves the conversion accuracy of audio signals, making the audio quality clearer and more detailed, allowing users to experience higher quality audio.

[0111] Advanced filtering algorithm: Specific solution: Step 1: Introduce the Kalman filter to filter the audio signal, eliminating noise while retaining the details of the signal. Step 2: Combine the fast Fourier transform (FFT) technology to process the signal in the frequency domain to improve the filtering effect.

[0112] Virtual results: The application of advanced filtering algorithms makes audio signal processing more accurate, the filtered audio signal is clearer and more realistic, the signal-to-noise ratio is further improved, and the user experience is significantly improved.

[0113] To achieve the above object, the present invention also provides a directional sound processing system suitable for remote monitoring and having a high signal-to-noise ratio, such as Fig.10 As shown, the system specifically includes: A data acquisition and generation unit, used to acquire the original audio signal data to be processed in the environment in real time based on remote monitoring, and process the original audio signal data once to generate corresponding first audio signal data; wherein the original audio signal data is a sound signal in the environment captured by a microphone array; a filtering and converting processing unit, configured to filter and process the first audio signal data to generate corresponding second audio signal data, and convert and process the second audio signal data to generate third audio signal data corresponding to the second audio signal data; wherein the second audio signal data is audio signal data after filtering; and the third audio signal data is audio signal data after analog-to-digital conversion; An audio data noise reduction generation unit is used to combine a deep learning model to perform noise reduction processing on the third audio signal data, generate and back up fourth audio signal data corresponding to the third audio signal data; wherein the fourth audio signal data is high signal-to-noise ratio audio signal data after noise processing.

[0114] Furthermore, the data acquisition generation unit further includes: A first data processing module, configured to detect valid audio signal data according to an algorithm based on feature extraction and machine learning, and to clip irrelevant audio signal data in combination with an audio signal processing method, and retain valid audio signal data; The second data processing module is used to adjust the gain of each frequency band of the audio signal data and keep the volume stable based on the automatic gain control algorithm; And / or, the filtering conversion processing unit further includes: A third data processing module is used to detect the strength of the audio signal data using a sensor and adjust the signal data strength in combination with an amplification circuit; a fourth data processing module, configured to improve the signal-to-noise ratio of the first audio signal data and combine and process the corresponding audio signal data; And / or, the audio data noise reduction generation unit further includes: A fifth data processing module is used to extract frequency spectrum feature data corresponding to the noise, and respectively identify and classify the noise frequency spectrum feature data based on a deep learning model; The sixth data processing module is used to perform real-time denoising processing on the third audio signal data according to the noise spectrum characteristic data.

[0115] Furthermore, the filtering conversion processing unit further includes: a seventh data processing module, for searching analog sound signal data in combination with the sensor, and converting the analog signal data into digital signal data using an analog-to-digital converter; The eighth data processing module is used to process the corresponding audio signal data in the time domain and the frequency domain based on the fast Fourier transform.

[0116] In the system solution embodiment of the present invention, the method steps involved in the directional sound collection processing suitable for remote monitoring and with a high signal-to-noise ratio have been described above in detail. That is to say, the functional modules in the system are used to implement the steps or sub-steps in the above method embodiment, which will not be repeated here.

[0117] To achieve the above object, the present invention also provides a directional sound processing platform suitable for remote monitoring and having a high signal-to-noise ratio, such as Fig.11 As shown, it includes a processor, a memory, and a directional sound collection processing platform control program suitable for remote monitoring and with a high signal-to-noise ratio; wherein, the directional sound collection processing platform control program suitable for remote monitoring and with a high signal-to-noise ratio is executed by the processor, and the directional sound collection processing platform control program suitable for remote monitoring and with a high signal-to-noise ratio is stored in the memory, and the directional sound collection processing platform control program suitable for remote monitoring and with a high signal-to-noise ratio implements the steps of the directional sound collection processing method suitable for remote monitoring and with a high signal-to-noise ratio. For example: S1. Based on remote monitoring, obtain the original audio signal data to be processed in the environment in real time, and process the original audio signal data once to generate corresponding first audio signal data; wherein the original audio signal data is the sound signal in the environment captured by the microphone array; S2, filtering and processing the first audio signal data to generate corresponding second audio signal data, and converting and processing the second audio signal data to generate third audio signal data corresponding to the second audio signal data; wherein the second audio signal data is the audio signal data after filtering; and the third audio signal data is the audio signal data after analog-to-digital conversion; S3. In combination with a deep learning model, perform noise reduction processing on the third audio signal data, generate and back up fourth audio signal data corresponding to the third audio signal data; wherein the fourth audio signal data is high signal-to-noise ratio audio signal data after noise processing.

[0118] The specific details of the steps have been explained above and will not be repeated here.

[0119] In the embodiment of the present invention, the built-in processor of the directional sound collection processing platform suitable for remote monitoring and having a high signal-to-noise ratio can be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of integrated circuits packaged with the same or different functions, including one or more central processing units (CPU), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor uses various interfaces and lines to connect various components, and executes various functions and processes data of the directional sound collection processing suitable for remote monitoring and having a high signal-to-noise ratio by running or executing programs or units stored in the memory, and calling data stored in the memory; The memory is used to store program codes and various data. It is installed in a directional sound processing platform suitable for remote monitoring and with a high signal-to-noise ratio, and can achieve high-speed and automatic access to programs or data during operation.

[0120] The memory includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electronically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc memory, magnetic disk memory, magnetic tape memory, or any other computer-readable medium that can be used to carry or store data.

[0121] To achieve the above object, the present invention also provides a computer readable storage medium, such as Fig.12 As shown, the computer-readable storage medium stores a directional sound collection processing platform control program suitable for remote monitoring and with a high signal-to-noise ratio. The directional sound collection processing platform control program suitable for remote monitoring and with a high signal-to-noise ratio implements the steps of the directional sound collection processing method suitable for remote monitoring and with a high signal-to-noise ratio, for example: S1. Based on remote monitoring, obtain the original audio signal data to be processed in the environment in real time, and process the original audio signal data once to generate corresponding first audio signal data; wherein the original audio signal data is the sound signal in the environment captured by the microphone array; S2, filtering and processing the first audio signal data to generate corresponding second audio signal data, and converting and processing the second audio signal data to generate third audio signal data corresponding to the second audio signal data; wherein the second audio signal data is the audio signal data after filtering; and the third audio signal data is the audio signal data after analog-to-digital conversion; S3. In combination with a deep learning model, perform noise reduction processing on the third audio signal data, generate and back up fourth audio signal data corresponding to the third audio signal data; wherein the fourth audio signal data is high signal-to-noise ratio audio signal data after noise processing.

[0122] The specific details of the steps have been explained above and will not be repeated here.

[0123] In the description of the embodiments of the present invention, it should be noted that any process or method description in the flowchart or otherwise described herein may be understood as representing a module, fragment or portion of a code comprising one or more executable instructions for implementing steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations, in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0124] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processing module, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM).

[0125] In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0126] In an embodiment of the present invention, in order to achieve the above-mentioned purpose, the present invention further provides a chip system, wherein the chip system includes at least one processor. When the program instructions are executed in the at least one processor, the chip system executes the steps of the directional sound collection processing method suitable for remote monitoring and with a high signal-to-noise ratio, for example: S1. Based on remote monitoring, obtain the original audio signal data to be processed in the environment in real time, and process the original audio signal data once to generate corresponding first audio signal data; wherein the original audio signal data is the sound signal in the environment captured by the microphone array; S2, filtering and processing the first audio signal data to generate corresponding second audio signal data, and converting and processing the second audio signal data to generate third audio signal data corresponding to the second audio signal data; wherein the second audio signal data is the audio signal data after filtering; and the third audio signal data is the audio signal data after analog-to-digital conversion; S3. In combination with a deep learning model, perform noise reduction processing on the third audio signal data, generate and back up fourth audio signal data corresponding to the third audio signal data; wherein the fourth audio signal data is high signal-to-noise ratio audio signal data after noise processing.

[0127] The specific details of the steps have been explained above and will not be repeated here.

[0128] Those of ordinary skill 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 to be beyond the scope of this application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0129] The present invention uses a method to obtain the original audio signal data to be processed in the environment in real time based on remote monitoring, and processes the original audio signal data once to generate corresponding first audio signal data; wherein the original audio signal data is a sound signal in the environment captured by a microphone array; Filtering the first audio signal data to generate corresponding second audio signal data, and converting the second audio signal data to generate third audio signal data corresponding to the second audio signal data; wherein the second audio signal data is audio signal data after filtering; and the third audio signal data is audio signal data after analog-to-digital conversion; Combined with the deep learning model, the third audio signal data is denoised and processed, and fourth audio signal data corresponding to the third audio signal data is generated and backed up; wherein the fourth audio signal data is high signal-to-noise ratio audio signal data after noise processing, as well as the system, platform and storage medium corresponding to the method, which can effectively suppress noise and significantly improve the signal-to-noise ratio of the audio signal, thereby improving the sound collection quality; it can also prevent data tampering, ensure the authenticity and integrity of the audio data, and ensure the security of sound collection.

[0130] That is to say, in the present invention, by providing an automatic sound collection processing module and a signal preprocessing module in the system, the automatic sound collection processing module can effectively process the collected audio, including preliminary processing of noise, and the signal preprocessing module can further filter the signal to improve the signal-to-noise ratio of the audio, and further improve the audio noise reduction effect. At the same time, a noise processing module of the third process is also provided, which can automatically extract noise features and perform deep learning on them, and can quickly identify noise in the subsequent process, so as to facilitate rapid processing thereof. Through these three steps of noise processing, the system's noise processing effect can be greatly improved. At the same time, a remote monitoring module is also provided in the system, which can monitor the sound collection content in real time to avoid subsequent tampering with the sound collection content, ensure the security of sound collection, and improve the actual application effect of the overall system.

[0131] In other words, the scheme of the present invention can significantly improve the signal-to-noise ratio: by introducing advanced noise suppression algorithms and technologies, such as adaptive filters and noise reduction microphone arrays, noise is effectively suppressed, the signal-to-noise ratio of the audio signal is significantly improved, and the sound quality is improved. Adaptive filter: The adaptive filter minimizes the output error by dynamically adjusting the filter parameters, adapting to different noise environments, and thus effectively suppressing noise. Common algorithms include LMS (Least Mean Squares) and RLS (Recursive Least Squares).

[0132] Noise reduction microphone array: An array of multiple microphones is used to suppress noise signals from a specific direction while enhancing audio signals in the target direction using spatial filtering techniques such as beamforming.

[0133] Realize remote monitoring: The system integrates a remote monitoring module, which can realize real-time monitoring and management of audio collection equipment through network connection. It can not only obtain audio collection content in real time, but also prevent data tampering, ensure the authenticity and integrity of audio data, and ensure the security of audio collection. Remote monitoring module: The system integrates a remote monitoring module, which can realize real-time monitoring and management of audio collection equipment through network connection. Adopt encrypted transmission protocol to ensure the security of data during transmission. Real-time acquisition of audio collection content: Audio collection data is transmitted in real time through the network, and users can view and manage audio collection content in real time through remote terminals. Prevent data tampering: Use blockchain technology or other data integrity protection mechanisms to ensure the authenticity and integrity of audio collection data.

[0134] Modular design, enhanced scalability and flexibility: The modular design makes the system more scalable and flexible. Different modules (such as the automatic sound processing module, signal preprocessing module, noise processing module, etc.) work together and can be expanded and adjusted according to specific needs, which improves the overall performance and applicability of the system. Modular design: The system adopts a modular design, and different functional modules (such as the automatic sound processing module, signal preprocessing module, noise processing module, etc.) work independently but can cooperate with each other. Functional expansion: Users can add or upgrade different modules according to specific needs, and the system can flexibly adapt to different application scenarios.

[0135] Efficient noise processing: Through three-step noise processing (preliminary noise processing, signal filtering processing, noise feature extraction and deep learning), the system can greatly improve the noise processing effect. In particular, the noise feature extraction and deep learning module can quickly identify and process noise, thereby improving the audio noise reduction effect. Three-step noise processing: Preliminary noise processing, using an adaptive filter to perform preliminary noise reduction on the original audio signal. Signal filtering processing, selecting a suitable filter (such as low-pass, high-pass, band-pass filter) to further remove residual noise. Noise feature extraction and deep learning, extracting noise features, and using deep learning models (such as CNN or RNN) to quickly identify and process noise. In the prior art, only a single noise reduction technology is usually used, which has limited effect and is difficult to handle complex noise environments. In the scheme of the present invention, through three-step noise processing, combined with a deep learning model, it is possible to more efficiently handle complex noise environments and improve the noise reduction effect of audio signals.

[0136] Automated audio processing: The automatic audio processing module can automatically detect, edit, reduce noise, balance control and volume control the collected audio, simplifying the audio collection process and improving the efficiency and audio quality of audio collection. Among them, automatic detection: using machine learning algorithms (such as SVM, KNN, etc.) to automatically detect valid audio signals. Automatic editing: based on the characteristics of the audio signal, automatically edit irrelevant parts and retain the valid parts. Automatic noise reduction: using adaptive filters and deep learning algorithms for noise reduction. Balance control: through spectrum analysis technology, automatically adjust the gain of each frequency band of audio. Volume control: based on the automatic gain control (AGC) algorithm, keep the volume stable. In the prior art, most of them are manual operations, which are inefficient and the processing effect is unstable. In the scheme of the present invention, the automatic processing technology is introduced to simplify the audio collection process and improve the efficiency and audio quality of audio collection.

[0137] Improved system application effect: Through the comprehensive application of the above technical solutions, the system's effect in actual application has been significantly improved, which can meet the needs of high-quality audio collection and is suitable for a variety of application scenarios. In the comprehensive application of the above technologies: Through the comprehensive application of adaptive filters, remote monitoring modules, modular design, three-step noise processing, automated sound collection processing and other technologies, the system's effect in actual application has been significantly improved.

[0138] Applicable scenarios: Suitable for high-quality audio acquisition in noisy environments, such as security monitoring, traffic management, intelligent buildings and other fields. In the prior art, due to various technical defects, the system application effect is limited and cannot meet the needs of high-quality audio acquisition. In the solution of the present invention, by comprehensively applying a variety of advanced technologies, the system can meet the needs of high-quality audio acquisition, is suitable for a variety of application scenarios, and improves the practicality and performance of the system.

[0139] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A directional sound collection processing method suitable for remote monitoring and having a high signal-to-noise ratio, characterized in that: The method comprises the steps of: Based on remote monitoring, the original audio signal data to be processed in the environment is obtained in real time, and the original audio signal data is processed once to generate corresponding first audio signal data; wherein the original audio signal data is a sound signal in the environment captured by a microphone array; Filtering the first audio signal data to generate corresponding second audio signal data, and converting the second audio signal data to generate third audio signal data corresponding to the second audio signal data; wherein the second audio signal data is audio signal data after filtering; and the third audio signal data is audio signal data after analog-to-digital conversion; In combination with a deep learning model, the third audio signal data is subjected to noise reduction processing to generate and back up fourth audio signal data corresponding to the third audio signal data; wherein the fourth audio signal data is high signal-to-noise ratio audio signal data after noise processing.

2. According to claim 1, a directional sound collection processing method suitable for remote monitoring and having a high signal-to-noise ratio, characterized in that: The method of acquiring the original audio signal data to be processed in the environment in real time based on remote monitoring, and processing the original audio signal data once to generate corresponding first audio signal data, further includes: Detect valid audio signal data according to an algorithm based on feature extraction and machine learning, and edit irrelevant audio signal data in combination with an audio signal processing method to retain valid audio signal data; Adjust the gain of each frequency band of the audio signal data and keep the volume stable based on the automatic gain control algorithm.

3. According to claim 1, a directional sound collection processing method suitable for remote monitoring and having a high signal-to-noise ratio, characterized in that: The filtering process of the first audio signal data to generate corresponding second audio signal data, and the conversion process of the second audio signal data to generate third audio signal data corresponding to the second audio signal data, further includes: The sensor is used to detect the strength of the audio signal data, and the signal data strength is adjusted in combination with the amplification circuit; The signal-to-noise ratio of the first audio signal data is improved, and the corresponding audio signal data is combined and processed.

4. A directional sound collection processing method suitable for remote monitoring and having a high signal-to-noise ratio according to claim 1 or 3, characterized in that: The filtering process of the first audio signal data to generate corresponding second audio signal data, and the conversion process of the second audio signal data to generate third audio signal data corresponding to the second audio signal data, further includes: Combine sensors to search for analog sound signal data, and use analog-to-digital converters to convert the analog signal data into digital signal data; Based on Fast Fourier Transform, the corresponding audio signal data is processed in the time domain and frequency domain.

5. According to claim 1, a directional sound collection processing method suitable for remote monitoring and having a high signal-to-noise ratio, characterized in that: The method of combining the deep learning model, performing noise reduction processing on the third audio signal data, and generating and backing up fourth audio signal data corresponding to the third audio signal data, further includes: Extracting the spectral feature data corresponding to the noise, and identifying and classifying the noise spectral feature data based on the deep learning model; The third audio signal data is denoised in real time according to the noise spectrum characteristic data.

6. A directional sound processing system suitable for remote monitoring and having a high signal-to-noise ratio, characterized in that: The system is used to implement the directional sound collection processing method suitable for remote monitoring and having a high signal-to-noise ratio as described in any one of claims 1 to 5, and the system includes: A data acquisition and generation unit, used to acquire the original audio signal data to be processed in the environment in real time based on remote monitoring, and process the original audio signal data once to generate corresponding first audio signal data; wherein the original audio signal data is a sound signal in the environment captured by a microphone array; a filtering and converting processing unit, configured to filter and process the first audio signal data to generate corresponding second audio signal data, and convert and process the second audio signal data to generate third audio signal data corresponding to the second audio signal data; wherein the second audio signal data is audio signal data after filtering; and the third audio signal data is audio signal data after analog-to-digital conversion; An audio data noise reduction generation unit is used to combine a deep learning model to perform noise reduction processing on the third audio signal data, generate and back up fourth audio signal data corresponding to the third audio signal data; wherein the fourth audio signal data is high signal-to-noise ratio audio signal data after noise processing.

7. A directional sound processing system suitable for remote monitoring and having a high signal-to-noise ratio according to claim 6, characterized in that: The data acquisition generation unit further includes: A first data processing module, configured to detect valid audio signal data according to an algorithm based on feature extraction and machine learning, and to clip irrelevant audio signal data in combination with an audio signal processing method, and retain valid audio signal data; The second data processing module is used to adjust the gain of each frequency band of the audio signal data and keep the volume stable based on the automatic gain control algorithm; And / or, the filtering conversion processing unit further includes: A third data processing module is used to detect the strength of the audio signal data using a sensor and adjust the signal data strength in combination with an amplification circuit; a fourth data processing module, configured to improve the signal-to-noise ratio of the first audio signal data and combine and process the corresponding audio signal data; And / or, the audio data noise reduction generation unit further includes: A fifth data processing module is used to extract frequency spectrum feature data corresponding to the noise, and respectively identify and classify the noise frequency spectrum feature data based on a deep learning model; The sixth data processing module is used to perform real-time denoising processing on the third audio signal data according to the noise spectrum characteristic data.

8. A directional sound processing system suitable for remote monitoring and having a high signal-to-noise ratio according to claim 6 or 7, characterized in that: The filtering conversion processing unit further includes: a seventh data processing module, for searching analog sound signal data in combination with the sensor, and converting the analog signal data into digital signal data using an analog-to-digital converter; The eighth data processing module is used to process the corresponding audio signal data in the time domain and the frequency domain based on the fast Fourier transform.

9. A directional sound processing platform suitable for remote monitoring and with high signal-to-noise ratio, characterized in that: The invention comprises a processor, a memory and a directional sound collection processing platform control program suitable for remote monitoring and having a high signal-to-noise ratio; wherein the directional sound collection processing platform control program suitable for remote monitoring and having a high signal-to-noise ratio is executed by the processor, the directional sound collection processing platform control program suitable for remote monitoring and having a high signal-to-noise ratio is stored in the memory, and the directional sound collection processing platform control program suitable for remote monitoring and having a high signal-to-noise ratio implements the directional sound collection processing method suitable for remote monitoring and having a high signal-to-noise ratio as described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a directional sound collection processing platform control program suitable for remote monitoring and with a high signal-to-noise ratio. The directional sound collection processing platform control program suitable for remote monitoring and with a high signal-to-noise ratio implements a directional sound collection processing method suitable for remote monitoring and with a high signal-to-noise ratio as described in any one of claims 1 to 5.

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