Abstract: A sensor transformation attention network (STAN) model including sensors configured to collect input signals, attention modules configured to calculate attention scores of feature vectors corresponding to the input signals, a merge module configured to calculate attention values of the attention scores, and generate a merged transformation vector based on the attention values and the feature vectors, and a task-specific module configured to classify the merged transformation vector is provided.

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 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 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: A sensor transformation attention network (STAN) model including sensors configured to collect input signals, attention modules configured to calculate attention scores of feature vectors corresponding to the input signals, a merge module configured to calculate attention values of the attention scores, and generate a merged transformation vector based on the attention values and the feature vectors, and a task-specific module configured to classify the merged transformation vector is provided.

Abstract: A sensor transformation attention network (STAN) model including sensors, attention modules, a merge module and a task-specific module is provided. The attention modules calculate attention scores of feature vectors corresponding to the input signals collected by the sensors. The merge module calculates attention values of the attention scores, and generates a merged transformation vector based on the attention values and the feature vectors. The task-specific module classifies the merged transformation vector.

Abstract: The invention relates to a method for monitoring touchscreen events (3) of a handheld device (6), wherein —a monitoring application (1) is provided, —at least one touchscreen event (3) on a display screen (61) of said handheld device (6) is detected by means of said monitoring application (1), wherein the touchscreen event (3) is directed to a foreground application (2), —event data (4) is generated for said at least one touchscreen event (3) by means of said monitoring application (1), wherein said event data (4) comprises a time stamp (41) of said at least one touchscreen event (3) and an identification (42) of said foreground application (2), —said event data (4) is stored by means of said monitoring application (1). The invention further relates to a computer program for executing the steps of the method according to the invention.

Abstract: The invention relates to a method for monitoring touchscreen events (3) of a handheld device (6), wherein —a monitoring application (1) is provided, —at least one touchscreen event (3) on a display screen (61) of said handheld device (6) is detected by means of said monitoring application (1), wherein the touchscreen event (3) is directed to a foreground application (2), —event data (4) is generated for said at least one touchscreen event (3) by means of said monitoring application (1), wherein said event data (4) comprises a time stamp (41) of said at least one touchscreen event (3) and an identification (42) of said foreground application (2), —said event data (4) is stored by means of said monitoring application (1). The invention further relates to a computer program for executing the steps of the method according to the invention.

Abstract: A sensor transformation attention network (STAN) model including sensors configured to collect input signals, attention modules configured to calculate attention scores of feature vectors corresponding to the input signals, a merge module configured to calculate attention values of the attention scores, and generate a merged transformation vector based on the attention values and the feature vectors, and a task-specific module configured to classify the merged transformation vector is provided.