Abstract: A computing system for a plurality of classes of audio events is provided, including one or more processors configured to divide a run-time audio signal into a plurality of segments and process each segment of the run-time audio signal in a time domain to generate a normalized time domain representation of each segment. The processor is further configured to feed the normalized time domain representation of each segment to an input layer of a trained neural network. The processor is further configured to generate, by the neural network, a plurality of predicted classification scores and associated probabilities for each class of audio event contained in each segment of the run-time input audio signal. In post-processing, the processor is further configured to generate smoothed predicted classification scores, associated smoothed probabilities, and class window confidence values for each class for each of a plurality of candidate window sizes.

Abstract: Examples are disclosed relating to computing devices and methods for performing touch detection within a virtual trackpad area of a touch screen display. In one example, a non-trackpad touch input signal is received from outside the virtual trackpad area and processed with at least a jitter restrictor algorithm that applies a non-trackpad distance between reported touch locations. A virtual trackpad touch input signal is received from within the virtual trackpad area. On condition of determining that the virtual trackpad touch input signal is received from within the virtual trackpad area, the virtual trackpad touch input signal is processed with the jitter restrictor algorithm that applies a virtual trackpad distance between reported touch locations that is smaller than the non-trackpad distance between reported touch locations.

Abstract: Examples are disclosed relating to computing devices and methods for performing touch detection within a virtual trackpad area of a touch screen display. In one example, a non-trackpad touch input signal is received from outside the virtual trackpad area and processed with at least one touch detection algorithm applying a non-trackpad threshold value. A virtual trackpad touch input signal is determined to be received from within the virtual trackpad area. On condition of determining that the virtual trackpad touch input signal is received from within the virtual trackpad area, the virtual trackpad touch input signal is processed with the touch detection algorithm applying a virtual trackpad threshold value different from the non-trackpad threshold value.

Abstract: One example provides a computing device comprising a touch sensor, a camera, a logic subsystem, and a storage subsystem. The storage subsystem comprises instructions executable by the logic subsystem to receive image data from the camera; determine, based on the image data, information regarding one or more of a hand of a user or a stylus held by the user; and based at least on the information regarding one or more of the hand of the user or the stylus held by the user, control operation of the touch sensor.

Abstract: An electronic device comprises a touch sensor, a movement sensor, and recalibration logic. The touch sensor provides input to the electronic device. The movement sensor has an output that varies in dependence on movement of the electronic device, and the recalibration logic is configured to vary a rate of successful recalibration of the touch sensor based at least partly on the output of the movement sensor. The successful recalibration comprises replacement of a stored calibration mapping of each of a plurality of (X, Y) coordinates of the touch sensor to a corresponding touch-free capacitance.

Abstract: An earbud is configured to detect its location (e.g., in-ear and out-of-ear) based on an acoustical signature with and without user-specific customization. The earbud location may be indicated to a host, e.g., to determine playback. Location determinations are based on features extracted from acoustical samples taken by the earbud compared to features extracted from out-of-ear acoustical samples and non-user-specific and/or user-specific in-ear samples. A non-user-specific machine learning (ML) model trained on features extracted from non-user-specific in-ear and out-of-ear samples may be an initial/default locator. The non-user-specific model may be customized for specific users. A user-specific in-model may be created by training the non-user-specific model on features extracted from user-specific in-ear samples collected when the earbud is located in-ear for a specific user. The user-specific ML model may be selected to classify a location of the earbud for one or more associated hosts.

Abstract: The method disclosed herein includes determining a user interaction pattern with respect to a digital marker, the user interaction pattern based at least in part on user interaction with a capacitive sensor of a computing device, determining a geometric characteristic of the digital marker, and generating a predicted digital marking position of the digital marker on a digital medium of the computing device based at least in part on the determined geometric characteristic of the digital marker and the determined user interaction pattern.

Abstract: A computing system for a plurality of classes of audio events is provided, including one or more processors configured to divide a run-time audio signal into a plurality of segments and process each segment of the run-time audio signal in a time domain to generate a normalized time domain representation of each segment. The processor is further configured to feed the normalized time domain representation of each segment to an input layer of a trained neural network. The processor is further configured to generate, by the neural network, a plurality of predicted classification scores and associated probabilities for each class of audio event contained in each segment of the run-time input audio signal. In post-processing, the processor is further configured to generate smoothed predicted classification scores, associated smoothed probabilities, and class window confidence values for each class for each of a plurality of candidate window sizes.

Abstract: Examples are disclosed relating to computing devices and methods for performing capacitive touch detection in rotatably coupled displays. In one example, a method comprises: in a first display, providing a first signal at a first frequency to a first drive electrode in a first frequency region abutting a first non-coupled side opposite to a first coupled side. Other signals at other frequencies are provided to other drive electrodes in other frequency regions. In a second display, a second signal at the first frequency is provided to a second drive electrode located in a different first frequency region abutting a second coupled side opposite to a second non-coupled side. The other frequency regions of the first display are positioned between the first frequency region and the different first frequency region, thereby spatially separating the first frequency region from the different first frequency region.

Abstract: An electronic device detects a user presence in a first distance range from a user detection sensor of an electronic device. The electronic device communicates with multiple input components, setting the electronic device to a first device state based on detecting a user presence in the first distance range. The first device state applies a scanning rate to a first input component. The electronic device also detects a user presence in a second distance range from the user detection sensor of the electronic device while the electronic device is in the first device state. The electronic device sets the electronic device to a second device state, based on detecting a user presence in the second distance range while the electronic device is in the first device state. The second device state applies a different scanning rate to the first input component than in the first device state.

Abstract: Examples are disclosed relating to computing devices and methods for performing touch detection within a virtual trackpad area of a touch screen display. In one example, a non-trackpad touch input signal is received from outside the virtual trackpad area and processed with at least one touch detection algorithm applying a non-trackpad threshold value. A virtual trackpad touch input signal is determined to be received from within the virtual trackpad area. On condition of determining that the virtual trackpad touch input signal is received from within the virtual trackpad area, the virtual trackpad touch input signal is processed with the touch detection algorithm applying a virtual trackpad threshold value different from the non-trackpad threshold value.

Abstract: Computing devices and methods for determining opening and closing of touch sensitive interfaces are disclosed. In one example, a computing device comprises a touch screen display on a first substrate that is rotatably coupled to a second substrate that includes a trackpad. A trackpad identification signal transmitted by the trackpad is received at the touch screen display, and a touch screen identification signal transmitted by the touch screen is received at the trackpad. If the trackpad identification signal matches a trackpad identification key and the touch screen identification signal matches a touch screen identification key, then an energy level of one or both signals is compared to an energy level threshold. Based at least in part on the comparison of the energy level to the threshold, a power state transition is initiated.

Abstract: A method for touch detection includes, at a touch-sensitive device having a plurality of touch-sensing electrodes, driving a first subset of the plurality of touch-sensing electrodes with a drive signal having a first phase. A second subset of the plurality of touch-sensing electrodes are driven with a drive signal having a second phase, different from the first phase. Output signals for each of the plurality of touch-sensing electrodes are measured. For each touch-sensing electrode of the plurality of touch-sensing electrodes, a complex gradient is determined between the measured output signal for the touch-sensing electrode and the measured output signal for a neighboring touch-sensing electrode, the neighboring touch-sensing electrode driven with a different phase of a drive signal from the touch-sensing electrode. A location of a touch input is identified based at least in part on magnitudes of the determined complex gradients.

Abstract: A computing device is provided comprising a processor, a first display device having a first capacitive touch sensor, a second display device having a second capacitive touch sensor, and a hinge positioned between and coupled to each of the first display device and the second display device, the first display device and second display device being rotatable about the hinge and separated by a hinge angle. The processor is configured to detect the hinge angle at a first point in time, determine that the hinge angle at the first point in time is outside a first predetermined range, and upon at least determining that the hinge angle is outside the first predetermined range, perform run-time calibration of at least a plurality of rows of the capacitive touch sensor of the first display device and of the capacitive touch sensor of the second display device.

Abstract: Examples are disclosed relating to computing devices and methods for performing touch detection within a virtual trackpad area of a touch screen display. In one example, a non-trackpad touch input signal is received from outside the virtual trackpad area and processed with at least one touch detection algorithm applying a non-trackpad threshold value. A virtual trackpad touch input signal is determined to be received from within the virtual trackpad area. On condition of determining that the virtual trackpad touch input signal is received from within the virtual trackpad area, the virtual trackpad touch input signal is processed with the touch detection algorithm applying a virtual trackpad threshold value different from the non-trackpad threshold value.

Abstract: The method disclosed herein includes determining a user interaction pattern with respect to a digital marker, the user interaction pattern based at least in part on user interaction with a capacitive sensor of a computing device, determining a geometric characteristic of the digital marker, and generating a predicted digital marking position of the digital marker on a digital medium of the computing device based at least in part on the determined geometric characteristic of the digital marker and the determined user interaction pattern.

Abstract: A touch-sensitive display device includes one or more touch-sensitive displays each including a plurality of touch-sensitive electrodes. A free touch-sensitive electrode is identified on the one or more touch-sensitive displays that is (1) at least temporarily unaffected by proximity of one or more input objects to the one or more touch-sensitive displays, and (2) affected by an electrical noise caused by display of image content on the one or more touch-sensitive displays. The electrical noise affecting the free touch-sensitive electrode is measured. Based at least in part on the measured electrical noise affecting the free touch-sensitive electrode, and using a trained neural network, an amount of electrical noise caused by the display of image content that is affecting an occupied touch-sensitive electrode is estimated, the occupied touch-sensitive electrode being affected by proximity of the one or more input objects to the one or more touch-sensitive displays.

Abstract: A touch-sensitive display device includes one or more touch-sensitive displays each including a plurality of touch-sensitive electrodes. A free touch-sensitive electrode is identified on the one or more touch-sensitive displays that is (1) at least temporarily unaffected by proximity of one or more input objects to the one or more touch-sensitive displays, and (2) affected by an electrical noise caused by display of image content on the one or more touch-sensitive displays. The electrical noise affecting the free touch-sensitive electrode is measured. Based at least in part on the measured electrical noise affecting the free touch-sensitive electrode, and using a trained neural network, an amount of electrical noise caused by the display of image content that is affecting an occupied touch-sensitive electrode is estimated, the occupied touch-sensitive electrode being affected by proximity of the one or more input objects to the one or more touch-sensitive displays.

Abstract: Computing devices and methods for determining opening and closing of touch sensitive interfaces are disclosed. In one example, a computing device comprises a touch screen display on a first substrate that is rotatably coupled to a second substrate that includes a trackpad. A trackpad identification signal transmitted by the trackpad is received at the touch screen display, and a touch screen identification signal transmitted by the touch screen is received at the trackpad. If the trackpad identification signal matches a trackpad identification key and the touch screen identification signal matches a touch screen identification key, then an energy level of one or both signals is compared to an energy level threshold. Based at least in part on the comparison of the energy level to the threshold, a power state transition is initiated.

Abstract: A touch-sensitive display device comprises a touch sensitive-display including display electrodes configured to detect proximity of input objects to the touch-sensitive display. A touch controller is configured to determine a two-dimensional position of a stylus touch input based on information from the plurality of display electrodes. An indication of a tilt angle and an azimuthal angle of the stylus is received. A touch restriction region is defined based at least on the two-dimensional position of the stylus touch input and the tilt and azimuthal angles of the stylus. Touch inputs within the touch restriction region are processed differently than touch inputs outside the touch restriction region.