Abstract: A device is disclosed, which includes a processor and a memory in communication with the processor. The memory includes executable instructions that, when executed by the processor, cause the processor to control the device to perform functions of providing a graphical user interface for a user on a display; displaying a user interface component on the graphical user interface; providing a pointer on the graphical user interface controlled by the user to interact with the user interface component on the graphical user interface; in response to the pointer being positioned over the displayed user interface component, and displaying after a dynamic hover trigger delay a hover graphic proximate to the component wherein the dynamic hover trigger delay dynamically changes depending on movement of the pointer by the user.

Abstract: A system may embed audio watermarks in audio data using an Eigenvector matrix. The system may detect audio watermarks in audio data despite the effects of reverberation. For example, the system may embed multiple repetitions of an audio watermark before generating output audio using loudspeaker(s). To detect the audio watermark in audio data generated by a microphone, the system may perform a self-correlation that indicates where the audio watermark is repeated. In some examples, the system may encode the audio watermark using multiple repetitions of a multi-segment Eigenvector. Additionally or alternatively, the system may encode the audio watermark using a binary sequence of positive and negative values, which may be used as a shared key for encoding/decoding the audio watermark. The audio watermark can be embedded in output audio data to enable wakeword suppression (e.g., avoid cross-talk between devices) and/or local signal transmission between devices in proximity to each other.

Abstract: A system configured to perform echo cancellation using a reduced number of reference signals. The system may perform multi-channel acoustic echo cancellation (MCAEC) processing on a first portion of a microphone audio signal that corresponds to early reflections and may perform single-channel acoustic echo cancellation (AEC) processing on a second portion of the microphone audio signal that corresponds to late reverberations. For example, the system may use MCAEC processing on a plurality of reference audio signals to generate a first echo estimate signal and may subtract the first echo estimate signal from the microphone audio signal to generate a residual audio signal. The system may delay the first echo estimate signal, perform the AEC processing to generate a second echo estimate signal, and subtract the second echo estimate signal from the residual audio signal to generate an output audio signal. This reduces an overall complexity associated with performing echo cancellation.

Abstract: A forward-backward Kalman filter for estimating phase noise present in a received signal. Both the forward and backward Kalman filters use hard-decision measurements of the received symbols. The phase noise estimate from the forward Kalman filter is used as a coarse phase noise estimate for the backward Kalman filter and vice versa. The final phase noise estimate is an optimal combination of the forward phase noise estimate and backward phase noise estimate.

Abstract: A system configured to perform cascade echo cancellation processing to improve a performance when reference signals are asymmetric (e.g., dominant reference signal(s) overshadow weak reference signal(s)). The system may perform cascade echo cancellation processing to separately adapt filter coefficients between the dominant reference signal(s) and the weak reference signal(s). For example, the system may use a dominant reference signal to process a microphone audio signal and generate a residual audio signal, using the residual audio signal to adapt first filter coefficient values corresponding to the dominant reference signal. Separately, the system may use a weak reference signal to process the residual audio signal and generate an output audio signal, using the output audio signal to adapt second filter coefficient values corresponding to the weak reference signal.

Abstract: Techniques for simulating a microphone array and generating synthetic audio data to analyze the microphone array geometry. This reduces the development cost of new microphone arrays by enabling an evaluation of performance metrics (False Rejection Rate (FRR), Word Error Rate (WER), etc.) without building device hardware or collecting data. To generate the synthetic audio data, the system performs acoustic modeling to determine a room impulse response associated with a prototype device (e.g., potential microphone array) in a room. The acoustic modeling is based on two parameters—a device response (information about acoustics and geometry of the prototype device) and a room response (information about acoustics and geometry of the room). The device response can be simulated based on the microphone array geometry, and the room response can be determined using a specialized microphone and a plane wave decomposition algorithm.

Abstract: A system may embed audio watermarks in audio data using a sign sequence. The system may detect audio watermarks in audio data despite the effects of reverberation. For example, the system may embed multiple repetitions of an audio watermark before generating output audio using loudspeaker(s). To detect the audio watermark in audio data generated by a microphone, the system may perform a self-correlation that indicates where the audio watermark is repeated. In some examples, the system may encode the audio watermark using multiple repetitions of a multi-segment Eigenvector. Additionally or alternatively, the system may encode the audio watermark using a binary sequence of positive and negative values, which may be used as a shared key for encoding/decoding the audio watermark. The audio watermark can be embedded in output audio data to enable wakeword suppression (e.g., avoid cross-talk between devices) and/or local signal transmission between devices in proximity to each other.

Abstract: A system may embed audio watermarks in audio data using an Eigenvector matrix. The system may detect audio watermarks in audio data despite the effects of reverberation. For example, the system may embed multiple repetitions of an audio watermark before generating output audio using loudspeaker(s). To detect the audio watermark in audio data generated by a microphone, the system may perform a self-correlation that indicates where the audio watermark is repeated. In some examples, the system may encode the audio watermark using multiple repetitions of a multi-segment Eigenvector. Additionally or alternatively, the system may encode the audio watermark using a binary sequence of positive and negative values, which may be used as a shared key for encoding/decoding the audio watermark. The audio watermark can be embedded in output audio data to enable wakeword suppression (e.g., avoid cross-talk between devices) and/or local signal transmission between devices in proximity to each other.

Abstract: A system configured to perform aligned beam merger (ABM) processing to combine multiple beamformed signals. The system may capture audio data and perform beamforming to generate beamformed audio signals corresponding to a plurality of directions. The system may apply an ABM algorithm to select a number of the beamformed audio signals, align the selected audio signals, and merge the selected audio signals to generate a distortionless output audio signal. The system may scale the selected audio signals based on relative magnitude and apply a complex correction factor to compensate for a phase error for each of the selected audio signals.

Abstract: A system configured to perform cascade echo cancellation processing to improve a performance when reference signals are asymmetric (e.g., dominant reference signal(s) overshadow weak reference signal(s)). The system may perform cascade echo cancellation processing to separately adapt filter coefficients between the dominant reference signal(s) and the weak reference signal(s). For example, the system may use a dominant reference signal to process a microphone audio signal and generate a residual audio signal, using the residual audio signal to adapt first filter coefficient values corresponding to the dominant reference signal. Separately, the system may use a weak reference signal to process the residual audio signal and generate an output audio signal, using the output audio signal to adapt second filter coefficient values corresponding to the weak reference signal.

Abstract: Techniques for simulating a microphone array and generating synthetic audio data to analyze the microphone array geometry. This reduces the development cost of new microphone arrays by enabling an evaluation of performance metrics (False Rejection Rate (FRR), Word Error Rate (WER), etc.) without building device hardware or collecting data. To generate the synthetic audio data, the system performs acoustic modeling to determine a room impulse response associated with a prototype device (e.g., potential microphone array) in a room. The acoustic modeling is based on two parameters—a device response (information about acoustics and geometry of the prototype device) and a room response (information about acoustics and geometry of the room). The device response can be simulated based on the microphone array geometry, and the room response can be determined using a specialized microphone and a plane wave decomposition algorithm.

Abstract: Techniques for improving acoustic echo cancellation to attenuate an echo signal generated by a loudspeaker included in a device are described. A system may determine a loudspeaker canceling beam (LCB) (e.g., fixed beam directed to the loudspeaker) and may use the LCB to generate LCB audio data that corresponds to the echo signal. For example, based on a configuration of the loudspeaker relative to microphone(s) of the device, the system may perform simulation(s) to generate a plurality of filter coefficient values corresponding to the loudspeaker. By subtracting the LCB audio data during acoustic echo cancellation, the system may attenuate the echo signal even when there is distortion or nonlinearity or the like caused by the loudspeaker. In some examples, the system may perform acoustic echo cancellation using the LCB audio data and playback audio data.

Abstract: A forward-backward Kalman filter for estimating phase noise present in a received signal. Both the forward and backward Kalman filters use hard-decision measurements of the received symbols. The phase noise estimate from the forward Kalman filter is used as a coarse phase noise estimate for the backward Kalman filter and vice versa. The final phase noise estimate is an optimal combination of the forward phase noise estimate and backward phase noise estimate.

Abstract: A system may embed audio watermarks in audio data using an Eigenvector matrix. The system may detect audio watermarks in audio data despite the effects of reverberation. For example, the system may embed multiple repetitions of an audio watermark before generating output audio using loudspeaker(s). To detect the audio watermark in audio data generated by a microphone, the system may perform a self-correlation that indicates where the audio watermark is repeated. In some examples, the system may encode the audio watermark using multiple repetitions of a multi-segment Eigenvector. Additionally or alternatively, the system may encode the audio watermark using a binary sequence of positive and negative values, which may be used as a shared key for encoding/decoding the audio watermark. The audio watermark can be embedded in output audio data to enable wakeword suppression (e.g., avoid cross-talk between devices) and/or local signal transmission between devices in proximity to each other.

Abstract: A system may embed audio watermarks in audio data using a sign sequence. The system may detect audio watermarks in audio data despite the effects of reverberation. For example, the system may embed multiple repetitions of an audio watermark before generating output audio using loudspeaker(s). To detect the audio watermark in audio data generated by a microphone, the system may perform a self-correlation that indicates where the audio watermark is repeated. In some examples, the system may encode the audio watermark using multiple repetitions of a multi-segment Eigenvector. Additionally or alternatively, the system may encode the audio watermark using a binary sequence of positive and negative values, which may be used as a shared key for encoding/decoding the audio watermark. The audio watermark can be embedded in output audio data to enable wakeword suppression (e.g., avoid cross-talk between devices) and/or local signal transmission between devices in proximity to each other.

Abstract: A system that performs wall detection, range estimation, corner detection and/or angular estimation. The system may determine an aggregate impulse response (e.g., impulse response of all components in a room) and may perform a deconvolution to remove a system impulse response (e.g., impulse response associated with loudspeaker(s) and microphone(s)). Thus, the system may use a sparse deconvolution algorithm to estimate a room impulse response (e.g., determine acoustic characteristics of the room). The system may detect a peak in the room impulse response and determine a distance and/or direction to an acoustically reflective surface based on the peak.

Abstract: Techniques for simulating a microphone array and generating synthetic audio data to analyze the microphone array geometry. This reduces the development cost of new microphone arrays by enabling an evaluation of performance metrics (False Rejection Rate (FRR), Word Error Rate (WER), etc.) without building device hardware or collecting data. To generate the synthetic audio data, the system performs acoustic modeling to determine a room impulse response associated with a prototype device (e.g., potential microphone array) in a room. The acoustic modeling is based on two parameters—a device response (information about acoustics and geometry of the prototype device) and a room response (information about acoustics and geometry of the room). The device response can be simulated based on the microphone array geometry, and the room response can be determined using a specialized microphone and a plane wave decomposition algorithm.

Abstract: A forward-backward Kalman filter for estimating phase noise present in a received signal. Both the forward and backward Kalman filters use hard-decision measurements of the received symbols. The phase noise estimate from the forward Kalman filter is used as a coarse phase noise estimate for the backward Kalman filter and vice versa. The final phase noise estimate is an optimal combination of the forward phase noise estimate and backward phase noise estimate.

Abstract: A beamformer system that can isolate a desired portion of an audio signal resulting from a microphone array. A fixed beamformer is used to dampen diffuse noise while an adaptive beamformer is used to cancel directional coherent noise. A gain is calculated using a signal quality value such as signal-to-noise ratio, signal-to-null ratio or other value. The adaptive beamformer output is adjusted by the gain prior to combining the fixed beamformer output and the adaptive beamformer output to determine the output audio data.

Abstract: A speech-capturing device that can modulate its output audio data volume based on environmental sound conditions at the location of a user speaking to the device. The device detects the sound pressure of a spoken utterance at the device location and determines the distance of the user from the device. The device also detects the sound pressure of noise at the device and uses information about the location of the noise source and user to determine the sound pressure of noise at the location of the talker. The device can then adjust the gain for output audio (such as a spoken response to the utterance) to ensure that the output audio is at a certain desired sound pressure when it reaches the location of the user.