Provided is a method for enabling speaker de-identification in public audio data by leveraging adversarial perturbation. The method may include receiving audio data associated with at least one voice sample. One or more of the voice sample(s) may be perturbed toward an edge of a decision boundary of at least one classifier model. One pitch of each voice sample may be perturbed to shift each voice sample across the decision boundary of the at least one classifier model to provide at least one de-identified voice sample. A media file with the at least one de-identified voice sample may be encoded. A system and computer program product are also disclosed.

According to an example, a method comprises receiving a first audio stream from an input device, detecting presence within the first audio stream of at least an acoustic pattern, executing at least one corrective action over a portion of data of the first audio stream including the acoustic pattern such that a second audio stream is obtained, and transmitting the second audio stream to an output device.

Methods and apparatuses for addressing open space noise are disclosed. In one example, a method for masking open space noise includes outputting from a speaker a speaker sound corresponding to a flow of water, and displaying a water element system, the water element system generating a sound of flowing water.

Methods and apparatuses for addressing open space noise are disclosed. In one example, a method for masking open space noise includes outputting from a speaker a speaker sound corresponding to a flow of water, and displaying a water element system, the water element system generating a sound of flowing water.

A computer implemented method for securely extracting secure data from a human capital management (HCM) system, includes receiving setup data from a production tenant of the HCM system, wherein the setup data includes one or more field types describing what type of secure data is stored on the production tenant, creating a scrambling module based on the setup data that is configured to scramble the secure data based on scrambling settings, wherein the scrambling module is configured to upload and install onto the HCM system and to communicate with the production tenant to receive the secure data to scramble the secure data, and uploading the scrambling module to the HCM system.

A network node in a distributed network employs a surface immunoglobulin program to monitor other nodes in the distributed network and generate an alert upon detecting a suspicious activity; and pushes a free-antibody program to a requesting node petitioning to access the distributed network. The free-antibody program can include a software agent that monitors the requesting node. The free-antibody program reports detected malware and/or suspicious activity to the surface immunoglobulin program, which can enact countermeasures against the requesting node. The network node's role is based on a hierarchy of trustworthiness levels, wherein it performs at least one of monitoring other nodes, sending alerts when anomalous behavior is detected, transmitting the free-antibody software program to the requesting node, updating defensive programs, participating in consensus-based threat analysis with other nodes, identifying threats, tagging suspicious nodes, and performing countermeasures against identified threats.

Methods and apparatuses for addressing open space noise are disclosed. In one example, a method for masking open space noise includes outputting from a speaker a speaker sound corresponding to a flow of water, and displaying a water element system, the water element system generating a sound of flowing water.

Methods and apparatuses for addressing open space noise are disclosed. In one example, a method for masking open space noise includes outputting from a speaker a speaker sound corresponding to a flow of water, and displaying a water element system, the water element system generating a sound of flowing water.

An acoustic sensor is positioned in an environment and configured to generate a data stream responsive to acoustic energy in the environment. A controller is configured to receive the data stream. The controller is further configured to analyze the data stream to determine ambient acoustic signals. The controller is further configured to generate an ambient acoustic template based on the determined ambient acoustic signals. The controller is further configured to apply the ambient acoustic template to the data stream so that the ambient acoustic signals are suppressed in the data stream. The controller is further configured to analyze the data stream after the ambient acoustic signals are suppressed in order to determine if the acoustic energy in the environment includes acoustic energy of human snoring. The controller is further configured to issue a control signal to a second controller in order to engage a home automation device.

A network node in a distributed network employs a surface immunoglobulin program to monitor other nodes in the distributed network and generate an alert upon detecting a suspicious activity; and pushes a free-antibody program to a requesting node petitioning to access the distributed network. The free-antibody program can comprise a software agent that monitors the requesting node. The free-antibody program reports detected malware and/or suspicious activity to the surface immunoglobulin program, which can enact countermeasures against the requesting node. The network node's role is based on a hierarchy of trustworthiness levels, its role comprising at least one of monitoring other nodes, sending alerts when anomalous behavior is detected, transmitting the free-antibody software program to the requesting node, updating defensive programs, participating in consensus-based threat analysis with other nodes, identifying threats, tagging suspicious nodes, and performing countermeasures against identified threats.