An information handling system presents audio information as audible sounds that include noise cancellation generated in response to environmental noise patterns detected by a microphone. For example, a machine learning model generates noise cancellation for plural environmental noise patterns, such as a baby crying, a dog barking, and a door bell ringing. The noise cancellation engine selectively applies and disables one or more types noise cancellation with the model based upon context at the information handling system, such as an application running on the system, a time of day or other factors.

An output apparatus according to the present application includes a prediction unit and an output unit. The prediction unit predicts whether or not waveform information having a predetermined context is generated on the basis of detection information detected by a predetermined detection device. The output unit outputs waveform information having an opposite phase to the waveform information having the predetermined context in a case where it has been predicted that the waveform information having the predetermined context is generated.

An output apparatus according to the present application includes a prediction unit and an output unit. The prediction unit predicts whether or not waveform information having a predetermined context is generated on the basis of detection information detected by a predetermined detection device. The output unit outputs waveform information having an opposite phase to the waveform information having the predetermined context in a case where it has been predicted that the waveform information having the predetermined context is generated.

Various implementations include systems for processing audio signals to remove artifacts introduced by a machine learning system in challenging environments. In particular implementations, a method includes generating a processed audio signal for a hearing assistance device in which the processed audio signal is intended to perceptually dominate a user auditory experience, including: processing an unprocessed audio signal received by the hearing assistance device, wherein the processing includes utilizing a machine learning (ML) system to generate an ML enhanced audio signal; determining a mixing coefficient from an environmental noise assessment; mixing the ML enhanced audio signal with the unprocessed audio signal using the mixing coefficient to generate the processed audio signal; and outputting the processed audio signal.

Systems and methods for reducing vibrations perceived by a human due to an artificial heart valve include a vest that is wearable around a torso of the human, a plurality of sensors mounted to the vest, a plurality of vibration-generating actuators mounted to the vest, and a controller. The plurality of sensors detects vibrations in the human generated by the artificial heart valve. The controller is operable to receive signals representing the detected vibrations from the plurality of sensors, and is operable to produce anti-vibration signals that substantially attenuate the detected vibrations. A first sensor of the plurality of sensors is located near a first vibration-generating actuator of the plurality of vibration-generating actuators to form a sensor/actuator set. In the sensor/actuator set, the anti-vibration signals generated by the controller for the first vibration-generating actuator correspond to the vibrations detected by the first sensor.

Systems and methods for reducing vibrations perceived by a human due to an artificial heart valve include a vest that is wearable around a torso of the human, a plurality of sensors mounted to the vest, a plurality of vibration-generating actuators mounted to the vest, and a controller. The plurality of sensors detects vibrations in the human generated by the artificial heart valve. The controller is operable to receive signals representing the detected vibrations from the plurality of sensors, and is operable to produce anti-vibration signals that substantially attenuate the detected vibrations. A first sensor of the plurality of sensors is located near a first vibration-generating actuator of the plurality of vibration-generating actuators to form a sensor/actuator set. In the sensor/actuator set, the anti-vibration signals generated by the controller for the first vibration-generating actuator correspond to the vibrations detected by the first sensor.

The improved exercise of artificial intelligence. Raw output data is obtained by applying an input data set to an artificial intelligence (AI). Such raw output data is sometimes difficult to interpret. The principles defined herein provide a systematic way to refine the output for a wide variety of AI models. An AI model collection characterization structure is utilized for purpose of refining AI model output so as to be more useful. The characterization structure represents, for each of multiple and perhaps numerous AI models, a refinement of output data that resulted from application of an AI model to input data. Upon obtaining output data from the AI model, the appropriate refinement may then be applied. The refined data may then be semantically indexed to provide a semantic index. The characterization structure may also provide tailored information to allow for intuitive querying against the semantic index.

For example, a controller of an Active Acoustic Control (AAC) system may be configured to process input information including AAC configuration information, and a plurality of noise inputs representing acoustic noise at a plurality of noise sensing locations. For example, the controller may be configured to process the input information to determine a sound control pattern to control sound within a sound control zone based on the plurality of noise inputs. For example, the controller may include a Neural-Network (NN) trained to generate an NN output based on an NN input, wherein the NN input is based on the AAC configuration information. For example, the controller may be configured to generate the sound control pattern based on the NN output, and to output the sound control pattern to one or more acoustic transducers.

An open-ear device performs active noise cancellation (ANC) for a user. A sensor located outside an ear of a user which does not occlude an ear canal of the ear and measures vibrational data indicative of a sound pressure level at a location outside the ear, or a level of pinna vibration of the user. A prediction pipeline generates a prediction of sound pressure within the ear canal using an individualized model, taking into account the measured vibrational data and the unique geometric shape of the user's head and pinna. This sound pressure prediction is used to generate audio instructions for rendering playback at an noise cancellation source, such as a bone conduction transducer and/or cartilage transducer, to perform ANC for the user by cancelling at least portion of the sound received at the ear canal.

A method for actively monitoring sound emissions of turbomachinery, in particular turbomachinery which has an electric motor, preferably a ventilator or a turbomachine. A sound signal, which is produced by superimposing the sound emission of the turbomachinery with at least one counter sound signal, is captured by at least one receiver at at least one receiver position and is transmitted to a control unit, wherein the control unit has an artificial intelligence, and a control signal is generated by the artificial intelligence for at least one actuator while taking into consideration the sound signal such that the actuator generates a counter sound signal that interacts with the sound emission of the turbomachinery such that a sound load at least in the region of the receiver position or the receiver positions is reduced.