Computer-based method, system and program for selectively enabling sound in a noise-canceling headset (Selectively enabling sound in a noise-canceling headset for industrial work environments)

Abstract

To provide a computer-based method, computer system, and computer program for allowing selective sounds within a noise cancellation headset in an industrial work environment (selective allowance of sounds in the noise cancellation headset in the industrial work environment).SOLUTION: A method for allowing a sound to be heard within a noise cancellation headset of a user comprises: receiving a sound from a noise-filled environment, analyzing the sound to determine a problem arising within the environment, and identifying the severity of the problem; and identifying a user within a boundary range of the problem. The boundary range is based, in part, on the severity of the problem. The user wears a noise cancellation headset which actively cancels sounds of the noise-filled environment.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates generally to the field of computing, and more particularly to the field of active noise control. [Background technology]

[0002] Active noise control (ANC), also known as noise cancellation (NC) or active noise reduction (ANR), is a method for reducing unwanted sounds by adding a second sound specifically designed to eliminate them. Sound is a pressure wave consisting of alternating periods of compression and spallation. A noise-canceling speaker emits sound waves with the same amplitude but inverted phase compared to the original sound (e.g., the unwanted sound). In a process called interference, the waves combine to form new waves that effectively cancel each other out. This effect is called destructive interference. Modern ANC is generally achieved using analog circuits or digital signal processing. An adaptive algorithm is designed to analyze the waveform of the background noise and then generate a signal that either shifts the phase or inverts the polarity of the original signal. This inverted signal is amplified, and a transducer creates a sound wave that is directly proportional to the amplitude of the original waveform, thereby creating destructive interference and effectively reducing the perceived volume of the noise. Noise-canceling headphones are headphones that use ANC to reduce undesirable or unsafe sound levels. For example, in the context of an industrial work environment, an employee's headset may use ANC to reduce the risk of noise-induced hearing loss (NIHL). Summary of the Invention [Problem to be solved by the invention]

[0003] When noise-canceling headsets are utilized by employees in an industrial work environment to eliminate all noise, employees present in the environment are unable to respond to the sounds by taking immediate corrective action (e.g., shutting down, repairing) or evacuating the work environment. [Means for solving the problem]

[0004] According to one embodiment, there are provided a method, computer system, and computer program product for enabling selective sound in a noise-canceling headset. The embodiment may include receiving sound from a noise-filled environment. The source of the sound is a machine in the noise-filled environment. The embodiment may include determining that the sound is indicative of a problem in the noise-filled environment. The embodiment may include identifying a severity of the problem. The embodiment may include identifying a user within a boundary range of the problem. The boundary range is based in part on the severity of the problem. The user is wearing a noise-canceling headset that actively cancels sound from the noise-filled environment. The embodiment may include enabling the sound to be heard in the noise-canceling headset of the identified user. [Brief explanation of the drawings]

[0005] These and other objects, features, and advantages of the present invention will become apparent from the following detailed description of exemplary embodiments of the invention, which should be read in conjunction with the accompanying drawings, in which various features are not drawn to scale, as the illustrations are for clarity in facilitating those skilled in the art to understand the invention in relation to the detailed description. The drawings are as follows:

[0006] [Figure 1] 1 illustrates an exemplary networked computing environment in accordance with at least one embodiment.

[0007] [Figure 2] 1 illustrates an operational flowchart for a work environment digital twin creation and sound classification process according to at least one embodiment.

[0008] [Figure 3]1 illustrates an operational flowchart for selectively enabling sound to be heard in a noise-canceling headset in a selective sound authorization process, according to at least one embodiment.

[0009] [Figure 4] FIG. 2 is a functional block diagram of the internal and external components of the computer and server shown in FIG. 1, according to at least one embodiment.

[0010] [Figure 5] 1 illustrates a cloud computing environment according to one embodiment of the present invention.

[0011] [Figure 6] 1 illustrates an abstract model layer according to one embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0012] Although detailed embodiments of the claimed structures and methods are disclosed herein, it should be understood that the disclosed embodiments are merely exemplary of the claimed structures and methods, which may be embodied in various forms. However, the present invention may be embodied in many different ways and should not be construed as being limited to the exemplary embodiments described herein. In the description, details of well-known features and techniques may be omitted to avoid unnecessarily obscuring the presented embodiments.

[0013] The singular forms "a," "an," and "the" should be understood to include plural references unless the context clearly dictates otherwise. Thus, for example, reference to "a surface of a component" includes a reference to the presence of one or more such surfaces unless the context clearly dictates otherwise.

[0014] The present invention relates generally to the field of computing, and more particularly to active noise control. The exemplary embodiments described below provide, among other things, systems, methods, and program products for identifying workplace machine sounds as indicative of a potential or occurring accident and, accordingly, selectively enabling the sounds to be heard in a noise-canceling headset. Thus, the present embodiments have the potential to improve the technical field of active noise control applications by enabling sounds to be heard in a noise-canceling headset when the sounds are identified as indicative of a potential or occurring problem in industrial machinery, thereby improving employee safety when utilizing noise-canceling headsets in industrial work environments.

[0015] As mentioned above, ANC is a method for reducing unwanted sounds by adding a second sound specifically designed to eliminate them. Sound is a pressure wave consisting of alternating periods of compression and spallation. Noise-canceling speakers emit sound waves with the same amplitude but an inverted phase compared to the original sound (e.g., the unwanted sound). In a process called interference, the waves combine to form new waves that effectively cancel each other out. This effect is called destructive interference. Modern ANC is generally achieved using analog circuits or digital signal processing. An adaptive algorithm is designed to analyze the waveform of the background noise and then generate a signal that either shifts the phase or inverts the polarity of the original signal. This inverted signal is amplified, and a transducer creates a sound wave that is directly proportional to the amplitude of the original waveform, thereby creating destructive interference and effectively reducing the perceived volume of the noise. Noise-canceling headphones are headphones that use ANC to reduce undesirable or unsafe sound levels. For example, in the context of an industrial work environment, an employee's headset may use ANC to reduce the risk of noise-induced hearing loss (NIHL).

[0016] In industrial work environments (e.g., machine shop workshops), employees are exposed to high levels of noise that can damage their hearing and, in some cases, result in NIHL. Indeed, World Health Organization statistics indicate that noise exposure contributes to a significant proportion of workplace-related health problems. In an effort to mitigate the risk of hearing loss and NIHL, it is common for employees in industrial work environments to utilize noise-canceling headsets. Such headsets may implement known methods of destructive interference to cancel out ambient industrial noise (e.g., noise from industrial machinery) while employees can comfortably perform their tasks. However, in any industrial work environment, sounds originating from the machine or its surroundings may be indicative of a current or future problem / accident within or around the machine. The resulting problem or accident within or around the machine may present financial damage (e.g., repair costs) to the company as well as physical harm to employees. If noise-canceling headsets are utilized by employees to eliminate all noise in an industrial work environment, employees present in the environment may be unable to respond to the sounds by taking immediate corrective action (e.g., shutting down, repairs) or evacuating the work environment. Therefore, it may be necessary to deploy a system that allows for selective listening to sounds from an industrial work environment in the noise-canceling headsets of present employees if the sounds are indicative of a current or impending problem / incident within the industrial work environment. Thus, embodiments of the present invention may be advantageous in, among other things, identifying problematic sounds on or around a machine and enabling employees wearing the noise-canceling headsets to listen to such problematic sounds, thereby improving employee safety within an industrial work environment. The present invention does not require that all embodiments of the present invention incorporate all of its advantages.

[0017] According to at least one embodiment, a digital twin representation for a given industrial work environment (IWE) may be created, which may include a digital twin representation for each machine present in the environment. Additionally, a corpus of machine and ambient sounds / vibrations collected from the IWE may be created. The collected sounds / vibrations in the corpus may be classified using machine learning as to whether they indicate a problem. According to at least one embodiment, an employee present in the IWE may be identified, and sounds within the IWE may be monitored. If the monitored sounds are determined to indicate a problem (e.g., an accident) based on comparison with the corpus of classified IWE sounds, the severity of the problem may be identified using a digital twin representation of the source of the sounds (e.g., a machine) and the corpus of classified IWE sounds. Employees present within a boundary of the problem may be identified, and the monitored sounds indicative of the problem may be audible in a noise-canceling headset utilized by the identified employee.

[0018] According to at least one embodiment, an artificial intelligence (AI) (e.g., machine learning) and Internet of Things (IoT) enabled system may analyze machine conditions to identify whether sounds from any machine or its surroundings are associated with predicted damage to the machine or any incidents in its surroundings, thus building a corpus of machine and surrounding sounds based on IoT feeds from the IWE. The proposed selective sound enabling system selectively enables learned sounds so that employees present in areas associated with the damage or incident can hear the sounds despite the use of noise-canceling headsets.

[0019] According to at least one embodiment, if the sound of the IWE is determined to be indicative of a current or upcoming accident, the proposed system identifies the degree of severity of the accident based on an analysis of the sound (e.g., the location of the accident derived from the location of the machine producing the sound, the type of machine producing the sound, and the impact of the accident on the machine and its surroundings) and dynamically adjusts the loudness level of the identified employee's noise-canceling headset so that the employee is alerted to the accident and can proactively respond to the accident.

[0020] According to at least one embodiment, based on the use of historical learning and digital twin simulation, the proposed system can identify the area affected by the accident, as well as the affected employees, and therefore identify a boundary area within the IWE that may enable the affected employees to hear the sound within their noise-canceling headsets.

[0021] According to at least one embodiment, if the severity of the incident is relatively low, the proposed system may identify only the employees in the IWE who are relevant to repairing the incident (e.g., the employees correcting the problem). For other employees in the IWE, the proposed system may continue to filter out the sound in addition to other noises in the other employees' noise-canceling headsets.

[0022] According to at least one embodiment, if the proposed system identifies that an accident has been corrected and the likelihood of a future accident has been eliminated or reduced, the proposed system may remove sounds associated with the accident.

[0023] According to at least one embodiment, the proposed system may apply continuous supervised machine learning models to sounds / vibrations collected from IWE so that sounds / vibrations and their correlated attributes (e.g., source, source location, magnitude, sound / vibration type, composite sound / vibration pattern, operating state of the source of the sound / vibration) can be learned, and the impact of the sound / vibration can be predicted in terms of resulting physical damage to infrastructure / source, physical harm to people, or both.

[0024] According to at least one embodiment, the proposed system may, through the use of a noise-canceling headset, remove sounds that are not indicative of a current or upcoming problem / incident, and may allow selected sounds to be heard within the noise-canceling headset based on a corpus of classified IWE sounds.

[0025] According to at least one embodiment, the proposed system may use the learned corpus of IWE sounds and digital twin representations to predict damage to machines and / or IWE infrastructure, and based on the predicted damage, provide differentially configured action instructions (e.g., instructions to reduce the rotational speed of the machine) as well as messages to identified targeted employees or devices so that the employees can be alerted / informed of the damage and proactively respond.

[0026] The present invention may be a system, a method, or a computer program product, or a combination thereof, at any possible level of technical detail integration. The computer program product may include a computer-readable storage medium (or multiple computer-readable storage media) having computer-readable program instructions for causing a processor to perform aspects of the present invention.

[0027] A computer-readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer-readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media includes portable computer diskettes, hard disks, random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), static random access memories (SRAMs), portable compact disk read-only memories (CD-ROMs), digital versatile disks (DVDs), memory sticks, floppy disks, punch cards, or mechanically encoded devices such as ridge structures in grooves with recorded instructions, and any suitable combination of the foregoing. Computer-readable storage medium, as used herein, should not be construed as being a transitory signal per se, such as an electric wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse passing through a fiber optic cable), or an electrical signal transmitted over a wire.

[0028] The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to each computing / processing device, or may be downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, or a wireless network, or a combination thereof. The network may comprise copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, or edge servers, or a combination thereof. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium within the respective computing / processing device.

[0029] The computer-readable program instructions for carrying out the operations of the present invention may be either assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, configuration data for integrated circuits, or source or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk®, C++, or the like, and procedural programming languages ​​such as the “C” programming language or similar. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), and the connection may be to an external computer (e.g., over the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), may execute computer-readable program instructions to personalize the electronic circuitry by utilizing state information of the computer-readable program instructions to perform aspects of the present invention.

[0030] Aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0031] These computer-readable program instructions may be provided to a processor of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions executing via the processor of the computer or other programmable data processing apparatus create means for implementing the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams. These computer-readable program instructions may also be stored on a computer-readable storage medium that can instruct a computer, programmable data processing apparatus, or other device, or combination thereof, to function in a particular manner, such that a computer-readable storage medium having instructions stored thereon comprises a product containing instructions that implement aspects of the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams.

[0032] The computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other device to generate a computer-implemented process, such that the instructions executing on the computer, other programmable apparatus, or other device implement the function(s) / act(s) specified in one or more blocks of the flowcharts and / or block diagrams.

[0033] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowcharts or block diagrams may represent a module, segment, or portion of instructions, comprising one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may actually be executed concurrently or substantially concurrently, and the blocks may sometimes be executed in the reverse order, depending on the functionality involved. It should also be noted that each block of the block diagrams and / or flowchart diagrams, and combinations of blocks in the block diagrams and / or flowchart diagrams, may be implemented by a special-purpose hardware-based system that performs the specified functions or operations or executes a combination of special-purpose hardware and computer instructions.

[0034] The exemplary embodiments described below provide systems, methods, and program products for determining whether sounds from around a machine or its IWE indicate a current or predicted problem at the machine or in the surroundings, and therefore blocking the sounds and adjusting the volume while allowing the sounds to be heard in a noise-canceling headset.

[0035] Referring to Figure 1, an exemplary networked computing environment 100 is shown according to at least one embodiment. The networked computing environment 100 may include a client computing device 102, a server 112, a headset IoT device 118, a machine IoT device 120, and a microphone IoT device 122 interconnected via a communications network 114. According to at least one implementation, the networked computing environment 100 may include multiple client computing devices 102, headset IoT devices 118, machine IoT devices 120, microphone IoT devices 122, and servers 112, although only one of each is shown for illustrative simplicity. Additionally, in one or more embodiments, the client computing device 102, server 114, and headset IoT device 118 may each host a selective sound authorization program 110A, 110B, 110C. In one or more other embodiments, the selective sound permission programs 110A, 110B, 110C may be partially hosted on the client computing device 102, the server 114, and the headset IoT device 118 so that functionality may be separated between the devices.

[0036] The communications network 114 may include various types of communications networks, such as a wide area network (WAN), a local area network (LAN), a telecommunications network, a wireless network, a public switched network, or a satellite network, or combinations thereof. The communications network 114 may include connections, such as wired, wireless communications links, or fiber optic cables. It can be appreciated that FIG. 1 provides only an illustration of one implementation and does not suggest any limitation with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environment may be made based on design and implementation requirements.

[0037] The client computing device 102 may include a processor 104 and a data storage device 106 enabled to host and execute the software program 108 and the selective sound authorization program 110A, and to communicate with the server 112, the headset IoT device 118, the machine IoT device 120, and the microphone IoT device 122 over a communications network 114 in accordance with an embodiment of the present invention. The client computing device 102 may be, for example, a mobile device, a phone, a personal digital assistant, a netbook, a laptop computer, a tablet computer, a desktop computer, or any type of computing device capable of executing programs and accessing a network. As described with reference to FIG. 4, the client computing device 102 may include internal components 402a and external components 404a, respectively.

[0038] The server computer 112 may be a laptop computer, a netbook computer, a personal computer (PC), a desktop computer, or any programmable electronic device or network of programmable electronic devices that can host and execute the selective sound authorization program 110B and the database 116 and communicate with the client computing devices 102, the headset IoT device 118, the machine IoT device 120, and the microphone IoT device 122 via a communications network 114 in accordance with an embodiment of the present invention. As described with reference to FIG. 4 , the server computer 112 may include internal components 402b and external components 404b, respectively. The server 112 may also operate in a cloud computing service model such as Software as a Service (SaaS), Platform as a Service (PaaS), or Infrastructure as a Service (IaaS). The server 112 may also be located in a cloud computing deployment model such as a private cloud, a community cloud, a public cloud, or a hybrid cloud.

[0039] The headset IoT devices 118 may be circumaural or over-ear headphones, supra-aural headphones, headsets, or any other headset IoT devices 118 known in the art for implementing noise cancellation using destructive interference techniques, or combinations thereof, that can connect to a communications network 114 and send and receive data to and from the client computing device 102, the machine IoT device 120, the microphone IoT device 122, and the server 112. According to at least one implementation, the networked computing environment 100 may include multiple headset IoT devices 118. As described with reference to FIG. 4, the headset IoT devices 118 may each include an internal component 402c and an external component 404c.

[0040] The machine IoT device 120 may be an IoT-enabled machine (e.g., industrial machinery in an IWE) with a microphone and various other sensors built into or external to the machine that can connect to the communications network 114 and send and receive data to and from the client computing device 102, the headset IoT device 118, and the server 112. The microphone of the machine IoT device 120 may include, for example, one or more piezoelectric microphone sensors mounted to capture vibrations from different parts of the machine or structure. The various other sensors of the machine IoT device 120 may include, for example, heating sensors, weight sensors, and pressure sensors and may continuously collect data for the machine IoT device 120. According to at least one implementation, the networked computing environment 100 may include multiple machine IoT devices 120.

[0041] The microphone IoT device 122 may be a microphone or any other microphone IoT device 122 known in the art for capturing audio output (e.g., sound / vibration), or a combination thereof, that can connect to the communications network 114 and send and receive data to and from the client computing device 102, the headset IoT device 118, and the server 112. According to at least one implementation, the networked computing environment 100 may include multiple microphone IoT devices 122.

[0042] According to this embodiment, the selective sound authorization programs 110A, 110B, 110C may be programs that can receive information about the work environment and the machines contained therein, create a digital twin representation for the work environment and the machines, classify sounds of the work environment and the machines contained therein to create a learned corpus of sounds indicative of a problem, monitor sounds of the work environment and the machines contained therein to determine whether sounds indicative of a problem have been received, identify the severity of the problem and employees near or involved in repairing the problem, and enable sounds indicative of a problem in the work environment or the machines contained therein to be heard through the identified employees' noise-canceling headsets. The work environment digital twin creation and sound classification method is described in more detail below with respect to FIG. 2. The selective sound authorization method is described in more detail below with respect to FIG. 3.

[0043] Referring now to FIG. 2, an operational flowchart for creating a digital twin representation of an IWE and creating a learned corpus of IWE sounds is shown in a digital twin creation and sound classification process 200, according to at least one embodiment. At 202, the selective sound authorization program 110A, 110B, 110C may receive information about the IWE and the machines present within the IWE. Utilizing the software program 108, a user may upload information that the selective sound authorization program 110A, 110B, 110C may access or receive. The information may include physical and non-physical attributes of the IWE, such as, but not limited to, the physical size of the interior space of the IWE, a floor plan of the IWE, the number of employees assigned to work within the IWE, a list of employee identification / badge numbers of employees assigned to work within the IWE, the number of machines within the IWE, the layout of machine placement within the IWE, sprinkler and other safety response systems within the IWE, and the placement of microphones (e.g., microphone IoT device 122) within the IWE. The physical attributes of an IWE may have states, and these states may change across dimensions such as time. Two or more changes to the states of the physical attributes of an IWE may be referred to as the experience or history of the IWE. Additionally, two or more changes to the states of the non-physical attributes of an IWE may also be part of the experience or history of the IWE.

[0044] The information may also include physical and non-physical attributes of each machine (e.g., machine IoT device 120) in the IWE. According to at least one embodiment, the physical attributes of a machine may include the machine's physical size, machine type, the machine's material composition and its individual components (at different granularities), the machine's physical placement or configuration relative to other machines, the physical placement or configuration of the machine's components relative to each other or other machines, the function of the machine or its components, and one or more IoT sensors (e.g., various other sensors of machine IoT device 120) embedded within or external to the machine. Physical attributes may have states, and these states may change across dimensions such as time. Two or more changes to the states of a machine's physical attributes may be referred to as the machine's experience or history. According to at least one embodiment, the non-physical attributes of a machine may include information describing the machine and its physical attributes, the machine's context with respect to other machines or entities, and information describing the state of the machine's attributes and changes in those states over time. Two or more changes to the states of a machine's non-physical attributes may also be part of the machine's experience or history. The machine's context may include information that defines the machine with respect to other machines or entities. Non-limiting examples of such context data may include, for a machine, bills of materials, maintenance plans, maintenance history, part replacement history, part usage history, specifications, three-dimensional models and computer-aided design (CAD) drawing data, fault codes, scheduled maintenance plans, operation manuals, usage data such as IoT sensor readings associated with the machine, the machine's assigned employee, AI and condition prediction data, operational history, ownership, and applicable standards. Each of such types of context data may also have associated change information.

[0045] Next, at 204, the selective authorization program 110A, 110B, 110C may create a digital representation of the IWE and a digital twin representation for each machine within the IWE based on the information received at 202. According to one definition, a digital twin refers to a digital representation, or more broadly, a computerized representation, of an IWE or a machine within the IWE. In an IoT system, a digital twin may represent an evolving virtual data model that mimics an IWE or machine and its experiences and state changes. A digital twin, in one embodiment, can be said to store and track information about its twin IWE or machine. According to at least one embodiment, a digital twin stores and tracks information about the physical and non-physical attributes of an IWE or machine, the context of the IWE or machine with respect to other machines or entities, and information describing the attribute states of the IWE or machine and changes in those states over time. According to at least one embodiment, creating a digital twin generally refers to a computer-implemented process (implemented by executing programming instructions using a processor) by which a digital record containing the digital twin is created on a non-transitory, tangible storage device. The storage device may be separate from the IWE or machine and may be a component in a cloud computing infrastructure available in distributed networks and systems, such as the Internet or IoT systems. According to at least one embodiment, the created digital twin may be created on and stored within the data storage device 106 or database 116. Creating a digital twin may also be described as instantiating a digital twin.

[0046] According to at least one embodiment, a digital twin of an IWE or machine can be created simultaneously with an IWE or machine that has similar baseline characteristics as the initial IWE or machine. According to at least one other embodiment, the digital twin can be created at a different time than the IWE or machine (e.g., before or after the IWE or machine). For example, a digital twin can be created via a pre-configured data representation of the IWE or machine. At any given time, regardless of when the digital twin of an IWE or machine was created, the two can be linked. Linking a digital twin and a corresponding IWE or machine can include, for example, a process in which data records containing or representing the digital twin are modified to reference the unique identity of the IWE or machine or to reflect any changes to the physical or non-physical attributes, or a combination thereof, of the IWE or machine.

[0047] In this embodiment, at 206, the selective sound permission program 110A, 110B, 110C may collect sounds / vibrations from a noise-filled environment, such as an IWE, and from machines within the IWE. According to at least one embodiment, the sounds / vibrations from the IWE and the machines contained therein may be detected and captured via one or more microphones integrated into or external to the IWE machine IoT device 120, or one or more microphone IoT devices 122 that may be deployed throughout the IWE, or a combination thereof. The captured sounds / vibrations of the IWE and the machines contained therein may be transmitted to the selective sound permission program 110A, 110B, 110C and stored as a corpus in the data storage device 106 or database 116. Correlated attributes of the captured sound / vibration (e.g., source, source location, magnitude, sound / vibration type, sound / vibration pattern, composite sound / vibration pattern, operating state of the source after the sound / vibration, resulting effect on the source, remedial instructions in response to the sound / vibration) may also be transmitted to the selective sound permission programs 110A, 110B, 110C and stored in the corpus.

[0048] Next, at 208, the selective sound authorization program 110A, 110B, 110C may classify the received corpus of sounds / vibrations created at 206. According to at least one embodiment, the selective sound authorization program 110A, 110B, 110C may apply a known continuous supervised machine learning model to the corpus of sounds / vibrations and correlated attributes such that a classification of whether the sound / vibration is indicative of a problem / incident can be made by the model. According to various embodiments, a problem / incident may include, but is not limited to, a mechanical malfunction of a machine, an electrical malfunction of a machine, an out-of-tolerance heating condition of a machine, and an adverse condition of an IWE (e.g., exposure to fire, smoke, chemicals, etc.). A user-defined training set of labeled sounds / vibrations (i.e., sounds / vibrations labeled as problematic or normal) having correlated attributes such as those listed above may be uploaded by the user via the software program 108 and accessed or received by the selective sound allowing programs 110A, 110B, 110C in the ongoing training of supervised machine learning models. The classifications (e.g., problematic, normal) of the sounds / vibrations in the corpus may be stored within the corpus along with the sounds / vibrations and their correlated attributes. Furthermore, depending on the classification of the sounds / vibrations, noise reduction attributes (e.g., sound removal, sound enabling) may be defined for the sounds / vibrations and stored within the corpus as one of the correlated attributes of the sounds / vibrations. According to at least one embodiment, application of the trained machine learning model to the corpus of sounds / vibrations may continue as new sounds / vibrations are added to the corpus by the selective sound allowing programs 110A, 110B, 110C. The user-defined training set and classified corpus of sounds / vibrations can serve as historical data (i.e., knowledge corpus) for reference and comparison by the selective sound authorization programs 110A, 110B, 110C in future sound / vibration evaluations.

[0049] Referring now to FIG. 3, an operational flowchart for selectively enabling sound to be heard in a noise-canceling headset in a selective sound authorization process 300 is shown, according to at least one embodiment. At 302, the selective sound authorization program 110A, 110B, 110C may identify a user (e.g., an employee) performing an activity in a noise-filled environment, such as an IWE, and utilizing a noise-canceling headset (e.g., a headset IoT device 118). According to an exemplary embodiment, the employee's use of a noise-canceling headset may be required when performing an activity in the IWE, and the selective sound authorization program 110A, 110B, 110C may actively cancel the noise of the IWE in the noise-canceling headset worn by the employee. In identifying the user in the IWE, the selective sound authorization program 110A, 110B, 110C may also identify the employee's role (e.g., work assignment, machine assignment) within the IWE. According to at least one embodiment, employees present within an IWE may be identified and located via a trackable employee-specific badge, the location of which may be tracked using known technology for indoor positioning (e.g., radio frequency identification, Wi-Fi, Bluetooth) and shared with the selective sound permission program 110A, 110B, 110C. According to another embodiment, employees present within an IWE may be identified and located via an employee-specific noise-canceling headset issued to the employee for use while present within the IWE. The location of the noise-canceling headset may be tracked using known technology for indoor positioning and shared with the selective sound permission program 110A, 110B, 110C. According to yet another embodiment, employees present within an IWE may be identified and located via a predetermined employee work schedule / assignment for the IWE, which may be uploaded to the selective sound permission program 110A, 110B, 110C.

[0050] At 304, the selective sound permission program 110A, 110B, 110C may monitor the IWE for sounds, including sounds from machines (e.g., machine IoT devices 120) within the IWE. According to one embodiment, the selective sound permission program 110A, 110B, 110C may receive sounds / vibrations along with correlated attributes from one or more microphones integrated into or external to the IWE machine IoT device 120, or one or more microphone IoT devices 122 that may be deployed throughout the IWE, or a combination thereof. Additionally, the selective sound permission program 110A, 110B, 110C may identify the current loudness level of the received sound and the source of the received sound. The source of the received sound may be identified based on the microphone that captured the sound. For example, a particular machine in the IWE may be identified as the source of the received sound if the received sound was captured by a microphone integrated into or external to the particular machine.

[0051] Next, at 306, the selective sound permission program 110A, 110B, 110C may determine whether a sound received while monitoring the IWE for sound indicates a problem / incident within the machine or its surroundings at the IWE. According to at least one embodiment, in determining whether a received sound / vibration indicates a problem / incident, the selective sound permission program 110A, 110B, 110C may reference historical data (i.e., a user-defined training set and a classification corpus of received sounds / vibrations, as described in process 200) and compare the received sound / vibration thereto. According to another embodiment, the selective sound permission program 110A, 110B, 110C may apply the machine learning model of process 200 to determine whether the received sound / vibration indicates a problem / incident. For example, a sound / vibration classified as a problem by the machine learning model may be determined to indicate a problem / incident. In various embodiments, the selective sound authorization program 110A, 110B, 110C may add the received sound / vibration, along with its correlated attributes, to the corpus of received sounds / vibrations described above in process 200. In response to determining that the received sound / vibration is indicative of a problem / incident (step 306, "Y" branch), the selective sound authorization process 300 may block the received sound / vibration from other sounds / vibrations of the IWE and proceed to step 310. In response to determining that the received sound / vibration is not indicative of a problem / incident (step 306, "N" branch), the selective sound authorization process 300 may proceed to step 308.

[0052] At 308, the selective sound enabling program 110A, 110B, 110C may continue to filter out sounds / vibrations received in noise-canceling headsets utilized by employees when performing activities within the IWE. The selective sound enabling program 110A, 110B, 110C may utilize known destructive interference techniques to filter out sounds / vibrations received in noise-canceling headsets.

[0053] At 310, the selective sound authorization program 110A, 110B, 110C may identify or predict the severity of a problem / incident indicated by a received sound. The problem / incident severity may include, among other things, the location of the problem / incident and the machine identification information derived from the received sound and the corresponding machine source. The problem / incident severity may also include the resulting impact in terms of physical damage to the machine source or IWE, or physical harm to humans, or a combination thereof, associated with the received sound. According to at least one embodiment, the selective sound authorization program 110A, 110B, 110C may utilize historical data in combination with data from the digital twin representation of the machine source or IWE, or a combination thereof, in identifying or predicting the severity of a problem / incident indicated by a received sound. Additionally, utilizing historical data along with data from the machine source and the digital twin simulation of the IWE, the selective sound authorization programs 110A, 110B, 110C can identify areas within the IWE affected by the problem / incident and, therefore, identify a boundary scope of the problem / incident within the IWE. Depending on the severity of the problem / incident, the boundary scope of the problem / incident may be limited to the identified impact area or may extend beyond it. For example, if the severity of the problem / incident is relatively low (e.g., below a threshold), the selective sound authorization programs 110A, 110B, 110C may limit the boundary scope to the identified impact area. On the other hand, if the severity of the problem / incident is relatively high (e.g., above a threshold), the selective sound authorization programs 110A, 110B, 110C may extend the boundary scope beyond the identified impact area, potentially to include the entire IWE. The threshold may be a pre-configured, user-defined condition (e.g., an impact on the sound source), such as, but not limited to, a fire in or near the sound source, or structural vibration of the sound source. Thresholds may also be derived from historical data and may include the operating state of the sound source after the sound / vibration or the resulting impact on the sound source.

[0054] Next, at 312, the selective sound permission program 110A, 110B, 110C may identify all employees located within the identified boundary of the problem / incident. Employees within the boundary may be tracked and consequently identified via their issued employee badges or noise-canceling headsets using known techniques for indoor positioning. Employees within the boundary may also be identified through the use of pre-established mapping of IWE, including machine locations and employee assignments to machine locations. According to at least one other embodiment, the selective sound permission program 110A, 110B, 110C may identify only employees within the boundary who are tasked with correcting the problem / incident.

[0055] Next, at 314, the selective sound permission program 110A, 110B, 110C may enable the received sound to be heard in the noise-canceling headsets of the identified employees within the problem / incident boundary. According to at least one embodiment, the selective sound permission program 110A, 110B, 110C may dynamically change (e.g., increase or decrease) the volume level of the received sound so that the identified employees within the boundary can hear the received sound through their noise-canceling headsets, be alerted to the problem / incident, and proactively respond. The selective sound permission program 110A, 110B, 110C may continue to filter the received sound, in addition to other noise, in the noise-canceling headsets of other employees in the IWE. According to at least one embodiment, in addition to dynamically changing the volume level of the received sound, the selective sound permission program 110A, 110B, 110C may temporarily disable the use of destructive interference techniques in the noise-canceling headsets of the identified employees within the problem / incident boundary. According to at least one embodiment, the selective sound permission program 110A, 110B, 110C may identify that a problem / incident has been corrected (e.g., via data received from various sensors of the machine IoT device 120) or that the possibility of the problem / incident has been eliminated or reduced, or a combination thereof, and may therefore filter sounds received in the noise-canceling headsets of identified employees within the problem / incident boundary. According to at least one embodiment, in addition to enabling the sounds received in the noise-canceling headsets to be heard, the selective sound permission program 110A, 110B, 110C may provide configured action items (i.e., repair instructions) and messages to identified employees within the problem / incident boundary in response to the received sounds. The messages may include warnings, alerts, or recommended safety actions in response to the problem / incident. The configured action items and messages may be in the form of audio messages transmitted through the noise-canceling headsets (e.g., headset IoT device 118) of identified employees within the problem / incident boundary.The configured action items and messages may also be in the form of text messages transmitted to the IWE machine (e.g., machine IoT device 120).

[0056] 2 and 3 are intended to be illustrative of only one implementation and are not intended to suggest any limitations on how different embodiments may be implemented. Many modifications to the depicted environments may be made based on design and implementation requirements.

[0057] FIG. 4 is a block diagram 400 of the internal and external components of the client computing device 102, server 112, and headset IoT device 118 shown in FIG. 1, according to one embodiment of the present invention. It can be appreciated that Figure 4 provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environments may be made based on design and implementation requirements.

[0058] Data processing systems 402, 404 represent any electronic device capable of executing machine-readable program instructions. Data processing systems 402, 404 may represent smartphones, computer systems, PDAs, or other electronic devices. Examples of computing systems, environments, or configurations, or combinations thereof, that may be represented by data processing systems 402, 404 include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, multiprocessor systems, microprocessor-based systems, network PCs, minicomputer systems, IoT devices, and distributed cloud computing environments that include any of the above systems or devices.

[0059] The client computing device 102, the server 112, and the headset IoT device 118 may include respective sets of internal components 402a, b, c and external components 404a, b, c shown in Figure 4. Each of the set of internal components 402 includes one or more processors 420, one or more computer-readable RAMs 422, and one or more computer-readable ROMs 424 on one or more buses 426, as well as one or more operating systems 428 and one or more computer-readable tangible storage devices 430. The one or more operating systems 428, the software programs 108 and selective sound permission program 110A in the client computing device 102, the selective sound permission program 110B in the server 112, and the selective sound permission program 110C in the headset IoT device 118 are stored in one or more of the respective computer-readable tangible storage devices 430 for execution by one or more of the respective processors 420 via one or more of the respective RAMs 422 (which typically include cache memory). 4, each of the computer-readable tangible storage devices 430 is an internal hard drive magnetic disk storage device. Alternatively, each of the computer-readable tangible storage devices 430 is a semiconductor storage device such as ROM 424, EPROM, flash memory, or any other computer-readable tangible storage device capable of storing computer programs and digital information.

[0060] Each set of internal components 402 also includes a R / W drive or interface 432 for reading from and writing to one or more portable computer-readable tangible storage devices 438, such as a CD-ROM, DVD, memory stick, magnetic tape, magnetic disk, optical disk, or semiconductor storage device. Software programs, such as selective sound authorization programs 110A, 110B, 110C, may be stored on one or more of the respective portable computer-readable tangible storage devices 438, read via the respective R / W drive or interface 432, and loaded onto the respective hard drive 430.

[0061] Each set of internal components 402a, b, c also includes a network adapter or interface 436, such as a TCP / IP adapter card, a wireless wi-fi interface card, or a 3G or 4G wireless interface card, or other wired or wireless communication link. The software program 108 and selective sound allowing program 110A in the client computing device 102, the selective sound allowing program 110B in the server 112, and the selective sound allowing program 110C in the headset IoT device 118 may be downloaded from an external computer to the client computing device 102, the server 112, and the headset IoT device 118 via a network (e.g., the Internet, a local area network, other wide area network) and their respective network adapters and interfaces 436. From the network adapters or interfaces 436, the software program 108 and selective sound allowing program 110A in the client computing device 102, the selective sound allowing program 110B in the server 112, and the selective sound allowing program 110C in the headset IoT device 118 are loaded onto their respective hard drives 430. The network may include copper wire, optical fiber, wireless transmissions, routers, firewalls, switches, gateway computers, or edge servers, or a combination thereof.

[0062] Each of the set of external components 404a,b,c may include a computer display monitor 444, a keyboard 442, and a computer mouse 434. The external components 404a,b,c may also include touch screens, virtual keyboards, touchpads, pointing devices, and other human interface devices. Each of the set of internal components 402a,b,c also includes a device driver 440 for interfacing to the computer display monitor 444, the keyboard 442, and the computer mouse 434. The device driver 440, the R / W drive or interface 432, and the network adapter or interface 436 include hardware and software (stored in the storage device 430 or the ROM 424, or a combination thereof).

[0063] Although this disclosure includes detailed descriptions related to cloud computing, it should be understood in advance that implementation of the teachings described herein is not limited to cloud computing environments. Rather, embodiments of the present invention can be implemented in conjunction with any other type of computing environment now known or later developed.

[0064] Cloud computing is a service delivery model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processes, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with the service provider. The cloud model can include at least five characteristics, at least three service models, and at least four deployment models.

[0065] The characteristics are as follows: On-Demand Self-Service: Cloud consumers can unilaterally provision computing capacity, such as server time and network storage, automatically, as needed, without requiring human interaction with the service provider. Wide network access: Capabilities are available over the network and accessed through standard mechanisms that facilitate use by heterogeneous thin or thick client platforms (eg, mobile phones, laptops, and PDAs). Resource Pooling: A provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically allocated and reallocated according to demand. Although consumers generally have no control or knowledge of the exact location of the resources provided, there is an implication of location independence in that the location may be specifiable at a higher level of abstraction (e.g., country, state, or data center). Rapid Elasticity: Capacity can be quickly and elastically provisioned, in some cases automatically, to quickly scale out, and quickly released to quickly scale in. To the consumer, the capacity available for provisioning often appears unlimited, and can be purchased in any amount at any time. Measured Services: Cloud systems automatically control and optimize resource usage using measurement capabilities at a level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency to both providers and consumers of utilized services.

[0066] The service model is as follows: Software as a Service (SaaS): The consumer is offered the ability to use a provider's applications running on a cloud infrastructure. The applications are accessible from a variety of client devices through a thin-client interface such as a web browser (e.g., web-based email). The consumer does not manage or control the underlying cloud infrastructure, including the network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. Platform as a Service (PaaS): The ability offered to consumers is to deploy consumer-generated or ingested applications, created using programming languages ​​and tools supported by the provider, on a cloud infrastructure. The consumer does not manage or control the underlying cloud infrastructure, including networks, servers, operating systems, or storage, but does have control over the deployed applications and, in some cases, application hosting environment configuration. Infrastructure as a Service (IaaS): The ability offered to consumers is to provision processing, storage, network, and other underlying computing resources. Consumers can deploy and run any software, which may include operating systems and applications. Consumers do not manage or control the underlying cloud infrastructure, but do have control over the operating system, storage, deployed applications, and possibly limited control of selected network components (e.g., host firewalls).

[0067] The deployment model is as follows: Private Cloud: Cloud infrastructure is run exclusively for an organization. The cloud infrastructure may be managed by that organization or a third party and may reside on-premise or off-premise. Community Cloud: Cloud infrastructure is shared by several organizations to support a specific community of shared interests (e.g., mission, security requirements, policies, and compliance considerations). It can be managed by the organization or a third party and can exist on-premises or off-premises. Public Cloud: Cloud infrastructure is made available to the general public or large industry groups and is owned by organizations that sell cloud services. Hybrid Cloud: A cloud infrastructure is a combination of two or more clouds (private, community, or public) that remain distinct entities but are tied together by standardized or proprietary technologies that enable data and application portability (e.g., cloud bursting to balance load between clouds).

[0068] Cloud computing environments are service-oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes.

[0069] Referring now to FIG. 5, an exemplary cloud computing environment 50 is shown. As shown, the cloud computing environment 50 comprises one or more cloud computing nodes 100 that may communicate with local computing devices used by cloud consumers, such as, for example, a personal digital assistant (PDA) or mobile phone 54A, a desktop computer 54B, a laptop computer 54C, or an automobile computer system 54N, or a combination thereof. The nodes 100 may communicate with each other. They may be physically or virtually grouped in one or more networks (not shown), such as the private, community, public, or hybrid clouds described above, or a combination thereof. This enables the cloud computing environment 50 to provide infrastructure, platform, or software, or a combination thereof, as a service without the cloud consumer having to maintain resources on their local computing device. It should be understood that the types of computing devices 54A-N shown in FIG. 5 are intended for illustrative purposes only, and that the computing nodes 100 and the cloud computing environment 50 may communicate with any type of computerized device via any type of network or network-addressable connection, or a combination thereof (e.g., using a web browser).

[0070] Referring now to Figure 6, there is shown a set of functional abstraction layers 600 provided by the cloud computing environment 50. It should be understood in advance that the components, layers, and functions shown in Figure 6 are intended to be illustrative only, and that embodiments of the present invention are not limited thereto. As shown, the following layers and corresponding functions are provided:

[0071] Hardware and software layer 60 includes hardware and software components. Examples of hardware components include mainframe 61, RISC (Minimum Instruction Set Computer) architecture-based servers 62, servers 63, blade servers 64, storage devices 65, and networks and networking components 66. In some embodiments, software components include network application server software 67 and database software 68.

[0072] The virtualization layer 70 provides an abstraction layer from which examples of virtual entities such as virtual servers 71 , virtual storage 72 , virtual networks including virtual private networks 73 , virtual applications and operating systems 74 , and virtual clients 75 can be sourced.

[0073] In one example, management layer 80 may provide the following functions: Resource provisioning 81 provides dynamic procurement of computing resources and other resources utilized to execute tasks within the cloud computing environment. Metering and pricing 82 provides cost tracking as resources are utilized within the cloud computing environment and charging or billing for the consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal 83 provides access to the cloud computing environment for consumers and system administrators. Service level management 84 provides cloud computing resource allocation and management so that required service levels are met. Service level agreement (SLA) planning and achievement 85 provides advance arrangement and procurement of cloud computing resources in anticipation of future requirements according to SLAs.

[0074] The workload tier 90 provides examples of functions for which a cloud computing environment may be utilized. Examples of workloads and functions that may be provided from this tier include mapping and navigation 91, software development and lifecycle management 92, virtual classroom instruction delivery 93, data analytics processing 94, transaction processing 95, and selective sound authorization 96. Selective sound authorization 96 may relate to selectively enabling sounds to be heard in a noise-canceling headset.

[0075] While the description of various embodiments of the present invention has been presented for purposes of illustration, it is not intended to be exhaustive or limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope of the described embodiments. The terminology used herein has been selected to best explain the principles, practical applications, or technical improvements of the embodiments over technologies found in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. 1. A computer-based method for enabling selective sound in a noise-canceling headset, the method comprising: receiving sound from a noise-filled environment, the source of the sound being a machine within the noise-filled environment; determining that the sound indicates a problem in the noise-filled environment; identifying the severity of the problem; identifying users within a boundary range of the problem, the boundary range being based in part on the severity of the problem, and the users wearing noise-canceling headsets that actively filter out sound from the noise-filled environment; enabling the sound to be heard in the noise-canceling headset of the identified user; A method comprising:

2. The method of claim 1 , wherein the sound is captured by one or more microphones built into the machine or external to the machine, and the sound may include vibrations.

3. receiving information of the noise-filled environment and information of one or more machines present in the noise-filled environment, the information including physical and non-physical attributes of the noise-filled environment and the one or more machines; creating a digital twin representation of the noise-filled environment and a digital twin representation for the one or more machines; collecting sounds from the noise-filled environment and the one or more machines, the collected sounds comprising correlated attributes; creating a sound corpus that includes the collected sounds; classifying the collected sounds of the corpus of sounds as problematic or normal by applying a supervised machine learning model to the collected sounds and correlated attributes; The method of claim 1 or 2, further comprising:

4. determining that the sound indicates a problem in the noise-filled environment further comprises: comparing the sound to a corpus of classified sounds of the noise-filled environment, wherein the classification of the classified sounds of the noise-filled environment comprises a problematic classification or a normal classification. The method according to claim 1 or 2.

5. The step of identifying the severity of the problem further comprises: identifying the severity of the problem using historical data in combination with data from a digital twin representation of the noise-filled environment and the machine; using the historical data in combination with the data from a digital twin representation of the noise-filled environment and the machine to identify areas within the noise-filled environment that are affected by the problem; 3. The method of claim 1 or 2, comprising:

6. The method of claim 5 , wherein if the severity of the problem is below a threshold, the boundary extent of the problem is limited to the affected area of ​​the problem within the noise-filled environment.

7. The method of claim 1 or 2, wherein identifying the user within the boundary range comprises tracking the user via a trackable user-specific badge or a trackable user-specific noise-canceling headset.

8. 1. A computer system, comprising: a computer system comprising one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage media, and program instructions stored on at least one of the one or more tangible storage media for execution by at least one of the one or more processors via at least one of the one or more memories; receiving sound from a noise-filled environment, the source of the sound being a machine within the noise-filled environment; determining that the sound indicates a problem in the noise-filled environment; identifying the severity of the problem; identifying users within a boundary range of the problem, the boundary range being based in part on the severity of the problem, and the users wearing noise-canceling headsets that actively filter out sound from the noise-filled environment; enabling the sound to be heard in the noise-canceling headset of the identified user; A computer system capable of performing a method including:

9. 10. The computer system of claim 8, wherein the sound is captured by one or more microphones built into the machine or external to the machine, and the sound may include vibrations.

10. The method comprises: receiving information of the noise-filled environment and information of one or more machines present in the noise-filled environment, the information including physical and non-physical attributes of the noise-filled environment and the one or more machines; creating a digital twin representation of the noise-filled environment and a digital twin representation for the one or more machines; collecting sounds from the noise-filled environment and the one or more machines, the collected sounds comprising correlated attributes; creating a sound corpus that includes the collected sounds; classifying the collected sounds of the corpus of sounds as problematic or normal by applying a supervised machine learning model to the collected sounds and correlated attributes; 10. The computer system of claim 8 or 9, further comprising:

11. determining that the sound indicates a problem within the noise-filled environment further comprises: comparing the sound to a corpus of classified sounds of the noise-filled environment, wherein the classification of the classified sounds of the noise-filled environment comprises a problematic classification or a normal classification.

10. A computer system according to claim 8 or 9.

12. The step of identifying the severity of the problem further comprises: identifying the severity of the problem using historical data in combination with data from a digital twin representation of the noise-filled environment and the machine; using the historical data in combination with the data from a digital twin representation of the noise-filled environment and the machine to identify areas within the noise-filled environment that are affected by the problem; 10. A computer system according to claim 8 or 9, comprising:

13. 13. The computer system of claim 12, wherein if the severity of the problem is below a threshold, the boundary extent of the problem is limited to the affected area of ​​the problem within the noise-filled environment.

14. 10. The computer system of claim 8 or 9, wherein identifying the user within the boundary range comprises tracking the user via a trackable user-specific badge or a trackable user-specific noise-canceling headset.

15. The processor receiving sound from a noise-filled environment, the source of the sound being a machine within the noise-filled environment; determining that the sound indicates a problem in the noise-filled environment; identifying the severity of the problem; identifying a user within a boundary range of the problem, the boundary range being based in part on the severity of the problem, and the user wearing a noise-canceling headset that actively filters out sound from the noise-filled environment; and a step of enabling the sound to be heard in the noise-canceling headset of the identified user.

16. 16. The computer program of claim 15, wherein the sound is captured by one or more microphones integrated into the machine or external to the machine, and the sound may include vibrations.

17. the processor, receiving information about the noise-filled environment and information about one or more machines present in the noise-filled environment, the information including physical and non-physical attributes of the noise-filled environment and the one or more machines; creating a digital twin representation of the noise-filled environment and a digital twin representation for the one or more machines; collecting sounds from the noise-filled environment and the one or more machines, the collected sounds comprising correlated attributes; creating a sound corpus containing the collected sounds; classifying the collected sounds of the corpus of sounds as problematic or normal by applying a supervised machine learning model to the collected sounds and correlated attributes; 17. The computer program of claim 15 or 16, further comprising:

18. The step of determining that the sound indicates a problem in the noise-filled environment further comprises: comparing the sounds to a corpus of classified sounds of the noise-filled environment, wherein the classification of the classified sounds of the noise-filled environment comprises a problematic classification or a normal classification; 17. A computer program according to claim 15 or 16.

19. The step of identifying the severity of the problem further comprises: using historical data in combination with data from a digital twin representation of the noise-filled environment and the machine to identify the severity of the problem; using the historical data in combination with the data from a digital twin representation of the noise-filled environment and the machine to identify areas within the noise-filled environment that are affected by the problem; 17. A computer program according to claim 15 or 16, comprising:

20. 20. The computer program of claim 19, wherein if the severity of the problem is below a threshold, the boundary extent of the problem is limited to the affected area of ​​the problem within the noise-filled environment.

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