Abstract: A hearing system performs nonlinear processing of signals received from a plurality of microphones using a neural network to enhance a target signal in a noisy environment. In various embodiments, the neural network can be trained to improve a signal-to-noise ratio without causing substantial distortion of the target signal. An example of the target sound includes speech, and the neural network is used to improve speech intelligibility.

Abstract: A hearing system performs nonlinear processing of signals received from a plurality of microphones using a neural network to enhance a target signal in a noisy environment. In various embodiments, the neural network can be trained to improve a signal-to-noise ratio without causing substantial distortion of the target signal. An example of the target sound includes speech, and the neural network is used to improve speech intelligibility.

Abstract: A hearing system performs nonlinear processing of signals received from a plurality of microphones using a neural network to enhance a target signal in a noisy environment. In various embodiments, the neural network can be trained to improve a signal-to-noise ratio without causing substantial distortion of the target signal. An example of the target sound includes speech, and the neural network is used to improve speech intelligibility.

Abstract: A hearing system performs nonlinear processing of signals received from a plurality of microphones using a neural network to enhance a target signal in a noisy environment. In various embodiments, the neural network can be trained to improve a signal-to-noise ratio without causing substantial distortion of the target signal. An example of the target sound includes speech, and the neural network is used to improve speech intelligibility.

Abstract: A real-time audio signal processing system includes an audio signal processor configured to process audio signals using a modified generalized eigenvalue (GEV) beamforming technique to generate an enhanced target audio output signal. The digital signal processor includes a sub-band decomposition circuitry configured to decompose the audio signal into sub-band frames in the frequency domain and a target activity detector configured to detect whether a target audio is present in the sub-band frames. Based on information related to the sub-band frames and the determination of whether the target audio is present in the sub-band frames, the digital signal processor is configured to use the modified GEV technique to estimate the relative transfer function (RTF) of the target audio source, and generate a filter based on the estimated RTF. The filter may then be applied to the audio signals to generate the enhanced audio output signal.

Abstract: Systems and methods for enhancing a headset user's own voice include an outside microphone, an inside microphone, audio input components operable to receive a plurality of time-domain microphone signals, including an outside microphone signal from the outside microphone and an inside microphone signal from the inside microphone, a subband decomposition module operable to transform the time-domain microphone signals to frequency domain subband signals, a voice activity detector operable to detect speech presence and absence in the subband signals, a speech extraction module operable to predict a clean speech signal in each of the inside microphone signal and the outside microphone signal, and cancel audio sources other than a headset user's own voice by combining the predicted clean speech signal from the inside microphone signal and the predicted clean speech signal from the outside microphone signal, and a postfiltering module operable to reduce residual noise.

Abstract: A hearing system performs nonlinear processing of signals received from a plurality of microphones using a neural network to enhance a target signal in a noisy environment. In various embodiments, the neural network can be trained to improve a signal-to-noise ratio without causing substantial distortion of the target signal. An example of the target sound includes speech, and the neural network is used to improve speech intelligibility.

Abstract: A hearing system performs nonlinear processing of signals received from a plurality of microphones using a neural network to enhance a target signal in a noisy environment. In various embodiments, the neural network can be trained to improve a signal-to-noise ratio without causing substantial distortion of the target signal. An example of the target sound includes speech, and the neural network is used to improve speech intelligibility.

Abstract: A real-time audio signal processing system includes an audio signal processor configured to process audio signals using a modified generalized eigenvalue (GEV) beamforming technique to generate an enhanced target audio output signal. The digital signal processor includes a sub-band decomposition circuitry configured to decompose the audio signal into sub-band frames in the frequency domain and a target activity detector configured to detect whether a target audio is present in the sub-band frames. Based on information related to the sub-band frames and the determination of whether the target audio is present in the sub-band frames, the digital signal processor is configured to use the modified GEV technique to estimate the relative transfer function (RTF) of the target audio source, and generate a filter based on the estimated RTF. The filter may then be applied to the audio signals to generate the enhanced audio output signal.

Abstract: A hearing system includes a pair of first and second hearing devices wirelessly coupled to a remote device that includes a microphone. One or more gains can each be calculated as a function of a first microphone signal received from the first hearing device, a second microphone signal received from the second hearing device, and a remote microphone signal received from the remote device. The function can be designed to improve speech intelligibility in a noisy environment. The one or more gains are applied to the first and second microphone signals to produce output sounds by the first and second hearing devices.

Abstract: Systems and methods for enhancing a headset user's own voice include an outside microphone, an inside microphone, audio input components operable to receive a plurality of time-domain microphone signals, including an outside microphone signal from the outside microphone and an inside microphone signal from the inside microphone, a subband decomposition module operable to transform the time-domain microphone signals to frequency domain subband signals, a voice activity detector operable to detect speech presence and absence in the subband signals, a speech extraction module operable to predict a clean speech signal in each of the inside microphone signal and the outside microphone signal, and cancel audio sources other than a headset user's own voice by combining the predicted clean speech signal from the inside microphone signal and the predicted clean speech signal from the outside microphone signal, and a postfiltering module operable to reduce residual noise.

Abstract: The use of a dynamic Relative Transfer Function (RTF) between two or more microphones may be used to improve multi-microphone speech processing applications. The dynamic RTF may improve speech intelligibility and speech quality in the presence of environmental changes, such as variations in head or body movements, variations in hearing device characteristics or wearing positions, or variations in room or environment acoustics. The use of an efficient and fast dynamic RTF estimation algorithm using short burst of noisy, reverberant mic recordings, which will be robust to head movements may provide more accurate RTFs which may lead to a significant performance increase.

Abstract: A hearing system includes a pair of first and second hearing devices wirelessly coupled to a remote device that includes a microphone. One or more gains can each be calculated as a function of a first microphone signal received from the first hearing device, a second microphone signal received from the second hearing device, and a remote microphone signal received from the remote device. The function can be designed to improve speech intelligibility in a noisy environment. The one or more gains are applied to the first and second microphone signals to produce output sounds by the first and second hearing devices.

Abstract: A hearing system performs nonlinear processing of signals received from a plurality of microphones using a neural network to enhance a target signal in a noisy environment. In various embodiments, the neural network can be trained to improve a signal-to-noise ratio without causing substantial distortion of the target signal. An example of the target sound includes speech, and the neural network is used to improve speech intelligibility.

Abstract: Embodiments of packet loss concealment in a hearing assistance device are generally described herein. A method for packet loss concealment can include receiving, at a first hearing assistance device, a first encoded packet stream from a second hearing assistance device and a signal frame. The method can include encoding, at the first hearing assistance device, the signal frame and determining, at the first hearing assistance device, that a second encoded packet stream was not received from the second hearing assistance device within a predetermined time. In response to determining that the second encoded packet stream was not received, the method can include decoding, at the first hearing assistance device, the encoded signal frame, and outputting the signal frame and the decoded signal frame.

Abstract: Embodiments of packet loss concealment for phone-to-hearing-aid streaming are generally described herein. A method for packet loss concealment can include receiving a first frame at a hearing assistance device, determining, at the hearing assistance device, that a second frame was not received within a predetermined time, and determining a first set of sequential samples that match the first frame. The method can include cross-fading the first frame and the first set of sequential samples to create a first cross-faded frame and extrapolating a third frame to replace the second frame using the first set of sequential samples and an autoregressive model.

Abstract: The use of a dynamic Relative Transfer Function (RTF) between two or more microphones may be used to improve multi-microphone speech processing applications. The dynamic RTF may improve speech intelligibility and speech quality in the presence of environmental changes, such as variations in head or body movements, variations in hearing device characteristics or wearing positions, or variations in room or environment acoustics. The use of an efficient and fast dynamic RTF estimation algorithm using short burst of noisy, reverberant mic recordings, which will be robust to head movements may provide more accurate RTFs which may lead to a significant performance increase.

Abstract: Embodiments of packet loss concealment in a hearing assistance device are generally described herein. A method for packet loss concealment can include receiving, at a first hearing assistance device, a first encoded packet stream from a second hearing assistance device and a signal frame. The method can include encoding, at the first hearing assistance device, the signal frame and determining, at the first hearing assistance device, that a second encoded packet stream was not received from the second hearing assistance device within a predetermined time. In response to determining that the second encoded packet stream was not received, the method can include decoding, at the first hearing assistance device, the encoded signal frame, and outputting the signal frame and the decoded signal frame.

Abstract: Embodiments of packet loss concealment for phone-to-hearing-aid streaming are generally described herein. A method for packet loss concealment can include receiving a first frame at a hearing assistance device, determining, at the hearing assistance device, that a second frame was not received within a predetermined time, and determining a first set of sequential samples that match the first frame. The method can include cross-fading the first frame and the first set of sequential samples to create a first cross-faded frame and extrapolating a third frame to replace the second frame using the first set of sequential samples and an autoregressive model.