Selective allowance of sound in noise cancellation headset in an industrial work environment

According to one embodiment, a method, computer system, and computer program product for allowing selective sounds within a noise cancellation headset. The embodiment may include receiving a sound from a noise-filled environment. A source of the sound is a machine within the noise-filled environment. The embodiment may include determining that the sound is indicative of a problem within 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 cancellation headset which is actively cancelling sounds of the noise-filled environment. The embodiment may include allowing the sound to be heard within the noise cancellation headsets of the identified user.

BACKGROUND

The present invention relates generally to the field of computing, and more particularly to active noise control.

Active noise control (ANC), also known as noise cancellation (NC), or active noise reduction (ANR), is a method for reducing unwanted sound by the addition of a second sound specifically designed to cancel the unwanted sound. Sound is a pressure wave which consists of alternating periods of compression and rarefaction. A noise-cancellation speaker emits a sound wave with the same amplitude but with inverted phase relative to an original sound (e.g., an unwanted sound). In a process called interference, the waves combine to form a new wave and effectively cancel each other out. This effect is called destructive interference. Modern ANC is generally achieved using analog circuits 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 creates a sound wave directly proportional to the amplitude of the original waveform, creating destructive interference and effectively reducing the volume of the perceivable noise. Noise-cancelling headphones are headphones that reduce unwanted or unsafe sound levels using ANC. For example, in the context of an industrial work environment, employee headsets may employ ANC to mitigate the risk of noise induced hearing loss (NIHL).

SUMMARY

According to one embodiment, a method, computer system, and computer program product for allowing selective sounds within a noise cancellation headset. The embodiment may include receiving a sound from a noise-filled environment. A source of the sound is a machine within the noise-filled environment. The embodiment may include determining that the sound is indicative of a problem within 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 cancellation headset which is actively cancelling sounds of the noise-filled environment. The embodiment may include allowing the sound to be heard within the noise cancellation headsets of the identified user.

DETAILED DESCRIPTION

The present invention relates generally to the field of computing, and more particularly to active noise control. The following described exemplary embodiments provide a system, method, and program product to, among other things, identify a workplace machine sound as an indication of a potential or occurring accident and, accordingly, selectively allow the sound to be heard within a noise cancellation headset. Therefore, the present embodiment has the capacity to improve the technical field of active noise control applications by allowing a sound to be heard within a noise cancellation headset when the sound has been identified as an indication of a potential or occurring problem within an industrial machine, thus improving employee safety when utilizing a noise cancellation headset in an industrial work environment.

As previously described, ANC is a method for reducing unwanted sound by the addition of a second sound specifically designed to cancel the unwanted sound. Sound is a pressure wave which consists of alternating periods of compression and rarefaction. A noise-cancellation speaker emits a sound wave with the same amplitude but with inverted phase relative to an original sound (e.g., an unwanted sound). In a process called interference, the waves combine to form a new wave and effectively cancel each other out. This effect is called destructive interference. Modern ANC is generally achieved using analog circuits 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 creates a sound wave directly proportional to the amplitude of the original waveform, creating destructive interference and effectively reducing the volume of the perceivable noise. Noise-cancelling headphones are headphones that reduce unwanted or unsafe sound levels using ANC. For example, in the context of an industrial work environment, employee headsets may employ ANC to mitigate the risk of NIHL.

In industrial work environments (e.g., a machine shop floor) employees are exposed to high levels of noise which may be damaging to employee hearing and possibly result in NIHL. In fact, World Health Organization figures indicate that noise exposure contributes to a notable percentage of workplace related health issues. In an effort to mitigate the risk of hearing damage and NIHL, it is common for employees in industrial work environments to utilize noise cancellation headsets. Such headsets may implement known methods of destructive interference to cancel out the surrounding industrial noise (e.g., noise from industrial machines) as employees perform their work comfortably. However, in any industrial work environment, a sound originating from a machine or its surroundings may be an indication of a current or future problem/accident within the machine or its surroundings. A resulting problem or accident within a machine or its surroundings may present financial harm (e.g., repair costs) to a company as well as physical harm to employees. If noise cancellation headsets are utilized by employees to cancel all noise in an industrial work environment, then the employees present within the environment may not be able to take immediate corrective action (e.g., shutdown, repair) or evacuate the work environment in response to the sound. It may therefore be imperative to have a system in place to selectively allow a sound, from an industrial work environment, to be heard within a noise cancellation headset of a present employee if that sound is indicative of a current or future problem/accident within the industrial work environment. Thus, embodiments of the present invention may be advantageous to, among other things, identifying problematic sounds of a machine or its surroundings, allowing such problematic sounds to be heard by employees wearing noise cancelling headsets, and enhancing employee safety within an industrial work environment. The present invention does not require that all advantages need to be incorporated into every embodiment of the invention.

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 within the environment. Additionally, a corpus of machine and surrounding sounds/vibrations harvested from the IWE may be created. Harvested sounds/vibrations within the corpus may be classified, using machine learning, as to whether they are indicative of a problem or not. According to at least one embodiment, employees present within an IWE may be identified and sounds within the IWE may be monitored. If a monitored sound is determined to be indicative of a problem (e.g., an accident) based on comparison to a corpus of classified IWE sounds, a severity of the problem may be identified using a digital twin representation of the sound source (e.g., a machine) and the corpus of classified IWE sounds. Employees present within a boundary range of the problem may be identified and the monitored sound indicative of the problem may be allowed to be heard within noise cancelling headsets utilized by the identified employees.

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 if a sound from any machine or its surroundings is related to predicted damage in the machine or any accident in the surroundings, and accordingly build a corpus of machine and surrounding sounds based on an IoT feed from an IWE. The proposed selective sound allowance system will selectively allow learned sounds so that employees present within an area associated with the damage or accident may hear the sound despite the use of noise cancellation headsets.

According to at least one embodiment, if a sound of an IWE is determined to be an indication of a current or future accident, the proposed system may identify the degree of severity of the accident (e.g., location of accident derived from location of machine producing the sound, type of machine producing the sound, impact of accident on machine and its surroundings) based on analysis of the sound, and dynamically adjust the level of loudness of the sound within noise cancellation headsets of identified employees so that they may be alerted to and proactively respond the accident.

According to at least one embodiment, based on historical learning and use of digital twin simulation, the proposed system may identify an impacted area of the accident, as well as impacted employees, and accordingly identify a boundary range within the IWE where the sound may be allowed to be heard within noise cancellation headsets of the impacted employees.

According to at least one embodiment, if the degree of severity of an accident is comparatively low, then the proposed system may identify only those employees concerned with remediation of the accident (e.g., those who will be rectifying the problem) within the IWE. For other employees in the IWE, the proposed system may continue to cancel the sound, in addition to other noise, within noise cancellation headsets of the other employees.

According to at least one embodiment, if the proposed system identifies that an accident is being rectified, and the chances of future accidents are eliminated or being reduced, then the proposed system may cancel the sound associated with the accident.

According to at least one embodiment, the proposed system may apply a continuous supervised machine learning model to harvested sounds/vibrations from an IWE so that sounds/vibrations and their associated attributes (e.g., source, source location, loudness, sound/vibration type, combination sound/vibration pattern, operational status of source after sound/vibration) may be learned, and an impact of a sound/vibration, in terms of resulting physical damage to an infrastructure/source and/or physical harm to human beings, may be predicted.

According to at least one embodiment, the proposed system may cancel, via the use of noise cancellation headsets, sounds which are not indicative of a current or future problem/accident and may allow select sounds to be heard within the noise cancellation headsets based on the corpus of classified IWE sounds.

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

The following described exemplary embodiments provide a system, method, and program product to determine that a sound from a machine, or its IWE surroundings, is indicative of a current or predicted problem in the machine or within the surroundings and, accordingly, isolate and allow the sound to be heard within noise cancellation headsets with adjusted loudness of the sound.

Referring toFIG. 1, an exemplary networked computer environment100is depicted, according to at least one embodiment. The networked computer environment100may include a client computing device102, a server112, a headset IoT device118, a machine IoT device120, and a microphone IoT device122interconnected via a communication network114. According to at least one implementation, the networked computer environment100may include a plurality of client computing devices102, headset IoT devices118, machine IoT devices120, microphone IoT devices122, and servers112, of which only one of each is shown for illustrative brevity. Additionally, in one or more embodiments, the client computing device102, the server114, and the headset IoT device118may each host a selective sound allowance program110A,110B,110C. In one or more other embodiments, the selective sound allowance program110A,110B,110C may be partially hosted on client computing device102, server114, and on headset IoT device118so that functionality may be separated among the devices.

Client computing device102may include a processor104and a data storage device106that is enabled to host and run a software program108and a selective sound allowance program110A and communicate with the server112, headset IoT device118, machine IoT device120, and microphone IoT device122via the communication network114, in accordance with one embodiment of the invention. Client computing device102may be, for example, a mobile device, a telephone, a personal digital assistant, a netbook, a laptop computer, a tablet computer, a desktop computer, or any type of computing device capable of running a program and accessing a network. As will be discussed with reference toFIG. 4, the client computing device102may include internal components402aand external components404a, respectively.

The server computer112may be a laptop computer, netbook computer, 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 program110B and a database116and communicating with the client computing device102, headset IoT device118, machine IoT device120, and microphone IoT device122via the communication network114, in accordance with embodiments of the invention. As will be discussed with reference toFIG. 4, the server computer112may include internal components402band external components404b, respectively. The server112may 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 server112may also be located in a cloud computing deployment model, such as a private cloud, community cloud, public cloud, or hybrid cloud.

Headset IoT device118may be a circumaural or over ear headphones, supra-aural headphones, a headset, and/or any other headset IoT device118known in the art for implementing noise cancellation using destructive interference techniques that is capable of connecting to the communication network114, and transmitting and receiving data with the client computing device102, machine IoT device120, microphone IoT device122, and the server112. According to at least one implementation, the networked computer environment100may include a plurality of headset IoT devices118. As will be discussed with reference toFIG. 4, the headset IoT device118may include internal components402cand external components404c, respectively.

Machine IoT device120may be an IoT enabled machine (e.g., an industrial machine within an IWE) with a microphone, and various other sensors, embedded in or external to the machine, that is capable of connecting to the communication network114, and transmitting and receiving data with the client computing device102, headset IoT device118, and the server112. The microphone of machine IoT device120may include, for example, one or more piezoelectric microphone sensors installed to capture the vibration from different portions of a machine or structure. The various other sensors of machine IoT device120may include, for example, heat sensors, weight sensors, and pressure sensors, and may gather data of machine IoT device120on a continuous basis. According to at least one implementation, the networked computer environment100may include a plurality of machine IoT devices120.

Microphone IoT device122may be a microphone and/or any other microphone IoT device122known in the art for capturing audio output (e.g., sound/vibration) that is capable of connecting to the communication network114, and transmitting and receiving data with the client computing device102, the headset IoT device118, and the server112. According to at least one implementation, the networked computer environment100may include a plurality of microphone IoT devices122.

According to the present embodiment, the selective sound allowance program110A,110B,110C may be a program capable of receiving information of a work environment and machines contained therein to create digital twin representations for the work environment and the machines, classifying sounds of a work environment and machines contained therein to create a corpus of learned sounds indicative of a problem, monitoring sounds of a work environment and machines contained therein to determine whether a sound indicative of a problem has been received, identifying a severity of the problem as well as employees near the problem or concerned with remediation of the problem, and allowing a sound indicative of a problem within a work environment or a machine contained therein to be heard through noise cancellation headsets of identified employees. The work environment digital twin creation and sound classification method is explained in further detail below with respect toFIG. 2. The selective sound allowance method is explained in further detail below with respect toFIG. 3.

Referring now toFIG. 2, an operational flowchart for creating digital twin representations of an IWE and creating a corpus of learned IWE sounds in a digital twin creation and sound classification process200is depicted according to at least one embodiment. At202, the selective sound allowance program110A,110B,110C may receive information of an IWE and machines present within the IWE. Utilizing the software program108, a user may upload the information which may be accessed or received by the selective sound allowance program110A,110B,110C. The information may include physical and non-physical attributes of the IWE such as, but not limited to, physical dimensions of an interior space of the IWE, a floor plan of the IWE, number of employees assigned to work within the IWE, listing of employee identification/badge numbers of employees assigned to work within the IWE, 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 device122) placement within the IWE. Physical attributes of an IWE may have states, and these states may undergo change across a dimension, such as time. Two or more changes to states of the physical attributes of an IWE may be referred to as experiences or a history of the IWE. Furthermore, two or more changes to states of the non-physical attributes of an IWE may also be part of the experiences or the history of the IWE.

The information may also include physical and non-physical attributes of every machine (e.g., machine IoT device120) within the IWE. According to at least one embodiment, physical attributes of a machine may include physical dimensions of a machine, machine type, material composition of a machine and its individual components (to varying degrees of granularity), the physical arrangements or configurations of a machine relative to other machines, the physical arrangements or configurations of components of a machine relative to one another or relative to other machines, functions of a machine or its components, one or more IoT sensors embedded within or external to a machine (e.g., the various other sensors of machine IoT device120). The physical attributes may have states, and these states may undergo change across a dimension, such as time. Two or more changes to states of the physical attributes of a machine may be referred to as experiences or a history of the machine. According to at least one embodiment, non-physical attributes of a machine may include information that describes a machine and its physical attributes, a context of a machine relative to other machines or entities, and information that describes states of a machine's attributes and the changes in those states over time. Two or more changes to states of the non-physical attributes of a machine may also be part of the experiences or the history of the machine. The context of a machine may include information that defines the machine relative to other machines or entities. Non-limiting examples of such context data may include, with respect to a machine: bill of material; maintenance plans; maintenance history; part replacement history; part usage history; specifications; 3-dimensional model and computer-aided design (CAD) drawing data; fault codes; scheduled maintenance plans; operating manuals; usage data, such as IoT sensor readings associated with the machine; assigned employees of a machine; AI and state prediction data; operating history; ownership; and applicable standards. Each such type of context data may also have associated change information.

Then, at204, the selective sound allowance program110A,110B,110C may create a digital representation of the IWE and digital twin representations for every machine within the IWE based on the information received at202. According to one definition, a digital twin refers to a digital representation of an IWE or a machine with the IWE, and more broadly, a computerized representation. In IoT systems, a digital twin can represent an evolving virtual data model that mimics the IWE or machine as well as its experiences and state changes. The digital twin may be said, in an embodiment, 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 physical and non-physical attributes of the IWE or machine, a context of the IWE or machine relative to other machines or entities, and information that describes 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 comprising the digital twin is created on a non-transitory tangible storage device. The storage device may be decoupled 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, created digital twins may be created on and stored within the data storage device106or the database116. Creating a digital twin may also be described as instantiating the digital twin.

According to at least one embodiment, a digital twin of an IWE or machine may be created at the same time as the IWE or machine with similar base features as the initial IWE or machine. According to at least one other embodiment, the digital twin may be created at a different time than the IWE or machine (for example, before or after the IWE or machine). For example, the digital twin may be created via a preconfigured data representation of a IWE or machine. At any given point in time, regardless of when the digital twin and the IWE or machine are created, the two may be linked. Linking a digital twin and a corresponding IWE or machine may include, for example, a process by which a data record including or representing the digital twin is modified to refer to unique identifying information of the IWE or machine or to reflect any changes to physical and/or non-physical attributes of the IWE or machine.

In the present embodiment, at206, the selective sound allowance program110A,110B,110C may harvest sounds/vibrations from a noise-filled environment such as, the IWE and from machines within the IWE. According to at least one embodiment, sounds/vibrations from the IWE and the machines contained therein may be detected and captured via one or more microphones embedded in, or external to, machine IoT devices120of the IWE and/or one or more microphone IoT devices122which may be deployed throughout the IWE. Captured sounds/vibrations of the IWE and the machines contained therein may be transmitted to the selective sound allowance program110A,110B,110C and stored as a corpus within the data storage device106or the database116. Associated attributes of the captured sounds/vibrations (e.g., source, source location, loudness, sound/vibration type, sound/vibration pattern, combination sound/vibration pattern, operational status of source after sound/vibration, resulting impact on source, remediation instructions in response to sound/vibration) may also be transmitted to the selective sound allowance program110A,110B,110C and stored within the corpus.

Next, at208, the selective sound allowance program110A,110B,110C may classify the corpus of received sounds/vibrations created at206. According to at least one embodiment, the selective sound allowance program110A,110B,110C may apply a known continuous supervised machine learning model to the corpus of sounds/vibrations and associated attributes so that a classification as to whether or not a sound/vibration is indicative of a problem/accident may be made by the model. According to various embodiments, a problem/accident may include, but is not limited to, a mechanical malfunction of a machine, an electrical malfunction of a machine, an out of tolerance heat condition of a machine, and a hazardous condition (e.g., fire, smoke, chemical exposure, etc.) of the IWE. A user defined training set of labeled sounds/vibrations (i.e., sounds/vibrations labeled as problematic or normal), with associated attributes such as those listed above, may be uploaded by the user via the software program108and may be accessed or received by the selective sound allowance program110A,110B,110C in training a continuous supervised machine learning model. The classification (e.g., problematic, normal) for a sound/vibration of the corpus may be stored within the corpus along with the sound/vibration and its associated attributes. Moreover, depending on the classification of the sound/vibration, a noise cancellation attribute (e.g., cancel sound, allow sound) may be defined for the sound/vibration and stored within the corpus as one of the associated attributes of the sound/vibration. According to at least one embodiment, application of the trained machine learning model to the corpus of sounds/vibrations may be continuous as new sounds/vibrations are added to the corpus by the selective sound allowance program110A,110B,110C. The user defined training set and the classified corpus of sounds/vibrations may serve as historical data (i.e., a knowledge corpus) for reference and comparison by the selective sound allowance program110A,110B,110C in evaluating future sounds/vibrations.

Referring now toFIG. 3, an operational flowchart for selectively allowing a sound to be heard within noise cancellation headsets in a selective sound allowance process300is depicted according to at least one embodiment. At302, the selective sound allowance program110A,110B,110C may identify users (e.g., employees) who are performing activity within a noise-filled environment such as the IWE and utilizing noise cancellation headsets (e.g., headset IoT device118). According to an example embodiment, employee use of noise cancellation headsets may be required when performing activity within the IWE and the selective sound allowance program110A,110B,110C may be actively cancelling noise of the IWE within the noise cancellation headsets worn by employees. In identifying a user in the IWE, the selective sound allowance program110A,110B,110C may also identify a role (e.g., a work assignment, a machine assignment) of the employee within the IWE. According to at least one embodiment, an employee present within the IWE may be identified and located via a trackable employee specific badge; the location of which may be tracked using known technologies for indoor positioning (e.g., radio frequency identification, WiFi, Bluetooth) and shared with the selective sound allowance program110A,110B,110C. According to another embodiment, an employee present within the IWE may be identified and located via an employee specific noise cancellation headset issued to the employee for use when present in the IWE. A location of the noise cancellation headset may be tracked using known technologies for indoor positioning and shared with the selective sound allowance program110A,110B,110C. According to yet another embodiment, an employee present within the IWE may be identified and located via a pre-determined employee work schedule/assignment for the IWE which may be uploaded to the selective sound allowance program110A,110B,110C.

At304, the selective sound allowance program110A,110B,110C may monitor sounds of the IWE, including sounds from machines (e.g., machine IoT device120) within the IWE. According to an embodiment, the selective sound allowance program110A,110B,110C may receive sounds/vibrations, along with associated attributes, from one or more microphones embedded in, or external to, machine IoT devices120of the IWE and/or one or more microphone IoT devices122which may be deployed throughout the IWE. Additionally, the selective sound allowance program110A,110B,110C may identify a current level of loudness of a received sound and a source of the received sound. The source of a received sound may be identified based on the microphone which captured the sound. For example, a particular machine of the IWE may be identified as the source of a received sound if the received sound was captured by a microphone embedded in, or external to, the particular machine.

Next, at306, the selective sound allowance program110A,110B,110C may determine if a sound received while monitoring sounds of the IWE is indicative of a problem/accident within a machine or its surroundings in the IWE. According to at least one embodiment, the selective sound allowance program110A,110B,110C may reference/compare the received sound/vibration against historical data (i.e., the user defined training set and the classified corpus of received sounds/vibrations described in process200) in determining whether the received sound/vibration is indicative of a problem/accident. According to another embodiment, the selective sound allowance program110A,110B,110C may apply the machine learning model of process200to determine whether the received sound/vibration is indicative of a problem/accident. For example, a sound/vibration classified by the machine learning model as problematic may be determined as indicative of a problem/accident. In various embodiments, the selective sound allowance program110A,110B,110C may add the received sound/vibration, along with its associated attributes, to the corpus of received sounds/vibrations described above in process200. In response to determining the received sound/vibration is indicative of a problem/accident (step306, “Y” branch), the selective sound allowance process300may isolate the received sound/vibration from other sounds/vibrations of the IWE and proceed to step310. In response to determining the received sound/vibration is not indicative of a problem/accident (step306, “N” branch), the selective sound allowance process300may proceed to step308.

At308, the selective sound allowance program110A,110B,110C may continue to cancel the received sound/vibration within the noise cancellation headsets utilized by employees when performing activity within the IWE. The selective sound allowance program110A,110B,110C may utilize known destructive interference techniques to cancel the received sound/vibration within the noise cancellation headsets.

At310, the selective sound allowance program110A,110B,110C may identify or predict a severity of the problem/accident indicated by the received sound. The severity of the problem/accident may include, among other things, a location of the problem/accident and a machine identification as derived from the received sound and its corresponding machine source. The severity of the problem/accident may also include a resulting impact, in terms of physical damage to the machine source or the IWE and/or physical harm to human beings, associated with the received sound. According to at least one embodiment, the selective sound allowance program110A,110B,110C may utilize historical data in combination with data from digital twin representations of the machine source and/or the IWE in identifying or predicting the severity of the problem/accident indicated by the received sound. Furthermore, utilizing historical data with data from digital twin simulations of the machine source and the IWE, the selective sound allowance program110A,110B,110C may identify an impacted area, within the IWE, of the problem/accident, and, accordingly, may identify a boundary range for the problem/accident within the IWE. Depending on the severity of the problem/accident, the boundary range for the problem/accident may be limited to the identified impacted area or may expand beyond it. For example, if the severity of the problem/accident is comparatively low (e.g., below a threshold value), then the selective sound allowance program110A,110B,110C may limit the boundary range to the identified impacted area. Whereas, if the severity of the problem/accident is comparatively high (e.g., equal to or above a threshold value), then the selective sound allowance program110A,110B,110C may expand the boundary range beyond the identified impacted area to potentially include the entire IWE. Threshold values may be pre-configured user defined situations (e.g., impacts on sound sources) such as, but not limited to, fire within or near a sound source or structural vibration of a sound source. Threshold values may also be derived from historical data and may include an operational status of the sound source after sound/vibration or a resulting impact on the sound source.

Next, at312, the selective sound allowance program110A,110B,110C may identify all employees located within the identified boundary range for the problem/accident. Employees within the boundary range may be tracked and consequently identified via their issued employee badges or noise cancellation headsets using known technologies for indoor positioning. Employees within the boundary range may also be identified via the use a pre-established mapping of the IWE with machine locations and employee assignments to machine locations. According to at least one other embodiment, the selective sound allowance program110A,110B,110C may identify only those employees within the boundary range tasked with rectifying the problem/accident.

Then, at314, the selective sound allowance program110A,110B,110C may allow the received sound to be heard within the noise cancellation headsets of identified employees within the boundary range for the problem/accident. According to at least one embodiment, the selective sound allowance program110A,110B,110C may dynamically alter (e.g., increase, decrease) a loudness level of the received sound so that the identified employees within the boundary range can hear the received sound through their noise cancellation headsets, be alerted to the problem/accident, and proactively respond. The selective sound allowance program110A,110B,110C may continue to cancel the received sound, in addition to other noise, within the noise cancellation headsets of other employees within the IWE. According to at least one embodiment, in addition to dynamically altering the loudness level of the received sound, the selective sound allowance program110A,110B,110C may temporarily suspend the use of destructive interference techniques within the noise cancellation headsets of identified employees within the boundary range for the problem/accident. According to at least one embodiment, the selective sound allowance program110A,110B,110C may identify that the problem/accident is being rectified (e.g., via data received from the various sensors of machine IoT device120) and/or that chances of the problem/accident are eliminated or being reduced, and, accordingly, cancel the received sound within the noise cancellation headsets of identified employees within the boundary range for the problem/accident. According to at least one embodiment, in addition to allowing the received sound to be heard within the noise cancellation headsets, the selective sound allowance program110A,110B,110C may provide a configured action item (i.e., remediation instructions), as well as a message, to identified employees within the boundary range for the problem/accident in response to the received sound. The message may include a warning, an alert, or recommended safety actions in response to the problem/accident. Configured action items and messages may be in the form of audio messages transmitted through the noise cancellation headsets (e.g., headset IoT device118) of identified employees within the boundary range for the problem/accident. Configured action items and messages may also be in the form of text messages transmitted to machines (e.g., machine IoT device120) of the IWE.

It may be appreciated thatFIGS. 2 and 3provide only an illustration of one implementation and do not imply any limitations with regard to how different embodiments may be implemented. Many modifications to the depicted environments may be made based on design and implementation requirements.

FIG. 4is a block diagram400of internal and external components of the client computing device102, the server112, and the headset IoT device118depicted inFIG. 1in accordance with an embodiment of the present invention. It should be appreciated thatFIG. 4provides 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.

The client computing device102, the server112, and the headset IoT device118may include respective sets of internal components402a,b,cand external components404a,b,cillustrated inFIG. 4. Each of the sets of internal components402include one or more processors420, one or more computer-readable RAMs422, and one or more computer-readable ROMs424on one or more buses426, and one or more operating systems428and one or more computer-readable tangible storage devices430. The one or more operating systems428, the software program108and the selective sound allowance program110A in the client computing device102, the selective sound allowance program110B in the server112, and the selective sound allowance program110C in the headset IoT device118are stored on one or more of the respective computer-readable tangible storage devices430for execution by one or more of the respective processors420via one or more of the respective RAMs422(which typically include cache memory). In the embodiment illustrated inFIG. 4, each of the computer-readable tangible storage devices430is a magnetic disk storage device of an internal hard drive. Alternatively, each of the computer-readable tangible storage devices430is a semiconductor storage device such as ROM424, EPROM, flash memory or any other computer-readable tangible storage device that can store a computer program and digital information.

Each set of internal components402a,b,calso includes a R/W drive or interface432to read from and write to one or more portable computer-readable tangible storage devices438such as a CD-ROM, DVD, memory stick, magnetic tape, magnetic disk, optical disk or semiconductor storage device. A software program, such as the selective sound allowance program110A,110B,110C can be stored on one or more of the respective portable computer-readable tangible storage devices438, read via the respective R/W drive or interface432, and loaded into the respective hard drive430.

Each set of internal components402a,b,calso includes network adapters or interfaces436such as a TCP/IP adapter cards, wireless Wi-Fi interface cards, or 3G or 4G wireless interface cards or other wired or wireless communication links. The software program108and the selective sound allowance program110A in the client computing device102, the selective sound allowance program110B in the server112, and the selective sound allowance program110C in the headset IoT device118can be downloaded to the client computing device102, the server112, and the headset IoT device118from an external computer via a network (for example, the Internet, a local area network or other, wide area network) and respective network adapters or interfaces436. From the network adapters or interfaces436, the software program108and the selective sound allowance program110A in the client computing device102, the selective sound allowance program110B in the server112, and the selective sound allowance program110C in the headset IoT device118are loaded into the respective hard drive430. The network may comprise copper wires, optical fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers.

Each of the sets of external components404a,b,ccan include a computer display monitor444, a keyboard442, and a computer mouse434. External components404a,b,ccan also include touch screens, virtual keyboards, touch pads, pointing devices, and other human interface devices. Each of the sets of internal components402a,b,calso includes device drivers440to interface to computer display monitor444, keyboard442, and computer mouse434. The device drivers440, R/W drive or interface432, and network adapter or interface436comprise hardware and software (stored in storage device430and/or ROM424).

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

Workloads layer90provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation91; software development and lifecycle management92; virtual classroom education delivery93; data analytics processing94; transaction processing95; and selective sound allowance96. Selective sound allowance96may relate to selectively allowing a sound to be heard within a noise cancellation headset.