Abstract: A hearing device, e.g. a hearing aid, is configured to be arranged at least partly on a user's head or at least partly implanted in a user's head. The hearing device comprises a) at least one input transducer for picking up an input sound signal from the environment and providing at least one electric input signal representing said input sound signal; b) a signal processor connected to the at least one input transducer, the signal processor being configured to analyze the electric input signal and to provide a transmit control signal in dependence thereof; c) a memory buffer, e.g. a cyclic buffer, for storing a current time segment of a certain duration of said at least one electric input signal, or a processed version thereof; and a transmitter for transmitting at least a part of said time segment, or a processed version thereof, to an external device in dependence of said transmit control signal.

Abstract: A hearing device, e.g.

Abstract: Disclosed herein are embodiments of a hearing device including a multitude of input transducers providing a corresponding multitude of electric input signals, a noise reduction system comprising first and second beamformer filters configured to provide first and second beamformed signals, respectively, first and second noise reduction controllers configured to receive said multitude of electric input signals, and said first and second beamformed signals, respectively, and to provide respective first and second noise reduced signals, according to first and second adaptive selection schemes, respectively.

Abstract: A hearing device, e.g. a hearing aid or a headset, configured to be worn by a comprises an input unit for providing at least one electric input signal in a time-frequency representation; and a signal processor comprising a target signal estimator for providing an estimate of the target signal; a noise estimator for providing an estimate of the noise; and a gain estimator for providing respective gain values in dependence of said target signal estimate and said noise estimate. The gain estimator comprises a trained neural network, wherein the outputs of the neural network comprise real or complex valued gains, or separate real valued gains and real valued phases.

Abstract: A hearing device, e.g. a hearing aid, is configured to be arranged at least partly on a user’s head or at least partly implanted in a user’s head. The hearing device comprises a) at least one input transducer for picking up an input sound signal from the environment and providing at least one electric input signal representing said input sound signal; b) a signal processor connected to the at least one input transducer, the signal processor being configured to analyze the electric input signal and to provide a transmit control signal in dependence thereof; c) a memory buffer, e.g. a cyclic buffer, for storing a current time segment of a certain duration of said at least one electric input signal, or a processed version thereof; and a transmitter for transmitting at least a part of said time segment, or a processed version thereof, to an external device in dependence of said transmit control signal.

Abstract: A hearing device comprises an input transducer comprising a microphone for providing an electric input signal representative of sound in the environment of the hearing device, a pre-processor for processing electric input signal and providing a multitude of feature vectors, each being representative of a time segment thereof, a neural network processor adapted to implement a neural network for implementing a detector configured to provide an output indicative of a characteristic property of the at least one electric input signal, the neural network being configured to receive said multitude of feature vectors as input vectors and to provide corresponding output vectors representative of said output of said detector in dependence of said input vectors.

Abstract: A hearing device, e.g. a hearing aid, is configured to be arranged at least partly on a user's head or at least partly implanted in a user's head.

Abstract: A hearing device comprises an input transducer comprising a microphone for providing an electric input signal representative of sound in the environment of the hearing device, a pre-processor for processing electric input signal and providing a multitude of feature vectors, each being representative of a time segment thereof, a neural network processor adapted to implement a neural network for implementing a detector configured to provide an output indicative of a characteristic property of the at least one electric input signal, the neural network being configured to receive said multitude of feature vectors as input vectors and to provide corresponding output vectors representative of said output of said detector in dependence of said input vectors.

Abstract: A hearing device, e.g.

Abstract: A hearing device, e.g. a hearing aid or a headset, configured to be worn by a comprises an input unit for providing at least one electric input signal in a time-frequency representation; and a signal processor comprising a target signal estimator for providing an estimate of the target signal; a noise estimator for providing an estimate of the noise; and a gain estimator for providing respective gain values in dependence of said target signal estimate and said noise estimate. The gain estimator comprises a trained neural network, wherein the outputs of the neural network comprise real or complex valued gains, or separate real valued gains and real valued phases.

Abstract: A hearing device, e.g. a hearing aid, is configured to be worn by a user at or in an ear or to be fully or partially implanted in the head at an ear of the user.

Abstract: A hearing device, e.g. a hearing aid, is configured to be worn by a user at or in an ear or to be fully or partially implanted in the head at an ear of the user. The hearing device comprises a) an input unit for providing at least one electric input signal in a time frequency representation k, m, where k and m are frequency and time indices, respectively, and k represents a frequency channel, the at least one electric input signal being representative of sound and comprising target signal components and noise components; and b) a signal processor comprising b1) an SNR estimator for providing a target signal-to-noise ratio estimate for said at least one electric input signal in said time frequency representation; and b2) an SNR-to-gain converter for converting said target signal-to-noise ratio estimate to respective gain values in said time frequency representation. The signal processor comprises a neural network, wherein the weights of the neural network have been trained with a plurality of training signals.

Abstract: A hearing device comprises an input transducer comprising a microphone for providing an electric input signal representative of sound in the environment of the hearing device, a pre-processor for processing electric input signal and providing a multitude of feature vectors, each being representative of a time segment thereof, a neural network processor adapted to implement a neural network for implementing a detector configured to provide an output indicative of a characteristic property of the at least one electric input signal, the neural network being configured to receive said multitude of feature vectors as input vectors and to provide corresponding output vectors representative of said output of said detector in dependence of said input vectors.

Abstract: A method of training an algorithm for optimizing intelligibility of speech components of a sound signal in hearing aids, headsets, etc.

Abstract: A hearing device, e.g. a hearing aid, is configured to be arranged at least partly on a user's head or at least partly implanted in a user's head.

Abstract: A hearing device, e.g. a hearing aid, is configured to be worn by a user at or in an ear or to be fully or partially implanted in the head at an ear of the user. The hearing device comprises a) an input unit for providing at least one electric input signal in a time frequency representation k, m, where k and m are frequency and time indices, respectively, and k represents a frequency channel, the at least one electric input signal being representative of sound and comprising target signal components and noise components; and b) a signal processor comprising b1) an SNR estimator for providing a target signal-to-noise ratio estimate for said at least one electric input signal in said time frequency representation; and b2) an SNR-to-gain converter for converting said target signal-to-noise ratio estimate to respective gain values in said time frequency representation. The signal processor comprises a neural network, wherein the weights of the neural network have been trained with a plurality of training signals.

Abstract: A method of training an algorithm for optimizing intelligibility of speech components of a sound signal in hearing aids, headsets, etc.

Abstract: The application relates to a binaural hearing system comprising left and right hearing devices, e.g.

Abstract: A monaural intrusive speech intelligibility predictor unit comprises: first and second input units for providing time-frequency representations s(k,m) and x(k,m) of noise-free and noisy and/or processed versions of a target signal, respectively, k being a frequency bin index, k=1, 2, . . . , K, and m being a time index; first and second envelope extraction units for providing time-frequency sub-band representations of the signals sj(m) and xj(m), j being a frequency sub-band index, j=1, 2, . . .

Abstract: Signal processing methods for predicting the intelligibility of speech, e.g., in the form of an index that correlate highly with the fraction of words that an average listener (amongst a group of listeners with similar hearing profiles) would be able to understand from some speech material are proposed. Specifically, solutions to the problem of predicting the intelligibility of speech signals, which are distorted, e.g., by noise or reverberation, and which might have been passed through some signal processing device, e.g., a hearing aid are described. In summary, the disclosure present solutions to the following problems: 1. Monaural, non-intrusive intelligibility prediction of noisy/processed speech signals 2. Binaural, non-intrusive intelligibility prediction of noisy/processed speech signals 3. Monaural and binaural intelligibility enhancement of noisy speech signals.