Abstract: Techniques for monitoring devices to use ultrasonic signals to detect and track the locations of moving objects in an environment. To determine distance information, the monitoring devices emit a frequency-modulated continuous wave (FMCW) signal at an ultrasound frequency range. Reflections of the FMCW ultrasonic signal are used to generate time-of-arrival (TOA) profiles that indicate distances between the monitoring device and objects in the environment. The reflections can be processed to suppress undesirable interferences, such as reflections off non-mobile objects in the environment (e.g., walls, furniture, etc.), vibrations off the floorings or the ceilings, etc. After processing the reflections, a heatmap can be used to plot the intensity of the reflections for the different TOAs of the reflections, and depict the movement of the user over time. Finally, a Kalman filter is used to smooth the peaks in the intensity values on the plot, and determine the trajectory of the human.

Abstract: A system configured to detect a tap event on a surface of a device only using microphone audio data. For example, instead of using a physical sensor to detect the tap event, the device may detect a tap event in proximity to a microphone based on a power level difference between two or more microphones. When a power ratio exceeds a threshold, the device may detect a tap event and perform an action. For example, the device may output an alarm and use a detected tap event as an input to delay or end the alarm. In some examples, the device may detect a tap event using a plurality of microphones. Additionally or alternatively, the device may distinguish between multiple tap events based on a location of the tap event, enabling the device to perform two separate actions depending on the location.

Abstract: A system configured to perform deep adaptive acoustic echo cancellation (AEC) to improve audio processing. Due to mechanical noise and continuous echo path changes caused by movement of a device, echo signals are nonlinear and time-varying and not fully canceled by linear AEC processing alone. To improve echo cancellation, deep adaptive AEC processing integrates a deep neural network (DNN) and linear adaptive filtering to perform echo and/or noise removal. The DNN is configured to generate a nonlinear reference signal and step-size data, which the linear adaptive filtering uses to generate output audio data representing local speech. The DNN may generate the nonlinear reference signal by generating mask data that is applied to a microphone signal, such that the reference signal corresponds to a portion of the microphone signal that does not include near-end speech.

Abstract: A computing device may receive audio data from a microphone representing audio in an environment of the device, which may correspond to an utterance and noise. A model may be trained to process the audio data to reduce noise from the audio data. The model may include an encoder that includes one or more dense layers, one or more recurrent layers, and a decoder that includes one or more dense layers.

Abstract: A personal listening system and a method of using the personal listening system to enhance speech intelligibility of audio playback, are described. The method includes determining a speech intelligibility metric, such as a speech reception threshold, of a user. Based on the speech intelligibility metric, a tuning parameter is applied to an audio input signal. The speech reception threshold is compared to an environmental signal-to-noise ratio to determine whether enhancement of the audio input signal is warranted. Application of the tuning parameter to the audio input signal generates an audio output signal having reduced noise, making playback of the audio output signal more intelligible to the user. Other aspects are also described and claimed.

Abstract: Techniques for presence-detection devices to emission levels of ultrasonic signals that are used to detect movement in an environment. The presence-detection devices may detect movement of a person by emitting the ultrasonic signals into an environment, and characterizing the change in the frequency, or the Doppler shift, of the reflections of the ultrasonic signals off the person caused by the movement of the person relative to the presence-detection devices. However, presence-detection devices that continuously emit ultrasonic signals may experience reduced battery life, increased likelihood of overheating, etc. To reduce these negative effects, the presence-detection devices may reduce the emission levels of ultrasonic signals. For instance, once motion is detected, the presence-detection devices may, for a period of time, stop emitting ultrasonic signals or reduce the power level at which the ultrasonic signals are emitted.

Abstract: Techniques for presence-detection devices to detect movement of a person in an environment by emitting ultrasonic signals using a loudspeaker that is concurrently outputting audible sound. To detect movement by the person, the devices characterize the change in the frequency, or the Doppler shift, of the reflections of the ultrasonic signals off the person caused by the movement of the person. However, when a loudspeaker plays audible sound while emitting the ultrasonic signal, audio signals generated by microphones of the devices include distortions caused by the loudspeaker. These distortions can be interpreted by the presence-detection devices as indicating movement of a person when there is no movement, or as indicating lack of movement when a user is moving. The techniques include processing audio signals to remove distortions to more accurately identify changes in the frequency of the reflections of the ultrasonic signals caused by the movement of the person.

Abstract: Techniques for calibrating presence-detection devices to account for various factors that can affect the presence-detection devices' ability to detect movement. Presence-detection devices may detect movement of a person in an environment by emitting ultrasonic signals into the environment, and characterizing the change in the frequency, or the Doppler shift, of the reflections of the ultrasonic signals off the person caused by the movement of the person. However, factors such as environmental acoustic conditions, noise sources, etc., may affect the ability of the presence-detection devices to detect movement. To calibrate for these factors, the presence-detection devices may use a loudspeaker to emit an ultrasonic sweep signal that spans different frequencies in an ultrasonic frequency range.

Abstract: This disclosure describes presence-detection devices that detect movement of a person by emitting ultrasonic signals into an environment, and characterizing the change in the frequency, or the Doppler shift, of the reflections of the ultrasonic signals off the person caused by the movement of the person. The techniques include downsampling the audio signals from the carrier frequency range down to a frequency range with a center frequency around 0 Hz. A filter is applied to attenuate signals around 0 Hz and below (or above), such as the emitted signals. In addition to removing the emitted signals, the negative side (or positive side) of the audio signals are removed, but the Doppler shift is still represented in the remaining portion of the audio signals. By removing a portion of the audio signals, the amount of processing required to detect the Doppler shift in the reflections of the ultrasonic signals is reduced.

Abstract: This disclosure describes, in part, techniques to process audio signals to lessen the impact that wind and/or other environmental noise has upon the resulting quality of these audio signals. For example, the techniques may determine a level of wind and/or other noise in an environment and may determine how best to process the signals to lessen the impact of the noise, such that one or more users that hear audio based on output of the signals hear higher-quality audio.

Abstract: A system configured to detect a tap event on a surface of a device only using microphone audio data. For example, instead of using a physical sensor to detect the tap event, the device may detect a tap event in proximity to a microphone based on a power level difference between two or more microphones. When a power ratio exceeds a threshold, the device may detect a tap event and perform an action. For example, the device may output an alarm and use a detected tap event as an input to delay or end the alarm. In some examples, the device may detect a tap event using a plurality of microphones. Additionally or alternatively, the device may distinguish between multiple tap events based on a location of the tap event, enabling the device to perform two separate actions depending on the location.

Abstract: A system configured to detect a tap event on a surface of a device only using microphone audio data. For example, instead of using a physical sensor to detect the tap event, the device may detect a tap event in proximity to a microphone based on a power level difference between two or more microphones. When a power ratio exceeds a threshold, the device may detect a tap event and perform an action. For example, the device may output an alarm and use a detected tap event as an input to delay or end the alarm. In some examples, the device may detect a tap event using a plurality of microphones. Additionally or alternatively, the device may distinguish between multiple tap events based on a location of the tap event, enabling the device to perform two separate actions depending on the location.

Abstract: An audio system has a housing in which are integrated a number of microphones. A programmed processor accesses the microphone signals and produces a number of acoustic pick up beams based groups of microphones, an estimation of voice activity and an estimation of noise characteristics on each beam. Two or more beams including a voice beam that is used to pick up a desired voice and a noise beam that is used to provide information to estimate ambient noise are adaptively selected from among the plurality of beams, based on thresholds for voice separation and thresholds for noise-matching. Other embodiments are also described and claimed.

Abstract: Described herein are methods and devices for mixing input signals from multiple audio input sources in a hearing device such as a hearing aid. In one embodiment, a gain is applied to a telecoil signal where the gain is computed so as to depend on the level of a microphone signal. The technique does not depend on the user's audiogram and allows control of the gain for all environments with one adjustment.

Abstract: Described herein are methods and devices for mixing input signals from multiple audio input sources in a hearing device such as a hearing aid. In one embodiment, a gain is applied to a telecoil signal where the gain is computed so as to depend on the level of a microphone signal. The technique does not depend on the user's audiogram and allows control of the gain for all environments with one adjustment.

Abstract: An audio system has a housing in which are integrated a number of microphones. A programmed processor accesses the microphone signals and produces a number of acoustic pick up beams based groups of microphones, an estimation of voice activity and an estimation of noise characteristics on each beam. Two or more beams including a voice beam that is used to pick up a desired voice and a noise beam that is used to provide information to estimate ambient noise are adaptively selected from among the plurality of beams, based on thresholds for voice separation and thresholds for noise-matching. Other embodiments are also described and claimed.

Abstract: An audio system processes a speech signal to maintain a target value of the speech intelligibility index (SII) while minimizing the overall speech level so that speech intelligibility is preserved across different environmental sound levels while possible distortions and overall loudness are mitigated. In one embodiment, a hearing aid processes a speech signal received from another device to maintain a target value of the SII while minimizing the overall speech level before mixing the speech signal with a microphone signal.

Abstract: An orientation detector can have a first microphone, a second microphone, and a reference microphone spaced from the first microphone and the second microphone. An orientation processor can be configured to determine an orientation of the first microphone, the second microphone, or both, relative to a user's mouth based on a comparison of a relative strength of a first signal associated with the first microphone to a relative strength of a second signal associated with the second microphone. A channel selector in a speech enhancer can select one signal from among several signals based at least in part on the orientation determined by the orientation processor. A mobile communication handset can include a microphone-based orientation detector of the type disclosed herein.

Abstract: An orientation detector can have a first microphone, a second microphone, and a reference microphone spaced from the first microphone and the second microphone. An orientation processor can be configured to determine an orientation of the first microphone, the second microphone, or both, relative to a user's mouth based on a comparison of a relative strength of a first signal associated with the first microphone to a relative strength of a second signal associated with the second microphone. A channel selector in a speech enhancer can select one signal from among several signals based at least in part on the orientation determined by the orientation processor. A mobile communication handset can include a microphone-based orientation detector of the type disclosed herein.

Abstract: Disclosed herein, among other things, are methods and apparatuses for integration of hearing aids with an MSD to improve intelligibility in noisy environments. One aspect of the present subject matter relates to a method of providing voice audio to a hearing aid or text in a hearing aid user's field of view, where the voice audio or text corresponds to a speaker of interest within a noisy environment, and where the speaker of interest is identified using smart glasses. An MSD graphical user interface is provided for a hearing aid user to identify a speaker of interest. The lip movement patterns of the speaker of interest are recorded by the smart glasses, and voice activity detection is performed on the lip movement patterns. Noise reduction is performed using the results of the voice activity detection. The noise-reduced voice audio may be transmitted to the hearing aid, or the speech recognition text may be transmitted to and displayed on the MSD.