A hearing device has a signal processor which has an adjustable parameter that has a given setting at a given time. The parameter is set depending on the situation by selecting a setting, depending on an environmental situation and by a learning machine. A current setting of the parameter can be rated by feedback from a user. In a first training procedure the learning machine is passively trained by negative feedback signals, by rating feedback from the user as dissatisfaction with the current setting and by assuming the user's satisfaction with the current setting as long as no feedback is given. In a second training procedure the learning machine is trained by changing the current setting independently of the feedback from the user and in spite of an assumed satisfaction with the current setting, so that the user is offered a different setting which can then be rated by feedback.

A method operates a hearing aid system having a hearing instrument. An electro-acoustic input transducer of the hearing instrument generates an input signal from an acoustic signal from the environment, and an output signal is generated from the input signal by a signal processor. An output acoustic signal is generated from the output signal by an electro-acoustic output transducer of the hearing instrument. For at least one sub-process of the signal processing an artificial neural network is used which is implemented in the hearing instrument. A topology of the artificial neural network is defined and/or weights between individual neurons of the artificial neural network are selected according to an operation to be performed in the sub-process and/or according to an ambient situation and/or according to a user input by a user of the hearing aid system.

Disclosed is a neuromorphic-processing systems including, in some embodiments, a special-purpose host processor operable as a stand-alone host processor; a neuromorphic co-processor including an artificial neural network; and a communications interface between the host processor and the co-processor configured to transmit information therebetween. The co-processor is configured to enhance special-purpose processing of the host processor with the artificial neural network. Also disclosed is a method of a neuromorphic-processing system having the special-purpose host processor and the neuromorphic co-processor including, in some embodiments, enhancing the special-purpose processing of the host processor with the artificial neural network of the co-processor. In some embodiments, the host processor is a hearing-aid processor.

A method, comprising receiving at least one sound at an electronic device. The at least one sound is enhanced for the at least one user based on a compound metric. The compound metric is calculated using at least two sound metrics selected from an engineering metric, a perceptual metric, and a physiological metric. The engineering metric comprises a difference between an output signal and a desired signal. At least one of the perceptual metric and the physiological metric is based at least in part on input sensed from the at least one user in response to the received at least one sound.

Embodiments relate to an audio system for various artificial reality applications. The audio system performs large scale filter optimization for audio rendering, preserving spatial and intra-population characteristics using neural networks. Further, the audio system performs adaptive hearing enhancement-aware binaural rendering. The audio includes an in-ear device with an inertial measurement unit (IMU) and a camera. The camera captures image data of a local area, and the image data is used to correct for IMU drift. In some embodiments, the audio system calculates a transducer to ear response for an individual ear using an equalization prediction or acoustic simulation framework. Individual ear pressure fields as a function of frequency are generated. Frequency-dependent directivity patterns of the transducers are characterized in the free field. In some embodiments, the audio system includes a headset and one or more removable audio apparatuses for enhancing acoustic features of the headset.

Systems and methods for modifying audio signals based on context may include at least one microphone configured to capture sounds from an environment of a user; and at least one processor. The processor may be programmed to receive an audio signal representative of sounds captured by the at least one microphone; and determine a context associated with the captured sounds based on the audio signal. Subject to the context being included in a set of stored contexts, the processor may be programmed to determine at least one first speaker whose speech is to be amplified; identify at least one first portion of the audio signal associated with the determined at least one first speaker; amplify the at least one first portion of the audio signal; and transmit to a hearing interface device the amplified at least one first portion of the audio signal.

A system includes a mobile device that receives an audio signal from a microphone of the mobile device. The mobile device processes the audio signal via a neural network to obtain a speech-enhanced audio signal. The system includes an ear-wearable device comprising a data interface operable to communicate with the external data interface of the mobile device. The ear-wearable device includes an audio processing path coupled to the data interface and is operable to receive the speech-enhanced audio signal and reproduce the speech-enhanced audio in an ear of a user.

A system and a corresponding method for assisting selective hearing are provided. The system includes a detector for detecting an audio source signal portion of one or more audio sources by using at least two received microphone signals of a hearing environment. In addition, the system includes a position determiner for allocating position information to each of the one or more audio sources. In addition, the system includes an audio type classifier for assigning an audio source signal type to the audio source signal portion of each of the one or more audio sources. In addition, the system includes a signal portion modifier for varying the audio source signal portion of at least one audio source of the one or more audio sources depending on the audio signal type of the audio source signal portion of the at least one audio source so as to obtain a modified audio signal portion of the at least one audio source. In addition, the system includes a signal generator.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for audio-visual speech separation. A method includes: receiving, by a user device, a first indication of one or more first speakers visible in a current view recorded by a camera of the user device, in response, generating a respective isolated speech signal for each of the one or more first speakers that isolates speech of the first speaker in the current view and sending the isolated speech signals for each of the one or more first speakers to a listening device operatively coupled to the user device, receiving, by the user device, a second indication of one or more second speakers visible in the current view recorded by the camera of the user device, and in response generating and sending a respective isolated speech signal for each of the one or more second speakers to the listening device.

Disclosed herein, among other things, are apparatus and methods for neural network-driven frequency translation for hearing assistance devices. Various embodiments include a method of signal processing an input signal in a hearing assistance device, the hearing assistance device including a receiver and a microphone. The method includes performing neural network processing to train a processor to identify acoustic features in a plurality of audio signals and predict target outputs for the plurality of audio signals, and using the trained processor to control frequency translation of the input signal.