Abstract: Methods, systems, and devices for adaptive sensors are described. A system may acquire physiological data from a user via multiple optical channels of a wearable device, where each optical channel includes a light-emitting component and a photodetector. The system may determine respective measurement quality metrics and respective power consumption metrics associated with each optical channel based on the physiological data. Additionally, the system may select one or more optical channels of the multiple optical channels of the wearable device based on a comparison of the respective measurement quality metrics and the respective power consumption metrics associated with each optical channel. The system may acquire additional physiological data using the one or more optical channels based on the selecting.

Abstract: Methods, systems, and devices for hardware noise filtering are described. A wearable device may emit light from a set of light emitting elements (e.g., light emitting diodes (LEDs)) based on a known input signal. The wearable device may measure an output signal at a set of photodetectors. The output signal may be generated by passing the light emitted from the set of light emitting elements into a material in contact with the wearable device. The wearable device may compare the known input signal to the output signal and may determine a hardware noise component of the output signal based on a known environmental noise component of the output signal and the comparison. The wearable device may store the hardware noise component for filtering hardware noise from sensor measurements.

Abstract: An apparatus comprising at least one processor and at least one memory including computer program code the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus at least to perform: filtering an audio signal into at least three frequency band signals; generating for each frequency band signal a plurality of sub-band signals; processing at least one sub-band signal from at least one frequency band; and combining the processed sub-band signals to form a combined processed audio signal.

Abstract: Both a cascade and a multichannel joint Bayesian estimator are provided for suppressing acoustic echo. An expansion basis (Power/Fourier series) is selected to convert a sample-based input signal xt into a DFT-domain multichannel signal [X?,1, . . . X?,p]. The posterior of unknown states (e.g., mean ?? and covariance P? of the echo path W? and the mean â? and covariance Q? of the nonlinear coefficients a?; or channel-wise mean ??,i and multichannel covariance P? of a compound quantity formed by merging together the echo path W? and the ith nonlinear coefficient a?,i) and model parameters ?? are estimated; and Kalman gain factor(s) K? are computed for optimal adaptation of the posterior of unknown states. An echo signal ?? is estimated using the multichannel input signal [X?,1, . . . X?,p] and the adapted posterior; and an error signal E? is generated.

Abstract: Both a cascade and a multichannel joint Bayesian estimator are provided for suppressing acoustic echo. An expansion basis (Power/Fourier series) is selected to convert a sample-based input signal xt into a DFT-domain multichannel signal [X?,1, . . . X?,p]. The posterior of unknown states (e.g., mean ?? and covariance P? of the echo path W? and the mean â? and covariance Q? of the nonlinear coefficients a?; or channel-wise mean ??,i and multichannel covariance P? of a compound quantity formed by merging together the echo path W? and the ith nonlinear coefficient a?,i) and model parameters ?? are estimated; and Kalman gain factor(s) K? are computed for optimal adaptation of the posterior of unknown states. An echo signal ?? is estimated using the multichannel input signal [X?,1, . . . X?,p] and the adapted posterior; and an error signal E? is generated.

Abstract: Disclosed herein is an apparatus. The apparatus includes a first audio input device, a second audio input device, an analog to digital converter, a voice activity detector, and a position detector. The first audio input device is configured to receive a first audio signal. The second audio input device is configured to receive a second audio signal. The analog to digital converter is connected to the first and the second audio input devices. The voice activity detector is connected to the analog to digital converter. The voice activity detector is configured to receive input from the first and the second audio input devices. The position detector is connected to the voice activity detector. The position detector is configured to determine a position of the apparatus and classify the audio signals based on, at least partially, a ratio of the first audio signal and the second audio signal.

Abstract: Disclosed herein is an apparatus. The apparatus includes a first audio input device, a second audio input device, an analog to digital converter, a voice activity detector, and a position detector. The first audio input device is configured to receive a first audio signal. The second audio input device is configured to receive a second audio signal. The analog to digital converter is connected to the first and the second audio input devices. The voice activity detector is connected to the analog to digital converter. The voice activity detector is configured to receive input from the first and the second audio input devices. The position detector is connected to the voice activity detector. The position detector is configured to determine a position of the apparatus and classify the audio signals based on, at least partially, a ratio of the first audio signal and the second audio signal.