Abstract: Disclosed are devices, systems, apparatus, methods, products, and other implementations, including a method comprising obtaining, by a device, a combined sound signal for signals combined from multiple sound sources in an area in which a person is located, and applying, by the device, speech-separation processing (e.g., deep attractor network (DAN) processing, online DAN processing, LSTM-TasNet processing, Conv-TasNet processing), to the combined sound signal from the multiple sound sources to derive a plurality of separated signals that each contains signals corresponding to different groups of the multiple sound sources. The method further includes obtaining, by the device, neural signals for the person, the neural signals being indicative of one or more of the multiple sound sources the person is attentive to, and selecting one of the plurality of separated signals based on the obtained neural signals. The selected signal may then be processed (amplified, attenuated).

Abstract: Disclosed are methods, systems, device, and other implementations, including a method (performed by, for example, a hearing aid device) that includes obtaining a combined sound signal for signals combined from multiple sound sources in an area in which a person is located, and obtaining neural signals for the person, with the neural signals being indicative of one or more target sound sources, from the multiple sound sources, the person is attentive to. The method further includes determining a separation filter based, at least in part, on the neural signals obtained for the person, and applying the separation filter to a representation of the combined sound signal to derive a resultant separated signal representation associated with sound from the one or more target sound sources the person is attentive to.

Abstract: Disclosed are devices, systems, apparatus, methods, products, and other implementations, including a method comprising obtaining, by a device, a combined sound signal for signals combined from multiple sound sources in an area in which a person is located, and applying, by the device, speech-separation processing (e.g., deep attractor network (DAN) processing, online DAN processing, LSTM-TasNet processing, Conv-TasNet processing), to the combined sound signal from the multiple sound sources to derive a plurality of separated signals that each contains signals corresponding to different groups of the multiple sound sources. The method further includes obtaining, by the device, neural signals for the person, the neural signals being indicative of one or more of the multiple sound sources the person is attentive to, and selecting one of the plurality of separated signals based on the obtained neural signals. The selected signal may then be processed (amplified, attenuated).

Abstract: Disclosed are methods, systems, device, and other implementations, including a method (performed by, for example, a hearing aid device) that includes obtaining a combined sound signal for signals combined from multiple sound sources in an area in which a person is located, and obtaining neural signals for the person, with the neural signals being indicative of one or more target sound sources, from the multiple sound sources, the person is attentive to. The method further includes determining a separation filter based, at least in part, on the neural signals obtained for the person, and applying the separation filter to a representation of the combined sound signal to derive a resultant separated signal representation associated with sound from the one or more target sound sources the person is attentive to.

Abstract: In one aspect of the present disclosure, method includes: receiving neural data responsive to a listener's auditory attention; receiving an acoustic signal responsive to a plurality of acoustic sources; for each of the plurality of acoustic sources: generating, from the received acoustic signal, audio data comprising one or more features of the acoustic source, forming combined data representative of the neural data and the audio data, and providing the combined data to a classification network configured to calculate a similarity score between the neural data and the acoustic source using one or more similarity metrics; and using the similarity scores calculated for each of the acoustic sources to identify, from the plurality of acoustic sources, an acoustic source associated with the listener's auditory attention.

Abstract: Disclosed are devices, systems, apparatus, methods, products, and other implementations, including a method comprising obtaining, by a device, a combined sound signal for signals combined from multiple sound sources in an area in which a person is located, and applying, by the device, speech-separation processing (e.g., deep attractor network (DAN) processing, online DAN processing, LSTM-TasNet processing, Conv-TasNet processing), to the combined sound signal from the multiple sound sources to derive a plurality of separated signals that each contains signals corresponding to different groups of the multiple sound sources. The method further includes obtaining, by the device, neural signals for the person, the neural signals being indicative of one or more of the multiple sound sources the person is attentive to, and selecting one of the plurality of separated signals based on the obtained neural signals. The selected signal may then be processed (amplified, attenuated).

Abstract: Disclosed are devices, systems, apparatus, methods, products, and other implementations, including a method comprising obtaining, by a device, a combined sound signal for signals combined from multiple sound sources in an area in which a person is located, and applying, by the device, speech-separation processing (e.g., deep attractor network (DAN) processing, online DAN processing, LSTM-TasNet processing, Conv-TasNet processing), to the combined sound signal from the multiple sound sources to derive a plurality of separated signals that each contains signals corresponding to different groups of the multiple sound sources. The method further includes obtaining, by the device, neural signals for the person, the neural signals being indicative of one or more of the multiple sound sources the person is attentive to, and selecting one of the plurality of separated signals based on the obtained neural signals. The selected signal may then be processed (amplified, attenuated).

Abstract: In one aspect of the present disclosure, method includes: receiving neural data responsive to a listener's auditory attention; receiving an acoustic signal responsive to a plurality of acoustic sources; for each of the plurality of acoustic sources: generating, from the received acoustic signal, audio data comprising one or more features of the acoustic source, forming combined data representative of the neural data and the audio data, and providing the combined data to a classification network configured to calculate a similarity score between the neural data and the acoustic source using one or more similarity metrics; and using the similarity scores calculated for each of the acoustic sources to identify, from the plurality of acoustic sources, an acoustic source associated with the listener's auditory attention.

Abstract: Disclosed are devices, systems, apparatus, methods, products, and other implementations, including a method comprising obtaining, by a device, a combined sound signal for signals combined from multiple sound sources in an area in which a person is located, and applying, by the device, speech-separation processing (e.g., deep attractor network (DAN) processing, online DAN processing, LSTM-TasNet processing, Conv-TasNet processing), to the combined sound signal from the multiple sound sources to derive a plurality of separated signals that each contains signals corresponding to different groups of the multiple sound sources. The method further includes obtaining, by the device, neural signals for the person, the neural signals being indicative of one or more of the multiple sound sources the person is attentive to, and selecting one of the plurality of separated signals based on the obtained neural signals. The selected signal may then be processed (amplified, attenuated).

Abstract: An audio signal (172) representative of an acoustic signal is provided to an auditory model (105). The auditory model (105) produces a high-dimensional feature set based on physiological responses, as simulated by the auditory model (105), to the acoustic signal. A multidimensional analyzer (106) orthogonalizes and truncates the feature set based on contributions by components of the orthogonal set to a cortical representation of the acoustic signal. The truncated feature set is then provided to classifier (108), where a predetermined sound is discriminated from the acoustic signal.

Abstract: An audio signal (172) representative of an acoustic signal is provided to an auditory model (105). The auditory model (105) produces a high-dimensional feature set based on physiological responses, as simulated by the auditory model (105), to the acoustic signal. A multidimensional analyzer (106) orthogonalizes and truncates the feature set based on contributions by components of the orthogonal set to a cortical representation of the acoustic signal. The truncated feature set is then provided to classifier (108), where a predetermined sound is discriminated from the acoustic signal.