Abstract: Methods and apparatus to audio adjustment based on vocal effort are disclosed herein. An example apparatus comprising interface circuitry, machine readable instructions, and programmable circuitry to at least one of instantiate or execute the machine readable instructions to identify speech with a soft voice type in audio from a first user device, the speech with the soft voice type including phonation, modify the audio to generate modified audio based on the identification of the speech with the soft voice type, and output the modified audio from a second user device.

Abstract: A system, article, and method of detection of acoustic source aiming direction uses multiple microphones.

Abstract: Various systems and methods for implementing a vehicle with an external speaker and microphone are described herein. A system includes an audio processor to receive audio data, the audio data sensed by a microphone array installed on the vehicle, the audio data generated by a source outside of the vehicle; an audio classification circuit to analyze the audio data using a machine learning technique to determine a sound event; and a vehicle interface to transmit a message to a vehicle control system, the message based on the sound event.

Abstract: In some embodiments, a method calculates a first color metric value from a detection window in a first image that is detected by a visual system. A second image of pixels is generated where the pixels include one or more second color metric values that meet the first color metric value within a threshold. The method compares the second image to an object. When the comparison meets a criterion, the method transitions from a first mode to a second mode. When the comparison does not meet the criterion, the method continues to operate in the first mode.

Abstract: Methods and apparatuses for identifying food chewed or beverage drank are disclosed herein. In embodiments, a device may comprise one or more sensors to provide vibration signal data representative of vibrations sensed from a nasal bridge of a user wearing the device, a chewing analyzer to extract from the vibration signal data a first plurality of features associated with chewing activities, and/or a drinking analyzer is to extract from the vibration signal data a second plurality of features associated with drinking activities. The extracted first and/or second plurality of features, in turn, may be used to determine a category of food the user was chewing, or a category of beverage the user was drinking. In embodiments, the methods and apparatuses may use personalized models.

Abstract: In an embodiment, a system includes a wearable device having a sensor that detects whisper data from a user. The whisper data may include vibrational data, audio data, and/or biometric signals, and correspond to words whispered by the user at a first decibel level. The system also includes a processor communicatively coupled to the sensor that extracts features associated with the whisper data including frequencies and/or amplitudes associated with the whispered data, and generates speech data based on the whisper data and the features. The speech data corresponds to the words spoken at a second decibel level, where the second decibel level is greater than the first decibel level.

Abstract: Apparatuses and methods for selecting, for communicating, between signals provided by various pickups on the basis of a calculated signal to noise ratio (SNR) are disclosed. The various pickups may include a microphone and a vibration sensor. Signals from the microphone may be compared with signals from the vibration sensor by extracting a root-mean-square (RMS) profile for each, and comparing the RMS profiles to determine an SNR estimate for the microphone signal. The microphone signal may be selected if the SNR estimate is below a predetermined threshold, otherwise the vibration sensor signal may be selected. The vibration sensor signal may be subject to further processing if selected to approximate the microphone signal.

Abstract: Apparatuses and methods for transforming a signal from a vibration sensor into an approximation of a signal from a microphone are disclosed. The vibration sensor signal may be transformed by first obtaining a smooth average spectrum and corresponding deviation data for the vibration sensor signal. The smooth average spectrum may be passed through a mapping neural network to obtain a transformed smooth average spectrum that approximates a smooth average spectrum that would be obtained from a microphone. The transformed signal is then obtained by recombining the transformed smooth average spectrum with the deviation data.

Abstract: In some embodiments, a method calculates a first color metric value from a detection window in a first image that is detected by a visual system. A second image of pixels is generated where the pixels include one or more second color metric values that meet the first color metric value within a threshold. The method compares the second image to an object. When the comparison meets a criterion, the method transitions from a first mode to a second mode. When the comparison does not meet the criterion, the method continues to operate in the first mode.

Abstract: Various systems and methods for implementing a vehicle with an external speaker and microphone are described herein. A system includes an audio processor to receive audio data, the audio data sensed by a microphone array installed on the vehicle, the audio data generated by a source outside of the vehicle; an audio classification circuit to analyze the audio data using a machine learning technique to determine a sound event; and a vehicle interface to transmit a message to a vehicle control system, the message based on the sound event.

Abstract: A wearable gesture recognition device is disclosed that provides gesture recognition for gestures that may include a hold or steady-state component, and may account and adapt for real-time fit-level changes. The wearable gesture recognition device may integrate a photoplethysmographic (PPMG) and a piezoelectric (PZE) sensor such that respective sensor signals may be used individually, or in concert for gesture recognition. Thus the wearable gesture recognition device generally disclosed herein may advantageously perform gesture recognition through the fusion of PPMG and PZE signals. To support continuous gesture recognition, the wearable gesture recognition device may use a low-power activity detection scheme that analyzes a PZE signal prior to higher-power gesture classification. Moreover, the wearable gesture recognition device may provide power management by controlling a duty-cycle of the PPMG sensor without necessarily reducing recognition performance.

Abstract: Embodiments of the present disclosure provide techniques and configurations for an apparatus for detection of a change of electromagnetic field in response to a gesture, to identify the gesture that caused the field change. In one instance, the apparatus may include a first conducting component having first features for the disposal on or around a portion of a user's body, to generate an electromagnetic field in response to a receipt of a source signal. The apparatus may further include a second conducting component having second features for the disposal on or around a portion of the user's body, at a distance from the first conducting component, to provide an indication of a change in the electromagnetic field over time, to identify a change of a position of the user's body portion (gesture) that causes the change in the electromagnetic field. Other embodiments may be described and/or claimed.

Abstract: Embodiments of the present disclosure provide techniques and configurations for an apparatus for a user's physiological context measurements. In one instance, the apparatus may include a processing block and first and second piezoelectric sensors coupled with the processing block. The first and second sensors may include respectively first and second electrodes to provide contact with a user's body in response to mounting of the apparatus on the user's body. The processing block may comprise a multi-modal sensing system configured to perform measurements of a user's physiological context during the contact of the user's body with the first and second electrodes, based at least in part on a voltage signal generated by the user's body and provided to the processing block via the first and second electrodes. Other embodiments may be described and/or claimed.

Abstract: Embodiments of the present disclosure provide techniques and configurations for an apparatus to determine a command to the apparatus, based on vibration patterns. In one instance, the apparatus may include a body with at least one surface to receive one or more user inputs; at least one sensor disposed to be in contact with the body to detect vibration manifested by the surface in response to the user input, and generate a signal indicative of vibration detected; and a controller coupled with the sensor, to process the vibration-indicative signal, to identify a vibration pattern, and determine a command based at least in part on the vibration pattern, based at least in part on a result of the process of the signal. The command may be provided to interact, operate, or control the apparatus. Other embodiments may be described and/or claimed.

Abstract: Embodiments of the present disclosure provide techniques and configurations for an apparatus for a user's physiological context measurements. In one instance, the apparatus may include a head-fitting component to be mounted at least partly around a user's head, and a sensor disposed on the head-fitting component to generate a signal indicative of a user's physiological context in response to contact with the user's head. The physiological context may comprise a respiration cycle, and the sensor may sense vibration in a portion of the user's head produced in response to the respiration cycle. The apparatus may further include a controller coupled with the sensor, to process the signal and generate data indicative of the physiological context of the user. Other embodiments may be described and/or claimed.

Abstract: Apparatuses and methods for selecting, for communicating, between signals provided by various pickups on the basis of a calculated signal to noise ratio (SNR) are disclosed. The various pickups may include a microphone and a vibration sensor. Signals from the microphone may be compared with signals from the vibration sensor by extracting a root-mean-square (RMS) profile for each, and comparing the RMS profiles to determine an SNR estimate for the microphone signal. The microphone signal may be selected if the SNR estimate is below a predetermined threshold, otherwise the vibration sensor signal may be selected. The vibration sensor signal may be subject to further processing if selected to approximate the microphone signal.

Abstract: Methods and apparatuses for identifying food chewed or beverage drank are disclosed herein. In embodiments, a device may comprise one or more sensors to provide vibration signal data representative of vibrations sensed from a nasal bridge of a user wearing the device, a chewing analyzer to extract from the vibration signal data a first plurality of features associated with chewing activities, and/or a drinking analyzer is to extract from the vibration signal data a second plurality of features associated with drinking activities. The extracted first and/or second plurality of features, in turn, may be used to determine a category of food the user was chewing, or a category of beverage the user was drinking. In embodiments, the methods and apparatuses may use personalized models.

Abstract: Apparatuses and methods for transforming a signal from a vibration sensor into an approximation of a signal from a microphone are disclosed. The vibration sensor signal may be transformed by first obtaining a smooth average spectrum and corresponding deviation data for the vibration sensor signal. The smooth average spectrum may be passed through a mapping neural network to obtain a transformed smooth average spectrum that approximates a smooth average spectrum that would be obtained from a microphone. The transformed signal is then obtained by recombining the transformed smooth average spectrum with the deviation data.

Abstract: A method is described including receiving real time signals from the sensor array, comparing the real time signals to corresponding training signals representing a gesture from each sensor in the sensor array to determine if a gesture has been recognized and transmitting a command corresponding to a recognized gesture to a remote computing device.

Abstract: Embodiments of the present disclosure provide techniques and configurations for an apparatus for audio signal emulation, based on a vibration signal generated in response to a user's voice. In some embodiments, the apparatus may include at least one sensor disposed on the apparatus to generate a sensor signal indicative of vibration induced by a user's voice in a portion of a user's head. The apparatus may further include a controller coupled with the sensor, to transform the sensor signal into an emulated audio signal, with distortions associated with the vibration in the user's head portion that are manifested in the generated sensor signal, to improve speech recognition based on the generated sensor signal at least partially mitigated. Other embodiments may be described and/or claimed.