An electronic device includes a speaker, a sensor, a communication circuit, a processor, and a memory to store instructions. The instructions, when executed by the processor, cause a wireless audio device to, while outputting a signal for reducing an external sound through the speaker, identify, using the communication circuit, an external electronic device, identify, using the sensor, a conversation responsive to a location of the external electronic device satisfying a specified condition, responsive to identifying the conversation, stop an output of the signal for reducing the external sound for a first period of time, and responsive to identifying a specified keyword included in the conversation, prolong stopping the output of the signal for reducing the external sound for a second period of time.

An urban air mobility (UAM) noise reduction system and method are provided, where the UAM noise reduction system includes a UAM configured to detect and to provide rotation per minute (RPM) information of a propeller and location coordinate information, and a noise canceling device configured to predict a noise canceling sound wave amplitude on the basis of the RPM information of the propeller and the location coordinate information received through the UAM and to output a noise canceling sound wave corresponding to the predicted noise canceling sound wave amplitude to the UAM.

An audio device capable of inhibiting malfunction of an information terminal is provided. The audio device includes a sound sensor portion, a sound separation portion, a sound determination portion, and a processing portion. The sound sensor portion has a function of sensing sound. The sound separation portion has a function of separating the sound sensed by the sound sensor portion into a voice and sound other than a voice. The sound determination portion has a function of storing the feature quantity of the sound. The sound determination portion has a function of determining, with a machine learning model such as a neural network model, whether the feature quantity of the voice separated by the sound separation portion is the stored feature quantity. The processing portion has a function of analyzing an instruction contained in the voice and generating an instruction signal representing the content of the instruction in the case where the feature quantity of the voice is the stored feature quantity. The processing portion has a function of performing, on the sound other than a voice separated by the sound separation portion, processing for canceling the sound other than a voice. Specifically, the processing portion has a function of performing, on the sound other than a voice, processing for inverting the phase thereof.

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.

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.

A vehicle noise cancellation system and method, operable for: receiving local contextual information affecting a soundwave present in a vehicle; receiving remote contextual information affecting the soundwave present in the vehicle; receiving vehicle occupant information; processing the local contextual information, the remote contextual information, and the vehicle occupant information to generate an augmented anti-soundwave; and delivering the augmented anti-soundwave to an occupant of the vehicle to mitigate the soundwave present in the vehicle. Delivering the augmented anti-soundwave to the occupant of the vehicle to mitigate the soundwave present in the vehicle may include delivering the first augmented anti-soundwave to an occupant present in the first zone of the vehicle and delivering the second augmented anti-soundwave to an occupant present in the second zone of the vehicle using a plurality of speakers.

A headset has at least two functions of an active noise control (ANC) function, an ambient sound hear through (HT) function, or an augment hearing (AH) function. The headset includes a first microphone and a second microphone. The first microphone is configured to collect a first signal. The first signal indicates a sound in a current external environment. The second microphone is configured to collect a second signal. The second signal indicates an ambient sound in an ear canal of a user wearing the headset. The headset can be a left earphone or a right earphone. Processing modes or processing strengths of the left earphone and the right earphone may be the same or different. The headset obtains a target mode based on a scene type of the current external environment; and obtains a second audio signal based on the target mode, the first signal, and the second signal.

An urban air mobility (UAM) noise reduction system and method are provided, where the UAM noise reduction system includes a UAM configured to detect and to provide rotation per minute (RPM) information of a propeller and location coordinate information, and a noise canceling device configured to predict a noise canceling sound wave amplitude on the basis of the RPM information of the propeller and the location coordinate information received through the UAM and to output a noise canceling sound wave corresponding to the predicted noise canceling sound wave amplitude to the UAM.

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.