An active noise cancellation system (1) for cancelling environment noise perceived by a driver or passenger seated in a seat (3) mounted in a cabin of a vehicle, in combination with said seat, the seat comprising a seat cushion (19), a seat back (21) coupled to the seat cushion at a bottom end and extending upwards to a seat shoulder (23), and a headrest (22) coupled to the seat back, extending upwardly from the seat shoulder, the active noise cancellation system comprising an active noise cancellation circuit (ANC) (30), a plurality of microphones (10) mounted in the headrest and connected electrically to the ANC, and a plurality of speakers (16) mounted in the seat and connected electrically to the ANC circuit. The plurality of microphones comprises at least one first microphone mounted on a right side of the headrest and at least one second microphone mounted on a left side of the headrest, and the plurality of speakers comprises at least one first speaker mounted in the seat shoulder on a left side and at least one second speaker mounted in the seat shoulder on a right side, the right speaker configured to generate a noise cancellation sound from a noise signal picked up by the right microphone processed by the ANC circuit and the left speaker configured to generate a noise cancellation sound from a noise signal picked up by the left microphone processed by the ANC circuit.

A method of echo path delay destination and echo cancellation is described in this disclosure. The method includes: obtaining a reference signal, a microphone signal, and a trained multi-task deep neural network, wherein the multi-task deep neural network comprises a first neural network and a second neural network; generating, using the first neural network of the multi-task deep neural network, an estimated echo path delay based on the reference signal and the microphone signal; updating the reference signal based on the estimated echo path delay; and generating, using the second neural network of the multi-task deep neural network, an enhanced microphone signal based on the microphone signal and the updated reference signal.

The present technology relates to a particular-sound detector and method, and a program that make it possible to improve the performance of detecting particular sounds.

The particular-sound detector includes a particular-sound detecting section that detects a particular sound on a basis of a plurality of audio signals obtained by collecting sounds by a plurality of microphones provided to a wearable device. In addition, the plurality of the microphones includes two microphones that are equidistant at least from a sound source of the particular sound, and one microphone arranged at a predetermined position. The present technology can be applied to headphones.

The noise suppression method of individual active noise reduction system comprises the steps that: (1) initial noise digital signals are received and converted to serve as input signals of a BP neural network; (2) the input signals are processed to generate secondary digital signals; (3) the secondary digital signals are output to a loudspeaker and secondary noise is generated; (4) remained noise digital signals obtained by overlapping the initial noise and the secondary noise are received; whether remained noise digital signals is continuously constant for the set times is judged; if yes, the secondary digital signals are kept outputting; (5) if not, BP neural network parameters are optimized and adjusted with the amplitude of the remained noise digital signals being minimum as the optimality principle; remained noise digital signals of previous step are served as new input signals and the step (2) is executed again.

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.

A communications enhancement computing system for connecting multiple users while balancing audio noise comprises a memory, a network interface device and a processor configured for applying signal processing techniques to a dataset of environmental sounds to extract sound characteristics of said sounds, executing a first deep neural network algorithm to train a first machine learning classification model for classifying sounds by label, executing a second deep neural network algorithm to train a second machine learning classification model for classifying sounds by environment, receiving, via the communications network, input sounds from a user and executing the first and second classification models to classify the input sounds by label and by environment, defining a sound softening technique configured to apply to audio from the user, wherein said sound softening technique is based on the environment and label, and executing the sound softening techniques to a continuous audio feed from the user.

Embodiments of the present disclosure set forth a method of reducing noise in an audio signal. The method includes determining, based on sensor data acquired from a first set of sensors, a first position of a user in an environment. The method also includes acquiring, via the first set of sensors, one or more audio signals associated with sound in the environment and identifying one or more noise elements in the one or more audio signals. The method also includes generating a first directional audio signal based on the one or more noise elements. When the first directional audio signal is outputted by a first speaker, the first speaker produces a first acoustic field that attenuates the one or more noise elements at the first position.

Adaptive filters output a cancellation sound from a speaker, a selector selects outputs of a plurality of auxiliary filters each corresponding to different positions, a subtractor subtracts the selected output from the output of the microphone and outputs the subtracted output to the adaptive filter as an error signal, and a position detection device detects a position of a head of a user. A transfer function estimated so that the error signal becomes 0 when noise is canceled at the corresponding position is preset in the auxiliary filter. When the auxiliary filter corresponding to the position close to the head of the user changes, the switching control unit stepwise increases the frequency with which the output of the auxiliary filter is selected by the selector to 100%.

An electronic device includes a communication module, and a processor. The processor is configured to identify context information. The processor is also configured to select a specific task corresponding to the context information from among predetermined inference tasks relating to processing of an audio signal The processor is further configured to select an external electronic device, which is to process the specific task, from among external electronic devices that are establishing a communication connection to the electronic device. Additionally, the processor is configured to assign processing of the specific task to the external electronic device.

The present disclosure provides an acoustic device including a microphone array, a processor, and at least one speaker. The microphone array may be configured to acquire an environmental noise. The processor may be configured to estimate a sound field at a target spatial position using the microphone array. The target spatial position may be closer to an ear canal of a user than each microphone in the microphone array. The processor may be configured to generate a noise reduction signal based on the environmental noise and the sound field estimation of the target spatial position. The at least one speaker may be configured to output a target signal based on the noise reduction signal. The target signal may be used to reduce the environmental noise. The microphone array may be arranged in a target area to minimize an interference signal from the at least one speaker to the microphone array.