Outdoor noise cancellation system and method of operation

Abstract

A noise cancellation system (100) is provided, comprising an acoustic receiving module (102) disposed at an external noise intrusion source (104) for receiving noise from an external, uncontrollable noise source, an acoustic sensor (106) for recording the received noise, a noise model construction module (108) for constructing a plurality of artificial intelligence models based on a plurality of noise data definitions pre-stored in a database (110), an analysis module (114) for classifying and analyzing the recorded noise and determining its intensity by using the plurality of artificial intelligence models, a sound wave emission module (116) for emitting a series of inverse sound waves and amplitudes based on the amplitude of the noise, and a user interface (118) for detecting long-term intermittent noise, allowing the user to control the intensity of noise cancellation, and notifying the user that noise indicating an emergency has been detected.

Description

[Technical Field]

[0001] FIELD OF THE DISCLOSURE Embodiments of the present disclosure relate to the field of electronics, and more particularly to noise cancellation systems and methods of operation thereof. [Background technology]

[0002] Many public places, homes, and corporate offices have open environments, where environmental noise pollution is a major problem. Common noise sources include roads and highways, aircraft, industrial facilities, factories and manufacturing plants, air conditioners, pool equipment, etc. One drawback of an open office environment is the difficulty of working or making phone calls in a noisy environment. Noise from the environment interferes with the ability to hear speakers. A noisy environment also interferes with the ability to speak clearly amid other noise sources. Summary of the Invention [Problem to be solved by the invention]

[0003] Alternatively, workers are forced to move to a quiet environment free of noise elements. However, such spaces may be limited. Reducing environmental noise requires noise reduction technology. Noise control technologies such as earplugs, thick walls, and acoustic ceiling tiles are undesirable in many situations because they can be uncomfortable, bulky, unsightly, or ineffective in situations where noise cancellation or suppression is desired. However, conventional active noise cancellation technologies have limited applicability. Conventional active noise cancellation technologies are only suitable for very small, enclosed spaces, such as headphones, or for continuous, low-frequency sounds, such as machinery noise. Furthermore, noise-canceling headphones cannot be used on newborns or noise-sensitive people who cannot wear headphones for physical or medical reasons. Furthermore, due in part to their reliance on complex signal processing algorithms, conventional technologies are limited to actively canceling noise only in a narrow, predictable frequency range.

[0004] For these reasons, existing active noise cancellation technologies are ineffective in many situations where noise reduction is desired. There is a need for a noise cancellation system that is not limited to the ear and can be freely placed like a speaker. There is also a need for a noise cancellation system that can be controlled, adjusted, and modified through a wireless system like a cell phone. There is also a need for a system that does not cancel noise by identifying emergency alarm sounds like smoke detectors.

[0005] Therefore, there is a need for a noise cancellation system and method of operation that addresses the above-mentioned problems. [Means for solving the problem]

[0006] According to one embodiment of the present disclosure, there is provided a noise cancellation system that cancels noise by emitting sound waves to cancel and nullify the paths of sound particles. The noise cancellation system includes an acoustic receiving module, a noise model construction module, an analysis module, an acoustic wave emission module, and a user interface. The acoustic receiving module is disposed at an external noise intrusion source and configured to receive noise from the external uncontrollable noise source. The acoustic receiving module includes an acoustic sensor that records the noise received from the external uncontrollable noise source. The noise model construction module is configured to construct a plurality of artificial intelligence models based on a plurality of noise data definitions pre-stored in a database. The plurality of noise data definitions are a set of frequently occurring noise records. The noise model construction module stores the plurality of artificial intelligence models in a model repository. The analysis module is operatively coupled to the acoustic receiving module and the noise model construction module. The analysis module is configured to classify and analyze the recorded noise by using the plurality of artificial intelligence models. The analysis of the recorded noise includes comparing the noise with a plurality of sound wave data stored in the database, and determining the intensity of the recorded noise based on the analysis of the plurality of sound wave data. The acoustic wave emission module is operatively coupled to the analysis module. The acoustic wave emission module is configured to emit a series of inverse acoustic waves and amplitudes based on the amplitude of the noise recorded by the acoustic sensor (106), thereby canceling the received noise. The series of inverse acoustic waves deforms the travel paths of a plurality of noise particles. The user interface is operatively coupled to the analysis module and the acoustic wave emission module. The user interface is configured to detect long-term intermittent noise and allow a user to control the intensity of the noise cancellation. The user interface is also configured to notify the user that noise indicating an emergency has been detected.

[0007] According to another embodiment, a method for operating a noise cancellation system is provided. The method includes receiving noise from an external uncontrollable noise source using an acoustic receiving module and recording the received noise from the external uncontrollable noise source using an acoustic sensor. The method also includes constructing, by a noise model construction module, a plurality of artificial intelligence models based on a plurality of noise data definitions pre-stored in a database, the plurality of noise data definitions being a set of artificial intelligence models of frequent noise recordings. The method also includes storing, by the noise model construction module, the plurality of artificial intelligence models in a model repository. The method also includes analyzing, by an analysis module, the recorded noise using the plurality of artificial intelligence models, the analysis of the recorded noise comprising comparing the noise with a plurality of sound wave data stored in the database. The method also includes determining, by the analysis module, an intensity of the recorded noise based on the analysis of the plurality of sound wave data. The method also includes emitting, by an acoustic wave emission module, a series of inverse sound waves and amplitudes based on the amplitude of the noise recorded by the acoustic sensor, thereby canceling the received noise, and the series of inverse sound waves modifying the travel paths of the plurality of noise sound particles. The method also includes detecting, via a user interface, the long-term intermittent noise and allowing a user to control the intensity of the noise cancellation. The method also includes notifying, via the user interface, the user that a noise indicative of an emergency has been detected.

[0008] To further clarify the advantages and features of the present disclosure, a detailed description of the present disclosure will be provided below with reference to specific embodiments illustrated in the accompanying drawings. It should be noted that these drawings only illustrate typical embodiments of the present disclosure and are therefore not intended to limit the scope. The present disclosure will be described and explained with more specificity and detail using the accompanying drawings. [Brief explanation of the drawings]

[0009] The disclosure will be described and explained with more specific details using the accompanying drawings.

[0010] [Figure 1] FIG. 1 is a block diagram illustrating a noise cancellation system according to an embodiment of the present disclosure.

[0011] [Figure 2] FIG. 2 is a block diagram illustrating another embodiment of the noise cancellation system of FIG. 1 in accordance with an embodiment of the present disclosure.

[0012] [Figure 3] FIG. 3 is a block diagram illustrating one embodiment of an architecture for the noise cancellation system of FIG. 1 in accordance with an embodiment of the present disclosure.

[0013] [Figure 4a] FIG. 4a is a diagram illustrating an embodiment of a modification of the acoustic wave of FIG. 1 according to an embodiment of the present disclosure.

[0014] [Figure 4b] FIG. 4b is a graphical representation of an exemplary embodiment of the acoustic wave of FIG. 1 in accordance with an embodiment of the present disclosure.

[0015] [Figure 5] FIG. 5 is a block diagram of a computer or server of a noise cancellation system according to an embodiment of the present disclosure.

[0016] [Figure 6] FIG. 6 is a flowchart showing the steps in the method of operation of the noise cancellation system.

[0017] Those skilled in the art will also understand that elements in the figures are illustrated for simplicity and have not necessarily been drawn to scale. Furthermore, the configuration of a device may be represented in the figures by conventional symbols for one or more of the device's components, and the figures may show only specific details relevant to understanding the embodiments of the present disclosure so as not to obscure the figures with details readily understandable to one skilled in the art having the benefit of the description herein. DETAILED DESCRIPTION OF THE INVENTION

[0018] To promote understanding of the principles of the disclosure, the principles will now be described with reference to embodiments illustrated in the drawings and using specific language. It should be understood, however, that no limitation on the scope of the disclosure is intended. Such modifications and further variations of the illustrated system, and further applications of the principles of the disclosure that would normally occur to one skilled in the art, are to be construed as falling within the scope of the present disclosure.

[0019] The use of "comprises," "comprising," or other similar terms is intended to be non-exclusive, and a process or method comprising a list of steps does not have only those steps, but may include other steps not expressly listed or inherent in such process or method. Similarly, the presence of one or more devices, subsystems, elements, structures, or components preceding "comprises..." does not, without further constraint, exclude the presence of other devices, subsystems, elements, structures, components, additional devices, additional subsystems, additional elements, additional structures, or additional components. The appearances of "in one embodiment," "in another embodiment," and similar phrases throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. The systems, methods, and examples described herein are illustrative only and are not intended to be limiting.

[0021] In the following specification and claims, reference will be made to plural terms, which shall be defined to have the following meanings: The singular forms "a," "an," and "the" include the plural forms unless the context clearly dictates otherwise.

[0022] An embodiment of the present disclosure provides a noise cancellation system that cancels noise by emitting sound waves to deform the travel path of sound particles. The noise cancellation system includes an acoustic receiving module disposed at an external noise intrusion source and configured to receive noise from the external uncontrollable noise source. The acoustic receiving module includes an acoustic sensor that records the noise received from the external uncontrollable noise source. The noise model construction module is configured to construct multiple artificial intelligence models based on multiple noise data definitions pre-stored in a database. The multiple noise data definitions are a set of frequently occurring noise records. The noise model construction module stores the multiple artificial intelligence models in a model repository. An analysis module is operatively coupled to the acoustic receiving module and the noise model construction module. The analysis module is configured to classify and analyze the recorded noise by using the multiple artificial intelligence models. The analysis of the recorded noise includes comparing the noise with multiple sound wave data stored in the database, and determining the intensity of the recorded noise based on the analysis of the multiple sound wave data. The acoustic wave emission module and a user interface are operatively coupled to the analysis module. The acoustic wave emission module is configured to emit a series of inverse acoustic waves and amplitudes based on the amplitude of the noise recorded by the acoustic sensor, thereby canceling the received noise. The series of inverse acoustic waves deforms the travel paths of a plurality of noise acoustic wave particles. A user interface is operably coupled to the analysis module and the acoustic wave emission module. The user interface is configured to detect long-term intermittent noise and allow a user to control the intensity of the noise cancellation. The user interface is also configured to notify the user that noise indicating an emergency has been detected.

[0023] As used herein, the term "noise cancellation" refers to the use of active noise control to reduce unwanted ambient sounds. Furthermore, the system described below in Figure 1 is a noise cancellation system and method of operation.

[0024] FIG. 1 is a block diagram illustrating a noise cancellation system (100) according to an embodiment of the present disclosure. The noise cancellation system (100) cancels noise by emitting sound waves to deform the travel path of sound particles. The noise cancellation system (100) includes an acoustic receiver module (102), a noise model construction module (108), an analysis module (114), a sound wave emission module (116), and a user interface (118). The acoustic receiver module (102) is disposed at an external noise intrusion source (104) and is configured to receive noise from the external uncontrollable noise source (104). The acoustic receiver module (102) includes an acoustic sensor (106) that records the noise received from the external uncontrollable noise source (104).

[0025] The noise model construction module (108) is configured to construct a plurality of artificial intelligence models based on a plurality of noise data definitions pre-stored in a database (110). The plurality of noise data definitions are sets of frequently occurring noise records. The noise model construction module (108) stores the plurality of artificial intelligence models in a model repository (112). In one embodiment, the artificial intelligence updates the model repository (112) as different noise data are generated.

[0026] The analysis module 114 is operatively coupled to the acoustic receiving module 102 and the noise model building module 108. The analysis module 114 is configured to classify and analyze the recorded noise by using a plurality of artificial intelligence models. The analysis of the recorded noise includes comparing the noise with a plurality of acoustic data stored in the database 110. The analysis module 114 is configured to determine the intensity of the recorded noise based on the analysis of the plurality of acoustic data.

[0027] The acoustic wave emission module (116) is operatively coupled to the analysis module (114), and the acoustic wave emission module (116) is configured to emit a series of inverse acoustic waves and amplitudes based on the amplitude of the noise recorded by the acoustic sensor (106), thereby canceling the received noise, and the series of inverse acoustic waves modify the travel paths of a plurality of noise acoustic wave particles. In one embodiment, a user may be able to use the mobile application interface to reduce external noise instead of canceling the noise entirely.

[0028] A user interface 118 is operatively coupled to the analysis module 114 and the acoustic emission module 116. The user interface 118 is configured to detect long periods of intermittent noise, allow a user to control the intensity of noise cancellation, and notify the user that a noise indicative of an emergency has been detected. In one embodiment, the user interface 118 comprises a mobile application interface.

[0029] In one embodiment, noises are recorded for future analysis and stored in a central database, which is then used as a training set by an artificial intelligence model. In another embodiment, the recorded noises are stored in the user's local database. In one embodiment, noises may be pre-stored in the noise cancellation system 100 based on the user's location. For example, if the user lives near a train station or a school, relevant noises are categorized and stored. If a new, uncategorized type of noise is recognized, the sound is automatically classified. The noise cancellation device 100 notifies the user of the user device of the arrival of new noises. The incoming new sound waves are canceled or reduced according to the user's request.

[0030] Figure 2 is a block diagram illustrating another embodiment of the noise cancellation system (100) of Figure 1 in accordance with an embodiment of the present disclosure. The noise cancellation system (100) cancels noise by emitting sound waves to deform the travel path of sound particles. The noise cancellation system (100) includes an acoustic receiving module (102), a noise model construction module (108), an analysis module (114), a sound wave emitting module (116), and a user interface (118). The acoustic receiving module (102) is disposed at an external noise intrusion source (104) and is configured to receive noise from the external uncontrollable noise source (104). The acoustic receiving module (102) includes an acoustic sensor (106) that records the noise received from the external uncontrollable noise source (104).

[0031] In one embodiment, the noise cancellation system includes a decibel meter (120) operably coupled to the analysis module (114) and configured to record residual sound after cancellation. The residual sound excludes human voices and speech. In one embodiment, the noise intensity is calculated by the decibel meter (120) and then controlled by the user interface (118). In one embodiment, the noise cancellation system (100) includes an artificial intelligence module (122) configured to store multiple artificial intelligence models and multiple machine learning models. The sound wave data is trained by one of the multiple machine learning models. In one embodiment, the noise cancellation system (100) also includes a notification module (124) configured to notify a user of an emergency based on the analyzed noise. In one embodiment, the noise cancellation system (100) is configured to be installed on a digital device from which the expected noise is received. In one embodiment, the noise cancellation system (100) may be installed to service various audio devices, such as Bluetooth® audio devices, sound bars, headphones, and EarPods®.

[0032] In one embodiment, the user interface 118 is enabled by artificial intelligence to display the stored recorded noise intensities, allowing the user to control the intensity of the noise cancellation. In another embodiment, the frequently occurring noise data stored in the repository 112 is available to the noise cancellation system 100 to enhance the user experience.

[0033] In one embodiment, the noise cancellation device 100 can control multiple types of noise during a busy day. In one embodiment, the noise cancellation system 100 can identify known noises, such as train, airplane, etc. noises, that are pre-stored in the system based on the user's location. In another embodiment, the noise cancellation system 100 also identifies unknown noises that are not stored in the system's database 110. In one embodiment, edge deployment of known noises is performed from the cloud to the device. In one embodiment, examples of multiple types of noise are as follows: Continuous noise: Continuous noises come from objects or machines that operate without interruption. These noises are noticeable and occur throughout the environment. For example, inside a car, a continuous noise can be heard from the engine. Intermittent noise: Intermittent noise occurs infrequently but regularly in everyday life. Intermittent noise is a sudden, loud sound that you notice but don't startle. For example, someone who lives near an airport can constantly hear the sounds of planes taking off and landing. Intermittent noise is loud, but doesn't startle you. Burst noises: Burst noises do not occur or are not noticed regularly. Rather, they are like sudden explosions or annoying sounds. For example, when a waiter drops a plate, the restaurant usually gets a little quiet as people look around for the source of the noise (104). Low-frequency noise: Low-frequency noise comes from objects in our everyday environment. Even a seemingly quiet room can consistently register a sound level of around 30-40 decibels. For example, in an office environment, noise comes from heating or ventilation systems, and in a home, noise comes from the ticking of a clock. Complex noise: Complex noise may be a combination of the four types of noise mentioned above. It is also a new type of noise that will be classified in the future using artificial intelligence (AI) annotation and classification methods. AI annotation automatically identifies and labels objects in images, videos, and text. Classification methods allow for annotation of the entire image.

[0034] In one embodiment, the noise cancellation system 100 includes a command module configured to issue commands using voice-enabled instructions. In one embodiment, the commands are "reduce noise," "nullify noise," "annotate noise," etc.

[0035] FIG. 3 is a block diagram illustrating an example architecture of the noise cancellation system of FIG. 1 according to an embodiment of the present disclosure. In one embodiment, a central noise model construction device (128) constructs noise based on a central noise definition database (126). In one embodiment, when a new, unclassified type of noise is recognized, the new noise is recorded in the database (110). Classification of the new noise is automatic. The noise cancellation device (100) notifies the user of the user device of the newly arriving noise. The incoming new sound waves are canceled or reduced according to the user's request. In one embodiment, the noise cancellation system (100) includes a control center (130). In one embodiment, the control center (130) identifies sound waves in a mobile application, and the system notifies the user of the new sound waves and suggests corresponding opposite-phase sound waves. If desired, the user can use the opposite-phase sound waves suggested by the mobile application's artificial intelligence algorithm to cancel the noise, after which the opposite-phase sound waves are emitted into the environment. The emitted opposite-phase sound waves cancel the newly recorded noise.

[0036] In one embodiment, the incoming noise is analyzed by an analysis module 114. In one embodiment, the analysis module 114 includes an AI noise type classifier 132, an AI noise mathematical equation generator 134, and an AI radiated wave modeler 136. The AI ​​noise mathematical equation generator 134 generates an equation based on the noise received from the AI ​​noise type classifier 132, and the AI ​​radiated wave modeler 136 determines the amount of emitted sound waves based on the equation. o AI Noise Type Classifier is an AI model that uses artificial intelligence to classify a heard noise into one of the identified noise types based on knowledge from a central noise model database (126). o The AI ​​noise mathematical equation or AI model creation process generates mathematical equations using the sound wave data from the AI ​​noise type classifier (132). o The AI ​​Wave Emission Modeler (136) reverses the mathematical equations or AI models generated by the AI ​​Noise Math Equations to generate sound waves that are the inverse of the original noise waves. The Noise Wave Emission Module (118) primarily converts the inverse sound emissions generated by the AI ​​Radiation Wave Modeler into inverse sound waves, which are then re-emitted into the surrounding environment to cancel out sound waves in the environment.

[0037] The noise model building module (108) comprises: o The Unique Noise Definition Database is a database of the most frequent noise records (5 or 10) that may be stored locally on the user's system. The AI ​​Continuous Novel Noise Learner acts as an observer, recording new noises identified by the learner. This can also be used if humans want to manually annotate ambient sounds as noise. This acts as a mechanism to learn new noises with a human in-loop. The Mobile Application Interface Communicator is the interface for communication between the home device and the mobile app, which itself is the interface for the end user to customize requests to the device such as changing the strength of noise cancellation, changing settings, etc. o The central noise definition database (126) is a cloud database that stores each and every observation recorded from all individual devices. ● A central AI noise model builder (128) generates various AI models based on noise data definitions available in the central noise definition database (126) and stores the models in a model repository (112) available in the central noise definition database (126). These models are eventually pushed to home devices through edge AI deployment for use in noise classification.

[0038] 4a is a graphical illustration of an exemplary embodiment of the deformation of the sound waves of FIG. 1 according to an embodiment of the present disclosure, and FIG. 4b is a graphical illustration of an exemplary embodiment of the sound waves of FIG. 1 according to an embodiment of the present disclosure. Consider a non-limiting example of the operation of noise cancellation system (100). When sound wave "a)" is generated, noise cancellation system (100) may understand and distinguish between the sound categories described above. Sound wave emitting module (116) emits sound wave b). In this case, both sound waves cancel each other out, leaving the user with silence.

[0039] In one embodiment, if a user desires to reduce the amount of sound / noise without completely canceling the sound, the user may communicate this desire to the system through the user interface 118. The user interface 118 may have an option to modify the request to attenuate the sound waves without completely canceling them. The noise cancellation system 100 transmits a signal of relatively low amplitude and in antiphase to the sound waves to cancel only a small portion of the total noise and reduce the intensity of the sound waves. The intensity of the sound waves may be controlled, adjusted, and changed through the user device, functioning seamlessly.

[0040] In one embodiment, artificial intelligence is also incorporated into the system to perform tasks such as detecting long periods of intermittent noise, adjusting without instruction, and detecting specific types of noise. Because the noise reduction system 100 recognizes and classifies sounds, it can also identify important sounds, such as fire alarms. In some situations, when the user is not at home, the system may notify the user interface 118 that the system has detected a fire alarm. In one embodiment, the system may be used in emergency situations such as fires, and training is performed with the highest quality, without compromising the quality of either the training or test data, to ensure the software does not send false alarm notifications.

[0041] In one embodiment, if the user device identifies a sound wave and the user approves control or cancellation of the sound wave, the noise cancellation system injects a counter wave into the environment to cancel the noise. In one embodiment, the noise cancellation system (100) includes a sound listener microphone positioned at the noise source (104), which may be a window, door, etc.

[0042] 5 is a block diagram of a computer or server (300) of a noise cancellation system (100) according to an embodiment of the present disclosure. In one embodiment, the computer may be a home device or a digital appliance. The server (300) includes a processor (302) and a memory (306) operably coupled to a bus (304).

[0043] As used herein, processor (302) refers to any type of computational circuitry, including, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or other types of processing circuitry or combinations thereof.

[0044] As used herein, bus (304) refers to an internal memory channel or computer network used to connect computer components and transfer data between them. Bus (304) includes a serial bus or a parallel bus, where a serial bus transmits data in a bit-serial format and a parallel bus transmits data over multiple wires. As used herein, bus (304) includes, but is not limited to, a system bus, an internal bus, an external bus, an expansion bus, a front-side bus, a back-side bus, etc.

[0045] The memory (306) includes a number of subsystems and modules stored in the form of executable programs that cause the processor (302) to perform the method steps shown in Figure 1. The memory (306) is substantially similar to the noise cancellation system (100) of Figure 1. The memory (306) includes sub-modules: an acoustic receiving module (102), a noise model building module (108), an analysis module (114), and an acoustic wave emitting module (116).

[0046] The noise model construction module (108) is configured to construct a plurality of artificial intelligence models based on a plurality of noise data definitions pre-stored in a database (110). The plurality of noise data definitions are sets of frequently occurring noise records. The noise model construction module (108) stores the plurality of artificial intelligence models in a model repository (112).

[0047] The analysis module 114 is operatively coupled to the acoustic receiving module 102 and the noise model building module 108. The analysis module 114 is configured to classify and analyze the recorded noise by using a plurality of artificial intelligence models. The analysis of the recorded noise includes comparing the noise with a plurality of acoustic data stored in the database 110. The analysis module 114 is configured to determine the intensity of the recorded noise based on the analysis of the plurality of acoustic data.

[0048] The acoustic wave emission module 116 is operatively coupled to the analysis module 114, and the acoustic wave emission module 116 is configured to emit a series of counter acoustic waves and amplitudes based on the amplitude of the noise recorded by the acoustic sensor 106, thereby canceling the received noise. The series of counter acoustic waves distorts the travel paths of a plurality of noise acoustic wave particles.

[0049] The computer memory elements may include any suitable memory device(s) for storing data and executable programs, such as read-only memory, random-access memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, hard drives, removable media drives that handle memory cards, etc. Embodiments of the present subject matter may be implemented in combination with program modules, including functions, procedures, data structures, and application programs that perform tasks or define abstract data types or low-level hardware contexts. Executable programs stored on any of the above-mentioned storage media may be executable by the processor (302).

[0050] FIG. 6 is a flowchart illustrating steps of a method (200) of operating a noise cancellation system (100) according to an embodiment of the present disclosure. The method (200) includes, in step (202), receiving noise from an external uncontrollable noise source with an acoustic receiver module and recording the received noise from the external uncontrollable noise source with an acoustic sensor. The method also includes recording residual sound after cancellation with a decibel meter. The residual sound excludes human voices and speech. The method also includes calculating noise intensity with the decibel meter and controlling with a user interface. The method also includes storing, with an artificial intelligence module, a plurality of artificial intelligence models and a plurality of machine learning models. The plurality of sound wave data is trained by one of the plurality of machine learning models. The method also includes, with a notification module, notifying a user of an emergency based on the analyzed noise. In one embodiment, the noise cancellation system is configured to be installed in a digital system receiving the expected noise.

[0051] The method also includes, in step (204), constructing, by a noise model construction module, a plurality of artificial intelligence models based on a plurality of noise data definitions pre-stored in a database, the plurality of noise data definitions being a set of artificial intelligence models of frequent noise records. The method also includes using the database as a training data set by the artificial intelligence models. In another embodiment, the recorded noises are stored in a user's local database.

[0052] The method also includes storing, by the noise model building module, the plurality of artificial intelligence models in a model repository at step 206. The method also includes updating, by the artificial intelligence module, the model repository with various occurrences of noise data.

[0053] The method also includes, in step (208), analyzing the recorded noise using a plurality of artificial intelligence models by the analysis module, where the analysis of the recorded noise comprises comparing the noise with a plurality of acoustic data stored in a database.

[0054] The method also includes, at step 210, determining, by the analysis module, an intensity of the recorded noise based on an analysis of the plurality of acoustic wave data. The method also includes analyzing the acoustic waves based on a database that may store the recorded acoustic waves of the noise.

[0055] The method also includes, in step (212), emitting, by the acoustic wave emission module, a series of inverse acoustic waves and amplitudes based on the amplitude of the noise recorded by the acoustic sensor, thereby canceling the received noise, and the series of inverse acoustic waves deforming the travel paths of the plurality of noise sound particles.

[0056] The method also includes detecting long-term intermittent noise and allowing a user to control the intensity of noise cancellation through a user interface in step 214. The method also includes displaying the saved recorded noise intensity, allowing a user to control the intensity of noise cancellation.

[0057] The method also includes notifying a user via a user interface that a noise indicative of an emergency has been detected at step 216. The method also includes notifying using wireless communication. The method also includes notifying a user of the emergency and suggesting that the user control noise cancellation.

[0058] In one embodiment, the steps for recognizing various sounds by the artificial intelligence module (122) are as follows. - Recording the sound with a sound recognition system that can perform recognition through sensors that can detect sound. This step allows the model to build a foundation for further processing of the sound, such as classifying it into sound types. - Analyze the sound recording. This means comparing the recorded sound with the data the model was trained on. The better the training process and the higher the quality of the data, the faster and more accurate the predictions will be. - Provides an interpretation of the type of sound recorded and an output of the intensity using a decibel meter to provide the appropriate sound wave output. The system also stores the recorded sounds in a database (110) so that similar sounds can later be referenced using classification methods to pinpoint the sound source (104), sound type, etc. This helps the system stay up-to-date with sounds and does not have to rely on training data. The output can use the recorded sounds as a reference point.

[0059] Various embodiments of the present disclosure allow users to hear clearly by canceling uncontrollable environmental noise. The noise cancellation system disclosed in the present disclosure reduces necessary environmental noise. The present disclosure suppresses unpleasant, loud, harsh, or unwanted noise to provide user comfort. The system disclosed in the present disclosure is not limited in its applicability. The system disclosed in the present disclosure is suitable for large spaces, even where low- or high-frequency sounds, such as mechanical noise, are present. The noise cancellation system disclosed in the present disclosure allows users to avoid noise in critical medical situations, such as bedridden patients, when they cannot wear headphones. Furthermore, the system includes a wide range of predictable and unpredictable frequencies.

[0060] Additionally, the noise cancellation system of the present disclosure does not cancel noise by identifying emergency alarm sounds such as fire alarms. The system disclosed in the present disclosure is not limited to the ear and can be freely installed in any open space. The noise cancellation system may also be controlled, adjusted, and modified via a wireless device. Wireless devices include remote controls, cell phones, etc. Because noise is essentially sound waves, the noise cancellation system of the present disclosure cancels or reduces noise even when it is not detected. The noise cancellation system improves as more customers purchase the system. Through AI, the noise cancellation system learns and records more data due to a common cloud database. Any device may access the cloud database, which is updated each time a new sound or noise pattern is recognized.

[0061] While specific language has been used to illustrate the disclosure, no limitation is intended thereon. Those skilled in the art will appreciate that various practical modifications may be made to the methods for implementing the inventive concepts taught herein.

[0062] The drawings and the above description illustrate examples of embodiments. Those skilled in the art will understand that one or more of the described elements may be combined into a single functional element. Alternatively, certain elements may be divided into multiple functional elements. Elements of one embodiment may be added to other embodiments. For example, the order of processes described herein may be changed and is not limited to the methods described herein. Furthermore, the operations in any flow diagram need not be performed in the order shown, and not all operations are necessarily performed. Furthermore, operations that are independent of other operations may be performed in parallel with other operations. The scope of the embodiments is not limited by these specific examples.

Claims

1. 1. An outdoor noise cancellation system (100) for canceling noise by emitting sound waves to cancel and nullify the paths of sound particles, comprising: an acoustic receiving module (102) arranged at an external noise intrusion source (104) and configured to receive noise from the external uncontrollable noise source (104), the acoustic receiving module including an acoustic sensor (106) for recording the noise received from the external uncontrollable noise source (104); a noise model construction module (108) configured to construct a plurality of artificial intelligence models based on a plurality of noise data definitions pre-stored in a database (110), the plurality of noise data definitions being a set of frequently occurring noise records; a noise model building module (108) that stores a plurality of artificial intelligence models in a model repository (112); an analysis module (114) operatively coupled to the acoustic receiving module (102) and the noise model building module (108), comprising: classifying and analyzing the recorded noise by using the plurality of artificial intelligence models, wherein the analysis of the recorded noise comprises comparing the noise with a plurality of acoustic wave data stored in the database (110); an analysis module (114) configured to determine an intensity of the recorded noise based on an analysis of the plurality of acoustic wave data; an acoustic wave emission module (116) operatively coupled to the analysis module (114), comprising: an acoustic wave emitting module (116) configured to emit a series of counter acoustic waves and amplitudes based on the amplitude of the noise recorded by the acoustic sensor (106), thereby canceling the received noise, the series of counter acoustic waves distorting the travel paths of a plurality of noise sound particles; a user interface (118) operatively coupled to the analysis module (114) and the acoustic wave emission module (116), Detects long periods of intermittent noise and allows the user to control the strength of noise cancellation; a user interface (118) configured to notify a user that a noise indicative of an emergency has been detected; A noise cancellation system (100) comprising:

2. 10. The noise cancellation system of claim 1, further comprising a decibel meter operatively coupled to the analysis module and configured to record residual sound after cancellation, the residual sound excluding human voice and speech.

3. 3. The noise cancellation system (100) of claim 2, wherein the intensity of the noise is calculated by the decibel meter (120) and then controlled by the user interface (118).

4. 10. The noise cancellation system of claim 1, further comprising an artificial intelligence module configured to store a plurality of artificial intelligence models and a plurality of machine learning models, wherein the plurality of sound wave data is trained by one of the plurality of machine learning models.

5. The noise cancellation system (100) of claim 1, wherein the recorded noise is stored in a database (110) and subsequently used by an artificial intelligence model as a training set.

6. The noise cancellation system (100) of claim 1, wherein the recorded noise is stored in a user's local database.

7. The noise cancellation system (100) of claim 1, comprising a notification module (124) configured to notify a user of an emergency based on the analyzed noise.

8. The noise cancellation system (100) of claim 1 configured to be installed in a digital device that receives the expected noise.

9. 10. The noise cancellation system of claim 1, wherein the user interface is enabled by artificial intelligence to display the recorded noise intensity that is saved, the display allowing a user to control the intensity of noise cancellation.

10. A method (200) of operating an outdoor noise cancellation system, comprising: receiving noise from an external uncontrollable noise source with an acoustic receiving module and recording the noise received from the external uncontrollable noise source with an acoustic sensor (202); A noise model construction module constructs a plurality of artificial intelligence models based on a plurality of noise data definitions pre-stored in a database, the plurality of noise data definitions being a set of artificial intelligence models of frequent noise records (204); Storing (206) a plurality of artificial intelligence models in a model repository by a noise model building module; analyzing, by an analysis module, the recorded noise using a plurality of artificial intelligence models, wherein the analysis of the recorded noise comprises comparing the noise with a plurality of acoustic data stored in a database (208); determining (210) by an analysis module an intensity of the recorded noise based on an analysis of the plurality of acoustic wave data; emitting a series of counter acoustic waves and amplitudes by an acoustic wave emission module based on the amplitude of the noise recorded by the acoustic sensor, thereby canceling the received noise, and the series of counter acoustic waves modifying the travel paths of a plurality of noise sound particles (212); A user interface detects long-term intermittent noise and allows the user to control the intensity of noise cancellation (214); notifying a user via a user interface that a noise indicative of an emergency has been detected (216); A method for providing the above.

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