Selective sound allowance for noise cancelling headphones in industrial work environments

Abstract

Selective sound allowance for noise cancelling headphones in industrial work environments. According to one embodiment, a method, computer system, and computer program product for allowing selective sound within a noise cancelling headphone are provided. Embodiments can include receiving sound from a noise filled environment. The sound source is a machine in the noise filled environment. Embodiments can include determining that the sound indicates a problem within the noise filled environment. The embodiments can include identifying a severity of the problem. Embodiments can 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 wears a noise cancelling headphone that actively cancels sound from the noise filled environment. Embodiments can include allowing the sound to be heard within the noise cancelling headphone of the identified user.

Description

BACKGROUND

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

[0002] Active noise control (ANC) (also known as noise cancellation (NC)) or active noise reduction (ANR)) is a method for reducing unwanted sound by adding a second sound specifically designed to cancel it out. Sound is a pressure wave, consisting of alternating compressions and rarefactions. A noise-cancelling speaker emits sound waves with the same amplitude but with opposite phase relative to the original sound (e.g., the unwanted sound). In a process called interference, these waves combine to form new waves and effectively cancel each other out. This effect is called destructive interference. Modern ANC is typically implemented using analog circuitry or digital signal processing. Adaptive algorithms are designed to analyze the waveform of the background noise, then generate a signal that will either phase-shift or invert the polarity of the original signal. This inverted signal is amplified, and a transducer produces sound waves proportional in amplitude to the original waveform, resulting in destructive interference and effectively reducing the volume of perceptible noise. Noise-cancelling headphones are headphones that use ANC to reduce the level of unwanted or unsafe sound. For example, in the context of an industrial work environment, employee headphones can employ ANC to mitigate the risk of noise-induced hearing loss (NIHL). SUMMARY

[0003] According to one embodiment, a method, computer system, and computer program product for allowing selective sound within a noise cancelling headphone. Embodiments can include receiving sound from a noise-filled environment. The sound source is a machine in the noise-filled environment. Embodiments can include determining that the sound indicates a problem within the noise-filled environment. The embodiments can include identifying a severity of the problem. Embodiments can 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 wears a noise cancelling headphone that actively cancels sound from the noise-filled environment. Embodiments can include allowing the sound to be heard within the noise cancelling headphone of the identified user. BRIEF DESCRIPTION OF DRAWINGS

[0004] These and other objects, features, and advantages of the present invention will become apparent in the following detailed description of illustrative embodiments thereof, which are to be read in connection with the accompanying drawings. The various features of the drawings are not to scale as the illustrations are for clarity of understanding of the present invention in conjunction with the detailed description. In the drawings:

[0005] Figure 1 An exemplary networked computer environment is shown in accordance with at least one embodiment.

[0006] Figure 2An operational flow diagram illustrating a work environment digital twin creation and sound classification process, in accordance with at least one embodiment.

[0007] Figure 3 An operational flow diagram illustrating a selective sound allowing process for selectively allowing a sound to be heard within a noise cancelling headset, in accordance with at least one embodiment.

[0008] Figure 4 is a functional block diagram of the internal and external components of a computer and server depicted in Figure 1

[0009] Figure 5 A cloud computing environment according to an embodiment of the invention is depicted.

[0010] Figure 6 An abstraction model layer according to an embodiment of the invention is depicted. DETAILED DESCRIPTION

[0011] Detailed embodiments of the claimed structures and methods are disclosed herein; however, it is to be understood that the disclosed embodiments are merely exemplary and that the claimed structures and methods can be implemented in a variety of forms. Therefore, specific implementations disclosed should not be construed to limit the scope of the present invention. In describing the exemplary embodiments, details of well-known features and techniques can be omitted to avoid unnecessarily obscuring the presented embodiments.

[0012] It should be understood that the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a component surface” includes reference to one or more of such surfaces unless the context clearly dictates otherwise.

[0013] The present invention relates generally to the field of computing, and more particularly to active noise control. The exemplary embodiments described below provide a system, method, and program product for, among other things, identifying work place machine sounds as an indication of a potential or occurring incident, and thus selectively allowing the sound to be heard within a noise cancelling headset. The present embodiments thus improve the art of active noise control applications by allowing the sound to be heard within a noise cancelling headset when the sound is identified as an indication of a potential or occurring problem within an industrial machine, thereby improving the safety of employees when utilizing noise cancelling headsets in an industrial work environment.

[0014] ​As previously described, ANC is a method of reducing unwanted sound by adding a specially designed second sound to cancel the unwanted sound. Sound is a pressure wave, consisting of alternating compression and rarefaction cycles. A noise-cancelling speaker emits a sound wave with the same amplitude but with opposite phase relative to the original sound (e.g., the unwanted sound). In a process called interference, these waves combine to form a new wave and effectively cancel each other out. This effect is called destructive interference. Modern ANC is typically implemented using analog circuitry or digital signal processing. Adaptive algorithms are designed to analyze the wave form of the background noise, then generate a signal that will either phase shift or invert the polarity of the original signal. This inverted signal is amplified, and a transducer produces a sound wave proportional to the amplitude of the original wave, resulting in destructive interference and effectively reducing the volume of the perceived noise. Noise-cancelling headphones are headphones that use ANC to reduce the level of unwanted or unsafe sound. For example, in the context of an industrial work environment, employee headphones can employ ANC to mitigate the risk of NIHL.

[0015] In an industrial work environment (e.g., a machine shop floor), employees are exposed to high noise levels that can damage the employees’ hearing and can lead to NIHL. In fact, data from the World Health Organization indicates that noise exposure contributes to a significant percentage of work place-related health problems. To mitigate the risk of hearing damage and NIHL, employees in industrial work environments typically use noise-cancelling headphones. Such headphones can implement the known method of destructive interference to cancel the surrounding industrial noise (e.g., noise from industrial machines) while the employees are comfortably performing their work. However, in any industrial work environment, sounds originating from machines or their surrounding environment can be an indication of a current or future problem / accident within the machine or its surrounding environment. A problem or accident arising within the machine or its environment can cause both financial harm to the company (e.g., repair costs) and physical harm to employees. If employees utilize noise-cancelling headphones to cancel all noise in the industrial work environment, the employees present within the environment can not be able to take immediate corrective action (e.g., shut down, repair) or evacuate the work environment in response to the sounds. Thus, if a sound from the industrial work environment is indicative of a current or future problem / accident within the industrial work environment, it can be necessary for the system to be in place to selectively allow the sound to be heard within the current employee’s noise-cancelling headphones. Thus, among other things, embodiments of the present invention can facilitate identifying problematic sounds of machines or their surrounding environments, allowing employees wearing noise-cancelling headphones to hear such problematic sounds, and enhancing employee safety within industrial work environments. The present invention does not require all of the advantages to be incorporated into every embodiment of the present invention.

[0016] According to at least one embodiment, a digital twin representation for a given Industrial Work Environment (IWE) can be created, which may include a digital twin representation for each machine present within the environment. Furthermore, a corpus of machine and surrounding sounds / vibrations collected from the IWE can be created. Machine learning can be used to classify the sounds / vibrations collected within the corpus to determine if they indicate a problem. According to at least one embodiment, employees present within the IWE can be identified, and sounds within the IWE can be monitored. If, based on a comparison with a classified corpus of IWE sounds, the monitored sounds are determined to indicate a problem (e.g., an accident), the severity of the problem can be identified using the digital twin representation of the sound source (e.g., a machine) and the classified corpus of IWE sounds. Employees present within the boundary of the problem can be identified, and the monitored sounds indicating the problem can be heard in noise-canceling headphones used by the identified employees.

[0017] According to at least one embodiment, an artificial intelligence (AI) (e.g., machine learning) and internet of things (IoT) enabled system can analyze machine conditions to identify whether sounds from any machine or its surrounding environment are related to predicted damage in the machine or any accident in the surrounding environment, and accordingly build a corpus of machine and surrounding sounds based on IoT feeds from IWE. The proposed selective sound allows the system to selectively allow learned sounds, such that employees present in the area associated with the damage or accident can still hear the sounds despite the use of noise-cancelling headphones.

[0018] According to at least one embodiment, if the sound of the IWE is determined to be an indication of a current or future incident, the proposed system can identify the severity of the incident based on analysis of the sound (e.g., the location of the incident derived from the location of the machine that produced the sound, the type of machine that produced the sound, and the impact of the incident on the machine and its surrounding environment), and dynamically adjust the loudness level of the sound in the noise cancellation headphones of the identified employees so that they can be warned and proactively respond to the incident.

[0019] According to at least one embodiment, based on the use of historical learning and digital twin simulation, the proposed system can identify the affected area of ​​an accident and the affected employees, and accordingly identify the boundary range within the IWE, within which sound can be heard in the noise-canceling headphones of the affected employees.

[0020] According to at least one embodiment, if the severity of the incident is relatively low, the proposed system can identify only those employees within the IWE who are involved in remediating the incident (e.g., those who will correct the problem). For other employees in the IWE, the proposed system can continue to eliminate sound in addition to other noises within their noise-canceling headphones.

[0021] According to at least one embodiment, the proposed system can eliminate the sound associated with the incident if the proposed system identifies that the incident is being rectified and the chances of future incidents are eliminated or reduced.

[0022] According to at least one embodiment, the proposed system can apply a continuously supervised machine learning model to the sound / vibration collected from the IWE, such that the sound / vibration and its associated attributes (e.g., source, source location, loudness, sound / vibration type, combined sound / vibration pattern, operating state of the source after the sound / vibration) can be learned and the impact of the sound / vibration in terms of the resulting physical damage to the infrastructure / source and / or physical harm to humans can be predicted.

[0023] According to at least one embodiment, the proposed system can eliminate the sound that does not indicate a current or future problem / incident by using noise cancelling headphones and can allow the corpus of classified IWE sound to select the sound that is heard within the noise cancelling headphones.

[0024] According to at least one embodiment, the proposed system can use the corpus of learned IWE sound and digital twin representation to predict damage to the machine and / or infrastructure of the IWE and provide different configured action instructions (e.g., instructions to reduce the rotational speed of the machine) and messages to the identified target employees or equipment based on the predicted damage, such that they can be warned / informed of the damage and act proactively.

[0025] The present application can be a system, a method, and / or a computer program product at any possible technical detail level of integration. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present application.

[0026] A computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, semiconductor, or any other suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0027] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives 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.

[0028] Computer readable program instructions for carrying out operations of the present application can be assembly instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for an integrated circuit, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like and a procedural programming language such as the "C" programming language or the like. The computer readable program instructions can execute entirely on a user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present application.

[0029] The computer readable program instructions can 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 produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0030] These computer readable program instructions can 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, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can include a non-transitory computer readable storage medium that can be a computer readable storage medium having no data, programs, program modules, and / or computer program

[0031] These computer readable program instructions can be provided to 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 produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0032] The flow diagrams and the block diagrams in the drawings are meant as illustrative representations of the architectures, functions, and operations of possible implementations of systems, methods and computer program products according to the present disclosure. It will be understood that each block of the flow diagrams and / or block diagrams, and combinations of blocks in the flow diagrams and / or the block diagrams, can be implemented by

[0033] The exemplary embodiments described below provide a system, method, and program product to determine sounds from a machine or its IWE surrounding environment indicative of a current or predicted problem in the machine or within the surrounding environment, and to isolate and allow hearing of sounds in a noise cancelling headset with adjusted sound loudness accordingly.

[0034] Referring to Figure 1 , an exemplary networked computer environment 100 is depicted in accordance with at least one embodiment. The networked computer environment 100 can 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 communication network 114. In accordance with at least one implementation, the networked computer environment 100 can include a plurality of client computing devices 102, headset IoT devices 118, machine IoT devices 120, microphone IoT devices 122, and servers 112, each of which are only shown for the sake of brevity of illustration. Additionally, in one or more embodiments, the client computing device 102, the server 114, and the headset IoT device 118 can each host a selective sound allowing program 110A, 110B, 110C. In one or more other embodiments, the selective sound allowing program 110A, 110B, 110C can be hosted partially on the client computing device 102, the server 114, and the headset IoT device 118, such that functionality can be separated between the devices.

[0035] The communication network 114 can include different types of communication networks, such as a wide area network (WAN), a local area network (LAN), a telecommunication network, a wireless network, a public switched network, and / or a satellite network. The communication network 114 can include connections, such as wired, wireless communication links, or fiber optic cables. It can be appreciated, Figure 1 Only one implementation is provided for illustration, and it is not implied that any limitations are imposed on the environment in which different embodiments can be implemented. Numerous modifications can be made to the depicted environment, based upon design and implementation requirements.

[0036] According to one embodiment of the present application, the client computing device 102 can include a processor 104 and a data storage device 106 capable of hosting and running a software program 108 and a selective sound allowance program 110A, and communicating with a server 112, a headset IoT device 118, a machine IoT device 120, and a microphone IoT device 122 via a communication network 114. The client computing device 102 can 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 running programs and accessing networks. As will be discussed in Figure 4 reference, the client computing device 102 can include internal components 402a and external components 404a, respectively.

[0037] According to an embodiment of the present application, the server computer 112 can be a laptop computer, a netbook computer, a personal computer (PC), a desktop computer, or any programmable electronic device or any network of programmable electronic devices capable of hosting and running a selective sound allowance program 110B and a database 116 and communicating with a client computing device 102, a headset IoT device 118, a machine IoT device 120, and a microphone IoT device 122 via a communication network 114. As will be discussed in Figure 4 reference, the server computer 112 can include internal components 402b and external components 404b, respectively. The server 112 can 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 can also be located in a cloud computing deployment model, such as a private cloud, community cloud, public cloud, or hybrid cloud.

[0038] The headset IoT device 118 can be a circumaural or supra-aural headset, an earbud, a headphone, and / or any other headset IoT device 118 known in the art for implementing noise cancellation using destructive interference techniques that is capable of connecting to the communication network 114 and transmitting and receiving data with 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 computer environment 100 can include multiple headset IoT devices 118. As will be discussed in greater detail below with respect to Figure 4 The headset IoT device 118 can include internal components 402c and external components 404c, respectively.

[0039] The machine IoT device 120 can be an IoT-enabled machine (e.g., an industrial machine within an IWE) having a microphone and different other sensors embedded in the machine or externally that is capable of connecting to the communication network 114 and transmitting and receiving data with the client computing device 102, the headset IoT device 118, and the server 112. The microphone of the machine IoT device 120 can include, for example, one or more piezoelectric microphone sensors mounted to capture vibrations from different parts of the machine or structure. The different other sensors of the machine IoT device 120 can include, for example, thermal sensors, weight sensors, and pressure sensors, and can continuously collect data of the machine IoT device 120. According to at least one implementation, the networked computer environment 100 can include multiple machine IoT devices 120.

[0040] The microphone IoT device 122 can be a microphone and / or any other microphone IoT device 122 known in the art for capturing audio output (e.g., sound / vibrations) that is capable of connecting to the communication network 114 and transmitting and receiving data with the client computing device 102, the headset IoT device 118, and the server 112. According to at least one implementation, the networked computer environment 100 can include multiple microphone IoT devices 122.

[0041] According to the present embodiments, the selective sound allowance program 110A, 110B, 110C can be a program capable of receiving information of a work environment and machines contained therein to create a digital twin representation of the work environment and the machines, classifying sounds of the work environment and the machines contained therein to create a corpus of learned problem-indicative sounds, monitoring sounds of the work environment and the machines contained therein to determine whether a problem-indicative sound has been received, identifying a severity of the problem and an employee nearby or involved in remediation of the problem, and allowing the problem-indicative sound within the work environment or the machines contained therein to be heard through a noise-cancelling headset of the identified employee. See below Figure 2The IWE digital twin creation and sound classification methods are further explained in detail. The selective sound allowance method is described below with respect to Figure 3 The IWE digital twin creation and sound classification methods are further explained in detail. The selective sound allowance method is described below with respect to

[0042] Referring now to Figure 2 , depicts an operational flow diagram for creating a digital twin representation of an IWE and creating a corpus of learned IWE sounds in the digital twin creation and sound classification process 200, in accordance with at least one embodiment. At 202, the selective sound allowance program 110A, 110B, 110C can receive information of an IWE and machines present in the IWE. With the software program 108, a user can upload information that can be accessed or received by the selective sound allowance program 110A, 110B, 110C. The information can include physical and non-physical attributes of the IWE such as, but not limited to, physical dimensions of the interior space of the IWE, a floor plan of the IWE, a number of employees assigned to work within the IWE, a list of employee badges / IDs of employees assigned to work within the IWE, a number of machines within the IWE, a layout of machine placement within the IWE, sprinklers and other safety response systems within the IWE, and microphone (e.g., microphone IoT devices 122) placement within the IWE. The physical attributes of the IWE can have states, and these states can undergo changes across dimensions such as time. Two or more changes in states of the physical attributes of the IWE can be referred to as an experience or history of the IWE. Further, two or more changes in states of the non-physical attributes of the IWE can also be part of the experience or history of the IWE.

[0043] Information can also include physical and non-physical attributes of each machine (e.g., machine IoT device 120) within the IWE. According to at least one embodiment, physical attributes of a machine can include physical dimensions of the machine, the machine type, material composition of the machine and its individual components (at varying degrees of granularity), physical arrangement or configuration of the machine relative to other machines, physical arrangement or configuration of components of the machine relative to each other or to other machines, functionality of the machine or its components, one or more IoT sensors embedded within or external to the machine (e.g., different other sensors of machine IoT device 120). Physical attributes can have states, and these states can undergo changes over a dimension (e.g., time). Two or more changes in states of physical attributes of a machine can be referred to as an experience or history of the machine. According to at least one embodiment, non-physical attributes of a machine can include information describing the machine and its physical attributes, context of the machine relative to other machines or entities, and information describing states of attributes of the machine and changes in those states over time. Two or more changes in states of non-physical attributes of a machine can also be part of an experience or history of the machine. Context of a machine can include information that defines the machine relative to other machines or entities. Non-limiting examples of such contextual data about a machine can include: bill of materials; maintenance schedule; maintenance history; parts replacement history; parts usage history; specifications; three-dimensional models and computer-aided design (CAD) drawing data; fault codes; periodic maintenance schedule; operating manual; usage data, e.g., IoT sensor readings related to the machine; designated staff for the machine; AI and state prediction data; operational history; ownership and applicable standards. Each such contextual data can also have associated change information.

[0044] Then, at 204, the selective sound enablement program 110A, 110B, 110C can create a digital representation of the IWE and a digital twin representation of each machine within the IWE based on the information received at 202. According to one definition, a digital twin refers to a digital representation of an IWE or machine with the IWE, and more broadly, a computerized representation. In an IoT system, a digital twin can represent an evolving virtual data model that simulates an IWE or machine and its experiences and state changes. In embodiments, it can be said that a digital twin stores and tracks 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 the IWE or machine, the context of the IWE or machine relative to other machines or entities, and information that describes the state of attributes 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 comprising the digital twin is created on a non-transitory, tangible storage device. The storage device can be decoupled from the IWE or machine and can be a component in a cloud computing infrastructure available in a distributed network and system, such as the Internet or an IoT system. According to at least one embodiment, the created digital twin can be created on and stored within the data storage device 106 or database 116. Creating a digital twin can also be described as instantiating a digital twin.

[0045] According to at least one embodiment, a digital twin of an IWE or machine can be created contemporaneously with the IWE or machine having similar basic features as the initial IWE or machine. According to at least one other embodiment, a 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 preconfigured data representation of the IWE or machine. At any given point in time, regardless of when the digital twin and the IWE or machine are created, the two can be linked. Linking a digital twin with a corresponding IWE or machine can include, for example, a process by which a data record comprising or representing the digital twin is modified to reference unique identification information of the IWE or machine or to reflect any changes in the physical and / or non-physical attributes of the IWE or machine.

[0046] In this embodiment, at 206, the selective sound allowance program 110A, 110B, 110C can collect sounds / vibrations from the noise-filled environment (e.g., IWE) and from the machines within the IWE. According to at least one embodiment, the sounds / vibrations from the IWE and the machines contained therein can be detected and captured via one or more microphones embedded in the machine IoT devices 120 of the IWE or externally and / or one or more microphone IoT devices 122 that can be deployed throughout the IWE. The captured sounds / vibrations of the IWE and the machines contained therein can be communicated to the selective sound allowance program 110A, 110B, 110C and stored as a corpus within the data store 106 or database 116. Associated attributes of the captured sounds / vibrations (e.g., source, source location, loudness, sound / vibration type, sound / vibration pattern, combined sound / vibration pattern, operating state of the source after the sound / vibration, resulting impact on the source, remedial instructions in response to the sound / vibration) can also be communicated to the selective sound allowance program 110A, 110B, 110C and stored within the corpus.

[0047] Next, at 208, the selective sound allowance program 110A, 110B, 110C can classify the corpus of received sounds / vibrations created at 206. According to at least one embodiment, the selective sound allowance program 110A, 110B, 110C can apply a known continuous supervised machine learning model to the corpus of sounds / vibrations and associated attributes, such that a classification as to whether the sound / vibration is indicative of a problem / incident can be made by the model. According to various embodiments, a problem / incident can include, but is not limited to, a mechanical failure of a machine, an electrical failure of a machine, an out-of-tolerance thermal condition of a machine, and a hazardous condition of an IWE (e.g., fire, smoke, chemical exposure, etc.). A training set of user-defined labeled sounds / vibrations (i.e., sounds / vibrations labeled as problematic or normal) having associated attributes such as those listed above can be uploaded by a user via the software program 108 and can be accessed or received by the selective sound allowance program 110A, 110B, 110C in training the continuous supervised machine learning model. The classification (e.g., problematic, normal) of the sounds / vibrations of the corpus can be stored within the corpus along with the sounds / vibrations and their associated attributes. Further, depending on the classification of the sound / vibration, a noise allowance attribute (e.g., allow sound, eliminate sound) can be defined for the sound / vibration and stored as one of the associated attributes of the sound / vibration within the corpus. According to at least one embodiment, as new sounds / vibrations are added to the corpus by the selective sound allowance program 110A, 110B, 110C, the application of the trained machine learning model to the corpus of sounds / vibrations can be continuous. The user-defined training set and the classified corpus of sounds / vibrations can be used as historical data (i.e., a knowledge corpus) for the selective sound allowance program 110A, 110B, 110C to reference and compare when evaluating future sounds / vibrations.

[0048] Referring now to Figure 3A flowchart illustrating an operation for selectively allowing sound to be heard within noise-canceling headphones during a selective sound-allowing process 300, according to at least one embodiment, is described. At 302, selective sound-allowing procedures 110A, 110B, and 110C can identify a user (e.g., an employee) performing an activity within a noisy environment (e.g., an IWE) and using noise-canceling headphones (e.g., headphone IoT device 118). According to an exemplary embodiment, when performing an activity within the IWE, the employee may need to use noise-canceling headphones, and selective sound-allowing procedures 110A, 110B, and 110C can actively cancel the noise in the IWE within the noise-canceling headphones worn by the employee. When identifying a user in the IWE, selective sound-allowing procedures 110A, 110B, and 110C can also identify the employee's role within the IWE (e.g., job assignment, machine assignment). According to at least one embodiment, employees present in the IWE can be identified and located via a trackable employee-specific badge; their location can be tracked using known technologies for indoor positioning (e.g., RFID, WiFi, Bluetooth) and shared with selective sound permission procedures 110A, 110B, and 110C. According to another embodiment, employees present in the IWE can be identified and located via employee-specific noise-canceling headphones, which are issued to the employees for use when present in the IWE. The location of the noise-canceling headphones can be tracked using known technologies for indoor positioning and shared with selective sound permission procedures 110A, 110B, and 110C. According to yet another embodiment, employees present in the IWE can be identified and located via a predetermined employee work schedule / assignment for the IWE, which can be uploaded to selective sound permission procedures 110A, 110B, and 110C.

[0049] At 304, selective sound enabling programs 110A, 110B, and 110C can monitor sound from the IWE, including sound from machines within the IWE (e.g., machine IoT devices 120). According to embodiments, selective sound enabling programs 110A, 110B, and 110C can receive sound / vibration and associated attributes from one or more microphones embedded in or external to the machine IoT device 120 within the IWE and / or from one or more microphone IoT devices 122 deployed throughout the IWE. Additionally, selective sound enabling programs 110A, 110B, and 110C can identify the current loudness level of the received sound and the source of the received sound. The source of the received sound can be identified based on the microphone that captured the sound. For example, if the received sound is captured by a microphone embedded in or external to a particular machine, then that particular machine in the IWE can be identified as the source of the received sound.

[0050] Next, at 306, the selective sound allowance program 110A, 110B, 110C can determine whether the received sound, while monitoring the sound of the IWE, indicates a problem / incident in the machine in the IWE or its surrounding environment. According to at least one embodiment, the selective sound allowance program 110A, 110B, 110C can reference / compare the received sound / vibration to historical data (i.e., the user-defined training set and the classified corpus of received sounds / vibrations described in process 200) in determining whether the received sound / vibration indicates a problem / incident. According to another embodiment, the selective sound allowance program 110A, 110B, 110C can 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 problematic by the machine learning model can be determined to indicate a problem / incident. In different embodiments, the selective sound allowance program 110A, 110B, 110C can add the received sound / vibration and its associated attributes to the corpus of received sounds / vibrations described above in process 200. In response to determining that the received sound / vibration indicates a problem / incident (step 306, “Y” branch), the selective sound allowance process 300 can isolate 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 does not indicate a problem / incident (step 306, “N” branch), the selective sound allowance process 300 can proceed to step 308.

[0051] At 308, the selective sound allowance program 110A, 110B, 110C can continue to cancel the sound / vibration received within the noise-cancelling headset used by the employee while performing activities within the IWE. The selective sound allowance program 110A, 110B, 110C can utilize known destructive interference techniques to cancel the sound / vibration received within the noise-cancelling headset.

[0052] At 310, the selective sound allowance program 110A, 110B, 110C can identify or predict the severity of the problem / incident indicated by the received sound. The severity of the problem / incident can include, among other things, the location of the problem / incident and the machine identification as derived from the received sound and its corresponding machine source. The severity of the problem / incident can also include the resulting impact associated with the received sound in terms of physical damage to the machine source or IWE and / or physical harm to humans. According to at least one embodiment, the selective sound allowance program 110A, 110B, 110C can utilize historical data in conjunction with data from the digital twin representation of the machine source and / or IWE to identify or predict the severity of the problem / incident indicated by the received sound. Further, utilizing historical data with data from the digital twin simulation of the machine source and IWE, the selective sound allowance program 110A, 110B, 110C can identify the affected area of the problem / incident within the IWE and, as a result, can identify the boundary scope of the problem / incident within the IWE. Depending on the severity of the problem / incident, the boundary scope of the problem / incident can be limited to the identified affected area or can extend beyond it. For example, if the severity of the problem / incident is relatively low (e.g., below a threshold), the selective sound allowance program 110A, 110B, 110C can limit the boundary scope to the identified affected area. However, if the severity of the problem / incident is relatively high (e.g., equal to or above a threshold), the selective sound allowance program 110A, 110B, 110C can extend the boundary scope beyond the identified affected area to potentially include the entire IWE. The threshold can be a preconfigured user-defined scenario (e.g., impact to the sound source), such as, but not limited to, a fire within or near the sound source or structural vibrations of the sound source. The threshold can also be derived from historical data and can include the operating state of the sound source after the sound / vibration or the resulting impact to the sound source.

[0053] Next, at 312, the selective sound allowance program 110A, 110B, 110C can identify all employees located within the identified boundary scope of the problem / incident. The employees within the boundary scope can be tracked and thus identified via their issued employee badges or noise cancelling headphones using known techniques for indoor positioning. The employees within the boundary scope can also be identified by using a pre-established mapping of the IWE to machine locations and employee assignments to machine locations. According to at least one other embodiment, the selective sound allowance program 110A, 110B, 110C can only identify those employees within the boundary scope tasked with correcting the problem / incident.

[0054] Then, at 314, the selective sound permitting program 110A, 110B, 110C can permit the received sound to be heard within the noise cancelling headphones of the identified employees within the boundary range of the issue / incident. According to at least one embodiment, the selective sound permitting program 110A, 110B, 110C can dynamically change (e.g., increase, decrease) the volume level of the received sound such that the identified employees within the boundary range of the issue / incident can hear the received sound through their noise cancelling headphones, be alerted of the issue / incident, and proactively respond. The selective sound permitting program 110A, 110B, 110C can continue to cancel the received sound within the noise cancelling headphones of other employees within the IWE in addition to other noise. According to at least one embodiment, in addition to dynamically changing the volume level of the received sound, the selective sound permitting program 110A, 110B, 110C can temporarily suspend the use of the destructive interference technology within the noise cancelling headphones of the identified employees within the boundary range of the issue / incident. According to at least one embodiment, the selective sound permitting program 110A, 110B, 110C can identify that the issue / incident is being corrected (e.g., via data received from the various sensors of the machine IoT devices 120) and / or an opportunity to cancel or reduce the issue / incident, and accordingly, cancel the received sound within the noise cancelling headphones of the identified employees within the boundary range of the issue / incident. According to at least one embodiment, in addition to permitting the received sound to be heard within the noise cancelling headphones, the selective sound permitting program 110A, 110B, 110C can provide configured action items (i.e., remedial instructions) and a message to the identified employees within the boundary range of the issue / incident in response to the received sound. The message can include a warning, an alert, or a recommended safety action in response to the issue / incident. The configured action items and message can be in the form of an audio message communicated through the noise cancelling headphones (e.g., the headset IoT devices 118) of the identified employees within the boundary range of the issue / incident. The configured action items and message can also be in the form of a text message communicated to the machines (e.g., the machine IoT devices 120) of the IWE.

[0055] It is recognized that, Figure 2 and 3 Only one implementation is provided for illustration and no inference is to be drawn concerning the limitations of different embodiments that can be implemented. Numerous modifications can be made to the depicted environments based on design and implementation requirements.

[0056] Figure 4 is a block diagram 400 of the internal and external components of the client computing device 102, the server 112, and the headset IoT device 118 depicted in Figure 1 FIG. 1, in accordance with embodiments of the present application. It should be appreciated that Figure 4The depicted environments are only illustrative and do not imply any limitation with regard to the environments in which different embodiments can be implemented. Many modifications can be made to the depicted environments, based upon the design and implementation requirements.

[0057] Data processing systems 402, 404 represent any electronic device able to execute machine-readable program instructions. Data processing systems 402, 404 can represent a smart phone, a computer system, a PDA, or other electronic devices. Examples of computing systems, environments, and / or configurations that can be represented by data processing systems 402, 404 include, but are not limited to, a personal computer system, a server computer system, a thin client, a thick client, a handheld or laptop device, a multiprocessor system, a microprocessor-based system, a networked PC, a minicomputer system, an IoT device, and distributed cloud computing environments that include any of the above systems or devices.

[0058] Client computing device 102, server 112, and earphone IoT device 118 can include Figure 4 The respective sets of internal components 402a, b, c and external components 404a, b, c are described in more detail below. Each set of internal components 402 includes one or more processors 420, one or more computer-readable RAM 422, and one or more computer-readable ROMs 424 on one or more buses 426, and one or more operating systems 428 and one or more computer-readable tangible storage devices 430. One or more operating systems 428, software programs 108 in client computing device 102 and selective sound enabling program 110A, selective sound enabling program 110B in server 112 and selective sound enabling program 110C in earphone IoT device 118 are stored on one or more respective computer-readable tangible storage devices 430 for execution by one or more respective processors 420 via one or more respective RAMs 422, which typically include cache memory. In this manner, computer- readable tangible storage device 430 acts as memory 422 for one or more processors 420, and the software programs 108 and selective sound enabling programs 110A, B, C take the form of software programs executing on one or more computer systems / capacities created and provided by one or more processors 420. Figure 4 In the illustrated embodiment, each computer-readable tangible storage device 430 is a magnetic disk storage device of an internal hard drive. Alternatively, each computer- readable tangible storage device 430 is a semiconductor memory device, such as a ROM 424, EPROM, flash memory or any other computer-readable tangible storage device that can store computer programs and digital information.

[0059] Each set of internal components 402a, b, c also includes an R / W drive or interface 432 for reading from and writing to one or more portable computer-readable physical storage devices 438 (such as CD-ROM, DVD, Memory Stick, magnetic tape, disk, optical disc, or semiconductor storage devices). Software programs (such as selective sound enable programs 110A, 110B, 110C) may be stored on one or more of the respective portable computer-readable physical storage devices 438, read via the respective R / W drive or interface 432, and loaded into the respective hard disk drive 430.

[0060] Each group 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 links. The software program 108 and selective sound enabler 110A in the client computing device 102, the selective sound enabler 110B in the server 112, and the selective sound enabler 110C in the headset IoT device 118 can be downloaded from an external computer to the client computing device 102, server 112, and headset IoT device 118 via a network (e.g., the Internet, a local area network, or another wide area network) and the corresponding network adapter or interface 436. From the network adapter or interface 436, the software program 108 and selective sound enabler 110A in the client computing device 102, the selective sound enabler 110B in the server 112, and the selective sound enabler 110C in the headset IoT device 118 are loaded into the corresponding hard disk drive 430. The network may include copper wires, fiber optics, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers.

[0061] Each of these external components 404a, b, and c may include a computer display monitor 444, a keyboard 442, and a computer mouse 434. External components 404a, b, and c may also include a touchscreen, a virtual keyboard, a touchpad, a pointing device, and other human-machine interface devices. Each group of internal components 402a, 402b, and 402c also includes a device driver 440 that interfaces with the computer display monitor 444, keyboard 442, and computer mouse 434. Device driver 440, R / W driver or interface 432, and network adapter or interface 436 include hardware and software (stored in storage device 430 and / or ROM 424).

[0062] It should be understood in advance that although this disclosure includes a detailed description of cloud computing, the implementation of the teachings described herein is not limited to a cloud computing environment. Rather, embodiments of the invention can be implemented in conjunction with any other type of computing environment now known or developed hereafter.

[0063] Cloud computing is a service delivery model that enables convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing power, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with the service provider. This cloud model may include at least five features, at least three service models, and at least four deployment models.

[0064] The characteristics are as follows:

[0065] On-demand self-service: Cloud consumers can unilaterally and automatically provide computing power, such as server time and network storage, as needed, without requiring human interaction with the service provider.

[0066] Extensive network access: Capabilities are available through networks and accessed via standard mechanisms that facilitate the use of heterogeneous thin client or thick client platforms (e.g., mobile phones, laptops, and PDAs).

[0067] Resource pooling: A provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, where different physical and virtual resources are dynamically assigned and reassigned as needed. There is a sense of location independence because consumers typically do not have control or knowledge of the exact location of the resources provided, but may be able to specify the location at a higher level of abstraction (e.g., country, state, or data center).

[0068] Rapid flexibility: The ability to provide capacity quickly and flexibly, automatically scaling down and up rapidly in some situations to scale up rapidly. For consumers, the available supply capacity often appears unlimited and can be purchased in any quantity at any time.

[0069] Measuring services: Cloud systems automatically control and optimize resource usage by leveraging metering capabilities at a level of abstraction appropriate to the service type (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency to both service providers and consumers.

[0070] The service model is as follows:

[0071] Software as a Service (SaaS): This provides consumers with the ability to use the provider's applications running on cloud infrastructure. Applications can be accessed from different client devices via thin client interfaces such as web browsers (e.g., web-based email). Consumers do not manage or control the underlying cloud infrastructure, including the network, servers, operating system, storage, or even individual application capabilities, with possible exceptions such as limited user-specific application configuration settings.

[0072] Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations.

[0073] Infrastructure as a Service (laaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls).

[0074] Deployment models are as follows:

[0075] Private cloud: the cloud infrastructure is operated solely for an organization. It can be managed by the organization or a third party and can exist on-premises or off-premises.

[0076] Community cloud: the cloud infrastructure is shared by several organizations and supports mission-oriented or business-oriented communities, such as mission-oriented business lines or schools. It can be managed by the organizations or a third party and can exist on-premises or off-premises.

[0077] Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services.

[0078] Hybrid cloud: the cloud infrastructure is a combination of two or more types of cloud (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technologies that enable data and application portability.

[0079] Referring now to the drawings Figure 5FIG. 4A illustrates an example of a cloud computing environment 50. As shown, cloud computing environment 50 includes one or more cloud computing nodes 100 with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone 54A, desktop computer 54B, laptop computer 54C, and / or automobile computer system 54N can communicate. Nodes 100 can communicate with one another. They can be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment 50 to offer infrastructure, platforms and / or software as services with Figure 5 The types of computing devices 54A-N shown in FIG. 4A are intended to be illustrative only and computing nodes 100 and cloud computing environment 50 can communicate with any type of computerized devices over any type of network and / or network addressable connection (e.g., using a web browser).

[0080] Referring now to FIG. 4B, a high-level cloud computing environment is shown Figure 6 which are intended to be illustrative only and embodiments of the application are not limited in this regard. As depicted, the following layers and corresponding functions are provided: Figure 6

[0081] Hardware and software layer 60 includes hardware and software components. Examples of hardware components include: mainframes 61; RISC (Reduced 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.

[0082] Virtualization layer 70 provides an abstraction layer from which the following examples of virtual entities can be provided: virtual servers 71; virtual storage 72; virtual networks 73, including virtual private networks; virtual applications and operating systems 74; and virtual clients 75.

[0083] ​In one example, management layer 80 can provide the functions described below. Resource provisioning 81 provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing 82 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources can 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 such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment 85 provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.

[0084] Workloads layer 90 provides examples of functionality for which the cloud computing environment can be utilized. Examples of workloads and functions which can be provided from this layer include: mapping and navigation 91; software development and lifecycle management 92; virtual classroom education delivery 93; data analytics processing 94; transaction processing 95; and selective sound admission 96. Selective sound admission 96 can involve selectively admitting sound within noise cancelling headphones.

[0085] The description of the different embodiments of the application has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A computer-based method for allowing selective sound cancellation in headphones, the method comprising: Receive sound from a noise-filled environment, wherein the source of the sound is a machine within the noise-filled environment; The sound indicates a problem within the noise-filled environment; Identify the severity of the problem; Identify users within the boundary range of the problem, wherein the boundary range is in part based on the severity of the problem, and wherein the users are wearing noise-canceling headphones that actively eliminate the noise filling the environment. as well as The sound is allowed to be heard within the noise-cancelling headphones of the identified user. Identifying the severity of the problem further includes: The severity of the problem is identified by using historical data combined with data from the digital twin representation of the noisy environment and the machine; The historical data is used in conjunction with data from the digital twin representation of the noise-filled environment and the machine to identify the affected areas of the problem within the noise-filled environment.

2. The method according to claim 1, wherein, The sound is captured by one or more microphones embedded in or outside the machine, and the sound may include vibrations.

3. The method according to claim 1, further comprising: Receive information about the noise-filled environment and one or more machines present in the noise-filled environment, wherein the information includes physical and non-physical properties of the noise-filled environment and the one or more machines; Create a digital twin representation of the noise-filled environment and a digital twin representation of the one or more machines; Sound is collected from the noise-filled environment and the one or more machines, wherein the collected sound includes associated attributes; Create an audio corpus that includes the collected audio; as well as By applying a supervised machine learning model to the collected sounds and associated attributes, the collected sounds in the sound corpus are classified as problematic or normal.

4. The method according to claim 1, wherein, Determining that the sound indicates a problem within the noise-filled environment further includes: The sound is compared with a corpus of classified sounds in the noise-filled environment, wherein the classification of the classified sounds in the noise-filled environment includes problem classification or normal classification.

5. The method according to claim 1, wherein, If the severity of the problem is below a threshold, then the boundary of the problem is limited to the area of ​​influence of the problem within the noise-filled environment.

6. The method according to claim 1, wherein, Identifying the user within the defined boundary range includes tracking the user via a user-specific badge or a user-specific noise-cancelling headset.

7. A computer system, the 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, wherein the computer system is capable of performing the steps of the method of any one of claims 1-6.

8. A computer program product, the computer program product comprising: One or more computer-readable tangible storage media and program instructions stored on at least one of the one or more tangible storage media, the program instructions being executable by a processor capable of performing the steps of the method of any one of claims 1-6.

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